Wealth Engine

Tag: AI Pricing

  • Cheapest 1M Context Model: GLM-5.3-Flash vs Gemini 3.7 Flash

    GLM-5.3-Flash is the cheapest 1M context model worth running in production. Z.ai lists it at $0.15 per million input tokens against $0.75 for Gemini 3.7 Flash — five times cheaper — while scoring 57 on the Artificial Analysis Intelligence Index versus Gemini’s 56. Google keeps two real advantages: raw throughput and vision. Everything else favors the open-weights challenger.

    Z.ai shipped GLM-5.3-Flash on August 26, 2026, thirteen days after Google made Gemini 3.7 Flash generally available. Both models advertise a 1,048,576-token context window. Both target agentic coding and long-document work.

    The gap is price. And at 1M-token scale, price is the entire product decision.

    What is GLM-5.3-Flash?

    GLM-5.3-Flash is a natively multimodal mixture-of-experts model with 320 billion total parameters and 18 billion active per token, released under an MIT license. It routes each token through 8 of 288 experts across 45 layers, ships in native FP8, and holds a 1,048,576-token context window.

    That active-parameter count is the whole story. Z.ai is charging flagship-tier context for a model that only lights up 18B weights per forward pass.

    The architecture behind the price

    The model combines KDA linear-attention layers with NoPE sparse MLA layers. Per MarkTechPost’s launch coverage, that combination delivers roughly 3x less attention compute and a 4.4x smaller KV cache than GLM-5.3.

    KV cache is what makes long context expensive to serve. Shrink it 4.4x and you can price a 1M window like a short one.

    The jump over the previous generation is not cosmetic. Z.ai’s own numbers put DeepSWE v1.1 at 63.4%, up from 46.2% on GLM-5.2, and AutomationBench at 48.8%, up from 26.2% — a 22.6-point gain in one release cycle, according to LLM Stats.

    How much does the cheapest 1M context model actually cost?

    GLM-5.3-Flash lists at $0.15 per million input tokens and $0.50 output, with cached input at $0.03. Gemini 3.7 Flash lists at $0.75 input and $3.75 output on Google’s own model page. That is 5x on input and 7.5x on output, before any discount either side is running.

    Spec GLM-5.3-Flash Gemini 3.7 Flash
    Released Aug 26, 2026 Aug 13, 2026
    License MIT open weights Proprietary API
    Parameters 320B total / 18B active Undisclosed
    Input context 1,048,576 tokens 1,048,576 tokens
    Max output 131,072 tokens 65,536 tokens
    Input / 1M $0.15 $0.75
    Output / 1M $0.50 $3.75
    Cached input / 1M $0.03 $0.06 (Vertex)
    AA Intelligence Index 57 56
    Output speed 50.2 tok/s 301 tok/s
    Time to first token 1.47s 3.83s

    Pricing from Z.ai list rates and Google DeepMind’s Gemini Flash page. Speed and index figures from Artificial Analysis and Requesty’s Vertex listing. Resellers differ: OpenRouter lists GLM-5.3-Flash at $0.075 / $0.25 and Gemini 3.7 Flash at $0.375 / $1.875.

    What it costs to fill the window once

    Push a full 1,048,576-token context through each model, one time, and the arithmetic is brutal.

    GLM-5.3-Flash: $0.157. Gemini 3.7 Flash: $0.786. Same window, same task, a $0.63 difference per call.

    Run that 10,000 times a month — a modest document-processing pipeline — and you are looking at $1,573 versus $7,864. The $6,291 monthly delta is a headcount line item, not a rounding error.

    For context on how wide the field has gotten, Morph’s context-window survey clocked a 71x spread between the cheapest and priciest 1M window on the market, from $0.14 on DeepSeek V4 Flash to $10.00 on Claude Fable 5.

    The January 2027 price cliff

    Google’s $0.75 / $3.75 is an introductory rate. Its own page states the promotion expires December 31, 2026, after which Gemini 3.7 Flash reverts to $1.50 per million input and $7.50 per million output.

    On January 1, filling that same 1M window costs $1.57 on Gemini. Against GLM’s $0.157, that is a clean 10x.

    Z.ai is running a promotion too — 50% off through September 9, 2026 — but its post-promo list price is the $0.15 already quoted. One vendor’s discount expires into a doubling. The other’s expires into the number on the page.

    Which is better for coding agents, GLM-5.3-Flash or Gemini 3.7 Flash?

    Gemini 3.7 Flash wins the coding benchmarks by margins too small to justify a 7.5x output bill. It leads Terminal-Bench 2.1 85.8% to 84.3% and DeepSWE v1.1 65.3% to 63.4%. GLM takes HLE 55.3% to 53.6% and destroys Gemini on AutomationBench, 48.8% to 30.4%.

    A 1.5-point Terminal-Bench edge is inside the noise band of most agent harnesses. An 18.4-point AutomationBench gap is not.

    AutomationBench measures multi-step tool use and workflow completion — the thing you actually buy an agent model for. GLM-5.3-Flash scores 60% higher there in relative terms.

    Coding agents also burn output tokens, not input tokens. A long agentic run is thousands of generated tokens per step. That is precisely the axis where Gemini costs 7.5x more.

    Our earlier breakdown of GLM-5.3 against DeepSeek V4 Pro found the same pattern in the open-weight tier: near-parity capability, order-of-magnitude price separation.

    Where Gemini 3.7 Flash still wins

    Google has genuine leads that no discount closes:

    • Throughput: 301 tokens/second median output versus 50.2 for GLM-5.3-Flash — 6x faster generation.
    • Vision: BabyVision 70.9% against GLM’s 53.4%, a 17.5-point gap that Z.ai does not dispute.
    • Long-context recall: GDM-MRCR v2 at 128k scores 97.0%, among the strongest retrieval numbers published this year.
    • Desktop agents: OSWorld-2.0 at 47.9% and Code Arena at 1588 Elo for web development.
    • Vertical accuracy: Harvey LAB-AA at 90.7% on legal reasoning tasks.

    GLM does answer faster on the first token — 1.47s versus 3.83s — which matters for interactive chat. But once generation starts, Gemini pulls away hard.

    Is GLM-5.3-Flash worth it for multimodal work?

    Only for charts and documents, not for general vision. GLM-5.3-Flash posts 78.0% on Chartography against DeepSeek-V4-Flash-Vision-Exp’s 64.3%, but trails Gemini 3.7 Flash badly on BabyVision, 53.4% to 70.9%. Structured visual data is a strength. Open-ended image understanding is not.

    It also scores 62.4% on OfficeQA Pro, which points at the same conclusion: business documents, spreadsheets, slides and charts are where the multimodal stack earns its keep.

    If your pipeline reads invoices, financial statements or dashboards, GLM handles it at a fifth of the price. If it captions arbitrary photos, pay Google.

    We ran similar math on DeepSeek’s vision model against Claude Opus 4.8, where the per-image gap ran 23x. Cheap vision is now a solved category — you just have to match the model to the image type.

    Which model should you buy for your workload?

    Pick on token mix, not on leaderboard position. Input-heavy jobs at 1M scale go to GLM-5.3-Flash on cost alone. Latency-critical streaming and general vision go to Gemini 3.7 Flash. Coding agents are close on quality and lopsided on price.

    Use case Buy Why
    Bulk document / RAG ingestion GLM-5.3-Flash $0.157 vs $0.786 per full 1M window
    Long-horizon coding agents GLM-5.3-Flash AutomationBench 48.8 vs 30.4; 7.5x cheaper output
    Real-time chat / streaming UX Gemini 3.7 Flash 301 tok/s vs 50.2 tok/s
    General image understanding Gemini 3.7 Flash BabyVision 70.9 vs 53.4
    Charts, invoices, office docs GLM-5.3-Flash Chartography 78.0; OfficeQA Pro 62.4
    Needle-in-haystack retrieval Gemini 3.7 Flash GDM-MRCR v2 at 97.0%
    Data that cannot leave your VPC GLM-5.3-Flash MIT weights, self-hostable
    Terminal-Bench maximalists Gemini 3.7 Flash 85.8 vs 84.3 — for a 5x premium

    Should you self-host GLM-5.3-Flash instead?

    Only above roughly 100 million tokens a month. The FP8 checkpoint is 306 GiB of weights before KV cache and needs NVIDIA Hopper or newer. That is a multi-GPU node running continuously against an API bill of $0.15 per million input tokens.

    The MIT license is the real asset here, not the savings. It permits commercial use, modification and redistribution with no revenue thresholds — which is what makes GLM viable for regulated buyers who cannot route customer data through a third-party API.

    Weights are published on Hugging Face as zai-org/GLM-5.3-Flash. Our Qwen3.8-Max self-hosting cost analysis laid out the crossover math in detail; the shape is unchanged, only the weight file got smaller.

    Frequently asked questions

    Is GLM-5.3-Flash actually the cheapest 1M context model?

    Not quite. DeepSeek V4 Flash fills a 1M window for about $0.14 against GLM’s $0.157. But GLM scores 57 on the Artificial Analysis Intelligence Index and adds native multimodality, which makes it the cheapest capable one.

    How much cheaper is GLM-5.3-Flash than Gemini 3.7 Flash?

    Five times cheaper on input ($0.15 vs $0.75 per million) and 7.5 times cheaper on output ($0.50 vs $3.75). After Google’s introductory pricing expires December 31, 2026, the input gap widens to 10x.

    Does GLM-5.3-Flash really have a 1M context window?

    Z.ai specifies 1,048,576 tokens. OpenRouter lists its routed endpoint at 1,310,720 tokens with 131,072 max output — double Gemini 3.7 Flash’s 65,536-token output ceiling.

    Which model is faster?

    Gemini 3.7 Flash generates 6x faster at 301 tokens per second versus 50.2. GLM-5.3-Flash responds faster initially, at 1.47 seconds to first token against Gemini’s 3.83 seconds.

    Are these benchmark scores independently verified?

    Partly. The Artificial Analysis Intelligence Index scores are third-party. The Terminal-Bench, DeepSWE and AutomationBench figures are vendor self-reported on both sides — LLM Stats flags this explicitly for GLM-5.3-Flash.

    Can I use GLM-5.3-Flash commercially?

    Yes. The weights ship under an MIT license, which permits commercial use, modification and redistribution without revenue caps or usage restrictions.

    What happens to Gemini 3.7 Flash pricing in 2027?

    Google’s model page states the introductory rate ends December 31, 2026, moving to $1.50 per million input tokens and $7.50 per million output from January 1, 2027.

    The bottom line

    Buy GLM-5.3-Flash. For any workload dominated by input tokens or agent output tokens, it is the correct default — 5x to 7.5x cheaper at an intelligence index one point above Gemini 3.7 Flash, with a bigger output ceiling and weights you can take in-house.

    Keep Gemini 3.7 Flash for exactly two jobs: user-facing streaming where 301 tokens per second is the product, and general image understanding where 17.5 BabyVision points decide whether the feature works at all.

    The broader signal matters more than either model. Google discounted a Flash-tier model and still got undercut 5x by open weights released thirteen days later. Google’s own price sheet says that gap widens to 10x in four months.

    If you are still routing 1M-token jobs through a proprietary Flash endpoint in 2027, you are paying a tenfold convenience tax. Compare that against our Gemini 3.7 Flash versus Claude Sonnet 5 cost-per-point analysis and the direction is unmistakable.

    Sources

  • Best Computer Use Agent 2026: Qwen-UI-Agent vs Opus 4.8

    Alibaba’s Qwen-UI-Agent posts the strongest mobile GUI scores anyone has published — 92.2% on MobileWorld-Real — and you cannot buy it. No weights, no API, no price. For a computer use agent you can deploy this week, Claude Opus 4.8 still owns desktop reliability at 83.4% on OSWorld-Verified, and Holo 3.1 wins on cost per task. Everything else is a benchmark press release.

    What is a computer use agent, and why is everyone shipping one now?

    A computer use agent is a model that operates a screen directly — clicking, typing, scrolling — instead of calling an API. It reads screenshots, decides the next action, and repeats. That makes it the only automation layer that works on software with no integration surface.

    The category went from research demo to procurement question fast. Temporal’s 2026 State of Development Report found daily agent usage among surveyed engineers jumped from 47.3% to 80.8% this year. Agents are now normal. Agents that drive a mouse are the next line item.

    The benchmark that actually decides purchases

    OSWorld-Verified is the number buyers use. It runs 369 real desktop tasks in a live Linux environment and scores binary success — the task worked, or it did not.

    On BenchLM’s August 2026 OSWorld-Verified board, Claude Opus 4.8 sits at 83.4%, Gemini 3.6 Flash at 83.0%, and GPT-5.5 at 78.7%. The top of that board is clustered inside about three points. That clustering is the whole story: on desktop, model choice barely moves the needle anymore. Price does.

    Is Qwen-UI-Agent actually better than Claude Opus 4.8?

    On phones, yes, and not by a little. On desktops, no. The Qwen-UI-Agent technical report from Alibaba’s Tongyi MAI team claims 92.2% on MobileWorld-Real, beating Claude Opus 4.8 by 7.5 points and GPT-5.6 Sol by 6.8. On OSWorld-Verified it lands second at 79.5%.

    Where Qwen-UI-Agent wins

    Mobile is a rout. The report puts Qwen-UI-Agent at 82.1% on MobileWorld — 14.6 points ahead of Opus 4.8, 12.0 ahead of GPT-5.6 Sol — and 97.5% on AndroidDaily.

    Grounding is the other win. It reports 81.5% on ScreenSpot-Pro under the zoom-in setting and 97.5% on ScreenSpot-V2. Grounding is the skill of hitting the right pixel, and it is where most agents quietly fail.

    It also takes WebArena at 73.6%, 1.7 points above Opus 4.8.

    Where it loses

    Desktop. Opus 4.8’s 83.4% on OSWorld-Verified beats Qwen-UI-Agent’s 79.5% by 3.9 points, and on the harder OSWorld-v2 the report concedes 13.9% binary success against a 40.0% partial-progress score. Long desktop chains still break.

    Deep research is worse. BrowseComp comes in at 64.1% — roughly 26 points behind GPT-5.6 on the same test, per eesel AI’s breakdown.

    Can you actually buy Qwen-UI-Agent right now?

    No. There is no API, no published price, and no checkpoint on Hugging Face. The Tongyi-MAI repository ships a technical report, demo videos, and cookbook notebooks. The Apache 2.0 license covers the repo contents, not a model you can run.

    What is downloadable is the previous generation: MAI-UI 8B (8.77B parameters) at 65.7% on ScreenSpot-Pro and MAI-UI 2B (2.13B) at 57.4%. The larger 235B-A22B variant tops AndroidWorld at 76.7% and is not published either.

    Treat the headline scores as a roadmap, not a product. That gap between announcement and availability is the single most expensive mistake in agent procurement this year.

    How much does a computer use agent cost in 2026?

    Between $0.03 and $30 per million input tokens, depending on tier. Screen-driving is token-hungry: every step ships a fresh screenshot, so a computer use agent burns input tokens at several times the rate of a chat workload. Input price, not output price, is what sets your bill.

    Agent / model Access Input $/M Output $/M Best published score
    Claude Opus 4.8 Hosted API (GA) $5.00 $25.00 83.4% OSWorld-Verified
    Claude Sonnet 5 Hosted API (GA) $2.00 $10.00 Mid-tier desktop
    GPT-5.6 Sol Hosted API (GA) $5.00 $30.00 92.8% Online-Mind2Web
    GPT-5.6 Luna Hosted API (GA) $0.20 $1.20 Budget browser tier
    Gemini 3.7 Flash Hosted API (preview) $0.75 $3.75 83.0% (3.6 Flash)
    Holo 3.1 Open weights + API $0.25 $1.80 82.56% OSWorld-Verified
    Qwen3.5 Flash Hosted API $0.10 $0.40 Grounding / cheap steps
    Qwen-UI-Agent Not released — — 92.2% MobileWorld-Real

    Claude prices come from Anthropic’s pricing documentation; OpenAI, Gemini and Qwen rates from BenchLM’s August 2026 tables.

    The token math nobody prices in

    Four multipliers decide whether your pilot survives contact with finance.

    • Tokenizer drift. Anthropic documents that Claude 4.7 and later use a newer tokenizer producing roughly 30% more tokens for the same text. Same price, bigger invoice.
    • Cache hits. Cached input bills at 0.1x on Claude and 10% of standard on OpenAI. For screenshot loops with a stable system prompt, that is the difference between viable and absurd.
    • Batch discounts. Both Anthropic and OpenAI cut 50% for asynchronous batch work. Most agent tasks are not urgent.
    • Fast mode. Anthropic’s fast tier for Opus 5 and 4.8 runs $10 input and $50 output — double the standard rate for latency you probably do not need on a background agent.

    One more piece of good news for budgets: Anthropic made Sonnet 5’s introductory $2/$10 pricing permanent, canceling the increase to $3/$15 that was scheduled for September 1. If you built a cost model around that hike, tear it up. We covered the token-efficiency side of this in Agent Skills vs MCP.

    Which computer use agent should you pick for your use case?

    Match the agent to the surface, not to the leaderboard. Desktop reliability, mobile coverage, browser scale and self-hosting are four different purchases, and no single vendor currently wins all four. Here is the split as of late August 2026.

    Use case Pick Why Cost signal
    Desktop work where errors are expensive Claude Opus 4.8 83.4% OSWorld-Verified; prompt-injection classifiers run over every screenshot $5/$25 per M tokens
    Phone plus desktop from one endpoint Gemini Computer Use Only hosted API with a documented Android environment $0.75/$3.75 (3.7 Flash)
    Browser-only automation at volume Browser Use 87.4% on Odysseys; model-agnostic, cloud supplies Chromium From $29/mo, $0.02/browser hour
    Data cannot leave your network Holo 3.1 82.56% OSWorld-Verified on a maintainer-run board; Apache 2.0 $0.25/$1.80 or self-host free
    Logged-in flows blocked by 2FA Skyvern Credentials never sent to the model; 85.85% WebVoyager From $29/mo; 2FA at $149/mo
    Commercial product, permissive license UI-TARS Apache 2.0, ~808k monthly downloads Free weights

    Are open-weight GUI agents good enough yet?

    One is. Holo 3.1 scores 82.56% on OSWorld-Verified on a maintainer-run board — within a point of Opus 4.8 — under Apache 2.0, with a hosted fallback at $0.25/$1.80 per million tokens and a free tier at 10 requests per minute. That is the only open model verified by a neutral board rather than its own vendor.

    The rest are not close. ByteDance’s UI-TARS is the most downloaded GUI agent at roughly 808,000 monthly pulls, but the downloadable checkpoint scores 27.5% on OSWorld — a 15-point gap from the published figure.

    The pattern mirrors what we found in GLM-5.3 vs DeepSeek V4 Pro: open weights close the gap on paper faster than they close it in production.

    Is a computer use agent worth it in 2026?

    Only when no API exists. The most useful finding in this cycle is that the best GUI agents avoid the GUI: 55.1% of actions on OSWorld-v2 were typed commands rather than clicks. Screen-driving is the fallback, not the strategy.

    The failure data backs that up. Roughly 52% of real-device failures trace to the screen itself and 40% to the agent, meaning half your errors come from interfaces that were never designed for a machine to read.

    The financial read is blunt. If a documented API exists, an API integration costs less, breaks less, and does not bill you for screenshots. Reserve computer use for legacy desktop software, vendor portals with no API, and mobile apps. That is a real and expensive category — it is just smaller than the demos suggest. The same discipline applies to coding agents, which we priced in Claude Code vs Codex CLI.

    Frequently asked questions

    What is the best computer use agent right now?

    Claude Opus 4.8 for desktop reliability at 83.4% OSWorld-Verified, Gemini Computer Use if you need Android, and Holo 3.1 if you need open weights. Qwen-UI-Agent has the best benchmarks and no product.

    When will Qwen-UI-Agent weights be released?

    Alibaba has not announced a date. As of late August 2026 the repository has no releases and no checkpoint on Hugging Face. Only the older MAI-UI 2B and 8B models are downloadable.

    How much does Claude computer use cost?

    Claude Opus 4.8 bills $5 per million input tokens and $25 per million output, per Anthropic’s pricing docs. Cache hits drop input to 0.1x and batch processing cuts both sides by 50%.

    Is Claude Sonnet 5 still going up in price on September 1?

    No. Anthropic canceled the increase and made the $2/$10 introductory pricing permanent. The previously scheduled $3/$15 rate will not take effect.

    Do computer use agents work on mobile?

    Partially. Gemini Computer Use is the only hosted API with a documented Android environment. Claude’s computer use has no mobile support. Qwen-UI-Agent leads mobile benchmarks but ships nothing.

    Are computer use agents safe to run on production systems?

    Only with guardrails. Anthropic runs prompt-injection classifiers over every screenshot automatically; Gemini’s injection detection is off by default, and OpenAI leaves safety implementation to you. Scope credentials tightly and log every action.

    What is OSWorld-Verified?

    A 369-task benchmark that scores computer use agents on real desktop work in a live environment, pass or fail. It is the closest thing the category has to a purchasing standard.

    The bottom line

    Buy Claude Opus 4.8 for desktop work where a failed task costs more than a token bill — the 83.4% OSWorld-Verified score and automatic injection screening justify $5/$25. Buy Holo 3.1 if data residency or cost dominates: 82.56% for $0.25/$1.80 is the best price-per-point in the category by a wide margin.

    Ignore Qwen-UI-Agent until weights land. A 92.2% score you cannot run is worth exactly zero. And before you buy any of them, check whether the target system has an API — because 55.1% of the winning agent’s own actions were typed commands, which is the benchmark quietly telling you to skip the screen entirely.

    Sources

  • Agent Skills vs MCP: Which One Cuts Your Token Bill

    Agent Skills vs MCP is not really a fight — but on cost, Skills win outright. Anthropic’s own numbers show 58 MCP tools burning roughly 55,000 tokens before an agent does anything useful. A Skill’s metadata costs about 100 tokens. Build Skills for procedure and judgment, MCP servers for live system access, and cache aggressively if you run both.

    On August 19, 2026, Anthropic moved Agent Skills, the Skills API, computer use, browser use and the Files API out of beta and into general availability on the Claude Developer Platform, according to the platform’s release notes. Three weeks earlier, the Model Context Protocol shipped its stateless 2026-07-28 specification.

    Both are now production infrastructure. Both are free to adopt. Only one of them charges you rent on every single request.

    What actually changed on August 19, 2026?

    Agent Skills stopped being an experiment. The skills-2025-10-02 beta header is gone, the /v1/skills endpoint is GA, and computer use shipped as computer_toolset_20260801 with browser use as a separate tool, browser_toolset_20260801. That is a full production agent stack in one release.

    The Files API went GA the same day. Upload, download, list, metadata and delete operations are free — you are billed only for file content that actually enters a Messages request, at standard input-token rates.

    Storage caps are generous: 500 MB per file and 1 TB per organization, rate-limited to roughly 500 requests per minute.

    None of the superseded beta headers have a published sunset date yet. Legacy identifiers still work. But GA is the signal that pricing and architecture decisions made today will stick.

    What is the difference between Agent Skills and MCP?

    MCP gives an agent reach. Agent Skills give an agent method. An MCP server tells Claude how to connect to GitHub and what it can do there. A Skill tells Claude how your team actually writes a release note, in what order, with which checks.

    That distinction sounds academic until you look at where each one lives in the context window.

    How MCP loads tools

    MCP tool definitions are pushed into the model’s context up front, on every request. The agent has to know a tool exists before it can call it, so the full schema — names, parameters, descriptions — sits in the prompt whether or not the tool ever gets used.

    That is a fixed tax. It scales linearly with how many servers you connect.

    How Agent Skills load

    Skills use three-tier progressive disclosure, documented in Anthropic’s Agent Skills overview. Level 1 is YAML frontmatter — name and description only — at roughly 100 tokens per Skill, always loaded. Level 2 is the SKILL.md body, under about 5,000 tokens, loaded only when the Skill is triggered.

    Level 3 is where it gets interesting. Bundled reference files and scripts cost zero tokens until read. Script code never enters the context window at all — only its output does.

    You can ship 300 pages of API documentation inside a Skill and pay nothing for it unless the agent opens the file.

    How much do MCP tool definitions actually cost?

    More than most teams realize. Anthropic published a concrete five-server example in its advanced tool use write-up: GitHub at 35 tools (~26K tokens), Slack at 11 tools (~21K), Sentry at 5 (~3K), Grafana at 5 (~3K), and Splunk at 2 (~2K).

    That is 58 tools consuming approximately 55,000 tokens before the conversation even starts.

    Anthropic’s own internal setup is worse: tool definitions there consume 134,000 tokens before optimization. On a 200K context window, that is two-thirds of the budget spent on a menu the agent mostly ignores.

    Now price it. Claude Sonnet 5 costs $2 per million input tokens and Claude Opus 5 costs $5, per the official pricing page. Sonnet 5’s introductory rate was made permanent on August 10, 2026, and the scheduled September increase was cancelled.

    What does that cost per month at real volume?

    Setup Tokens per request Sonnet 5 cost / request 10,000 runs / month
    Anthropic’s internal tool set 134,000 $0.268 $2,680
    58 MCP tools (5 servers) ~55,000 $0.110 $1,100
    Same tools + Tool Search Tool ~8,700 $0.017 $174
    20 Skills + 1 triggered SKILL.md ~7,000 $0.014 $140
    20 Agent Skills (metadata only) ~2,000 $0.004 $40
    Token figures from Anthropic; cost math at the published $2/MTok Sonnet 5 input rate, uncached.

    The spread between the top and bottom row is 27x. On Opus 5 at $5 per million input tokens, the same gap costs $6,700 versus $100 a month.

    The prompt caching escape hatch

    MCP defenders have a real counterargument: cache the tool definitions. Anthropic’s pricing page lists cache reads at 0.1x the base input rate — $0.20 per million tokens on Sonnet 5.

    Cached, that 55,000-token block drops from $0.110 to about $0.011 per request. A 1-hour cache write costs 2x base and pays for itself after two reads.

    So caching closes most of the gap — if your traffic is dense enough to keep the cache warm and your tool list is stable. Bursty, low-volume agents get cache misses and pay full freight.

    Does trimming tools hurt accuracy?

    No — it helps. This is the part that surprises people. Anthropic’s Tool Search Tool delivers an 85% reduction in token usage while keeping the full tool library reachable, cutting roughly 77K tokens of overhead down to about 8.7K and preserving 95% of the context window.

    Accuracy went up. Opus 4 improved from 49% to 74% on the tool-use benchmark. Opus 4.5 went from 79.5% to 88.1%.

    Programmatic Tool Calling shows the same pattern: average usage dropped from 43,588 to 27,297 tokens, a 37% reduction on complex research tasks, while internal knowledge retrieval rose from 25.6% to 28.5% and GIA scores went from 46.5% to 51.2%.

    Fewer tools in context means less for the model to confuse. Context bloat is an accuracy problem wearing a cost problem’s clothes.

    When should you still build an MCP server?

    When you need a live connection, real authentication, or one integration shared across many agents. Skills are static files — they cannot hold an OAuth token, stream an update, or talk to your database. MCP is the transport layer, and nothing about Skills replaces it.

    MCP’s adoption numbers back that up. Claude’s connector directory now lists over 950 MCP servers, and MCP has passed 400 million monthly SDK downloads — a 4x increase this year, per Anthropic’s spec announcement. The TypeScript and Python SDKs have crossed a billion total downloads between them.

    Use this five-question test before you write a line of either:

    1. Does it need a live connection to a running system? MCP server.
    2. Does it need per-user auth or scoped permissions? MCP server.
    3. Is the hard part procedure and judgment, not access? Agent Skill.
    4. Is it large reference material used occasionally? Agent Skill — Level 3 files cost nothing until opened.
    5. Do many agents share one integration? MCP server centralizes it; a Skill travels with the agent.

    Agent Skills vs MCP: which should you use for your task?

    What you’re building Pick Reason
    Read/write live Slack, GitHub or Jira data MCP server Needs a live, authenticated connection
    House style guide, review checklist, report format Agent Skill Pure procedure; ~100 tokens idle
    Ship 300 pages of API reference to the agent Agent Skill Level 3 files cost 0 tokens until read
    Per-user OAuth scopes and audit trails MCP server Auth belongs at the connection layer
    Deterministic script the agent runs but shouldn’t read Agent Skill Script code never enters context
    One integration consumed by a dozen agents MCP server Central updates, single surface
    Pull live data and apply a fixed workflow Both MCP for access, Skill for method
    Low-volume, bursty agent on a tight budget Agent Skill Cold caches make MCP overhead expensive
    The production default is both — MCP for reach, Skills for method.

    What did the 2026-07-28 MCP spec change?

    It made MCP stateless, which is the single biggest cost change on the server side. The initialize/initialized handshake and the Mcp-Session-Id header are retired. Every request is now self-describing, so any request can land on any instance behind a plain round-robin load balancer.

    That kills the shared-storage requirement. You can run MCP serverless and stop paying for sticky sessions.

    Method and tool names now travel in HTTP headers rather than JSON bodies, letting gateways route without parsing payloads. Multi Round-Trip Requests replace server-initiated streams for interactive confirmations.

    Roots, Sampling and Logging are deprecated but will keep working for at least twelve months, as will the legacy HTTP+SSE transport. You have a year to migrate — not a weekend.

    One number worth sitting with: at Honeycomb, nearly 20% of all monthly interactive queries are now made by agents, not humans. That ratio is why the per-request tax matters.

    Frequently asked questions

    Are Agent Skills a replacement for MCP?

    No. Skills carry procedural knowledge as files; MCP carries live, authenticated access. Anthropic shipped both to GA in 2026 and the standard production pattern uses them together.

    How many tokens does one Agent Skill cost?

    Roughly 100 tokens for its metadata at startup, per Anthropic’s documentation. The SKILL.md body — under about 5,000 tokens — loads only when the Skill is triggered.

    Do MCP tool definitions get charged on every request?

    Yes, unless cached. Anthropic’s example of 58 tools across five servers consumes roughly 55,000 input tokens per request. Prompt caching cuts that to 0.1x the base rate on cache hits.

    Does the Files API cost extra?

    No. Upload, download, list, metadata and delete are free. You pay only for file content that enters a Messages request, billed as normal input tokens.

    Is the old MCP spec still supported?

    Yes. Roots, Sampling, Logging and the HTTP+SSE transport are deprecated but supported for at least twelve months from the 2026-07-28 release.

    Which model should I run agents on to keep costs down?

    Claude Sonnet 5 at $2/$10 per million tokens is the volume workhorse; its introductory pricing was made permanent on August 10, 2026. Opus 5 at $5/$25 is 2.5x the input cost — worth it only when the task genuinely needs it.

    Do fewer tools in context make agents dumber?

    The opposite. With Tool Search Tool enabled, Opus 4.5 improved from 79.5% to 88.1% on Anthropic’s tool-use benchmark while using 85% fewer tokens.

    The bottom line

    Build Skills first. They are nearly free to keep loaded, they went GA on August 19, and Anthropic’s own benchmarks show that trimming context raises accuracy rather than lowering it.

    Add MCP servers only where you need a live connection, real auth, or one integration shared across many agents — then wrap them in Tool Search Tool and prompt caching on day one, not after the first surprising invoice.

    The decision rule is exactly this: if the hard part is reaching the system, build MCP; if the hard part is knowing what to do once you’re there, build a Skill. Teams running high-volume agents on the naive pattern are paying somewhere between 8x and 27x more per request than they need to, and getting worse answers for the money.

    Related reading on agent economics: our breakdown of Claude Code vs Codex CLI on cost, the Gemini 3.7 Flash vs Claude Sonnet 5 cost-per-coding-point comparison, and ChatGPT Business vs Claude Team on the seat-license side.

    Sources

  • ChatGPT Business vs Claude Team: Which $125 AI Seat Wins

    ChatGPT Business vs Claude Team has collapsed into a price tie. Both labs now charge $125 per premium seat per month, $100 on annual billing, and $25 for a standard seat. The sticker no longer decides it. Claude Team wins for engineers who live in a terminal. ChatGPT Business wins for mixed teams that need research, documents, and code in one subscription.

    What changed in AI seat pricing in August 2026?

    OpenAI introduced $125 premium seats for ChatGPT Business on August 11, 2026, aimed at users hitting rate limits. Anthropic already sold a $125 premium Team seat. Meta is preparing Hatch, its first paid AI product, with a tier reported as high as $199.99 per month. Flat-rate AI is over.

    Why flat-rate AI plans broke

    Agents do not chat. They loop. A single agentic coding task can consume more tokens than a week of manual prompting.

    OpenAI said the change was driven by teams “tackling more complex tasks,” according to The Decoder’s August 11 report. The subtext is simpler: $25 seats were losing money on power users.

    The five-hour cap that started it

    Standard ChatGPT Business seats are throttled by a rolling five-hour usage limit. Premium seats remove it and add five times the capacity, with usage resetting weekly for both tiers.

    Anthropic runs the same playbook. Claude’s own pricing page describes Pro usage in terms of “per 5-hour session,” and Max tiers sell 5x or 20x that allowance for $100 and up.

    How much does each seat actually cost?

    Standard seats are $25 monthly or $20 annually at both vendors, with a two-seat minimum. Premium seats are $125 monthly or $100 annually at both. Anthropic caps self-serve Team plans at 150 people. Microsoft charges $30 per user on top of an existing Microsoft 365 license.

    The full price comparison

    Plan Monthly Annual (per mo.) Seat rules Usage
    ChatGPT Business standard $25 $20 2 seat minimum 5-hour rolling cap
    ChatGPT Business premium $125 $100 Mix with standard 5x, no 5-hour cap
    Claude Team standard $25 $20 2–150 people Above Pro limits
    Claude Team premium $125 $100 2–150 people 5x standard seat
    Claude Enterprise $20 + usage Annual only Contact sales Metered at API rates
    Microsoft 365 Copilot $30 add-on Annual commit Needs base license Tenant-level

    Sources: claude.com/pricing, The Decoder, and ExplainX’s 2026 Copilot licensing breakdown.

    What the premium markup really buys

    Notice the ratio. OpenAI charges 5x the price for 5x the usage. That is perfectly linear — there is no volume discount for committing to a heavier seat.

    You are not buying cheaper tokens. You are buying permission to keep working past lunchtime.

    Which is better for coding, ChatGPT Business or Claude Team?

    Claude Team, narrowly. Both bundle a terminal coding agent into paid seats, but Anthropic includes Claude Code across Pro, Max, Team and Enterprise with no separate line item. OpenAI bills Codex, Deep Research and agent workflows against per-seat caps, which makes heavy coding weeks unpredictable.

    Terminal access is the dividing line

    Claude’s pricing page lists Claude Code as included on every paid tier, including the $20 Pro plan. That is unusual generosity for a product this expensive to serve.

    OpenAI’s structure is different: Codex runs against the same seat allowance as chat and research. Burn your budget on a refactor and your product manager’s Deep Research queries compete for the same pool.

    We ran the head-to-head on the tools themselves in Claude Code vs Codex CLI. The seat math reinforces that verdict rather than changing it.

    Where OpenAI takes the win back

    Breadth. A single ChatGPT Business seat covers Deep Research, Sora, agent mode and Codex. Anthropic has no video model and no consumer media stack.

    If your team is five engineers, that does not matter. If it is fifty people across marketing, legal and engineering, it matters a lot.

    Is a $125 premium seat cheaper than paying API rates?

    Only if the seat holder is genuinely heavy. At Anthropic’s published API rates, $125 buys a specific and knowable amount of compute. Below that threshold you are subsidizing the vendor. Above it, the flat seat is one of the best deals in enterprise software.

    The break-even numbers

    Using Anthropic’s official API price list — Sonnet 5 at $2 per million input tokens and $10 per million output, Opus 5 at $5 and $25 — a $125 monthly seat is worth:

    • 12.5 million Sonnet 5 output tokens at standard rates
    • 5 million Opus 5 output tokens, the frontier-tier equivalent
    • 25 million Sonnet 5 output tokens if you route through the Batch API, which is discounted 50%
    • 62.5 million Sonnet 5 input tokens on uncached prompts
    • 625 million cached input tokens, since cache reads bill at 0.1x the base input rate

    That last line is the one finance teams miss. Prompt caching turns a $125 seat into an enormous amount of context.

    The verdict on break-even

    A developer running agentic coding loops daily will clear 12.5 million output tokens without trying. A weekly Deep Research user will not come close.

    Mix your seats. Both vendors let standard and premium sit in the same workspace, and buying every seat at $125 is the most common way teams overspend.

    How do Microsoft Copilot and Meta Hatch compare?

    Badly, on price. Microsoft 365 Copilot costs $30 per user monthly but requires a qualifying base license, pushing the true all-in cost to roughly $66–$69 on E3 and $87–$90 on E5. Meta’s Hatch has not launched publicly and its pricing remains reported, not confirmed.

    Copilot’s stacked-license problem

    The $30 headline is not the bill. ExplainX puts Microsoft 365 E3 at roughly $36–$39 per user and E5 at $57–$60 before Copilot is added.

    Small businesses get a break: Microsoft 365 Copilot Business runs $18 per user promotionally through December 31, 2026, rising to a $21 standard rate, capped at 300 seats.

    There is an irony worth pricing in. Microsoft’s Copilot Cowork tier reportedly runs on Claude Opus 4.8 and Sonnet 5. You can pay Microsoft roughly $60 a month for a wrapper around models Anthropic will sell you directly at $25.

    Meta Hatch and the $199.99 question

    Meta is preparing Hatch as its first paid AI product, a consumer agent that completes multi-step tasks across apps. The Decoder reported a premium tier priced as high as $199.99 per month, with free-tier limits five to ten times lower.

    Testing has reportedly involved DoorDash, Reddit and Etsy. That is a consumer commerce agent, not a work seat. Treat it as a signal about where price ceilings are heading, not a procurement option — and note it will migrate to Meta’s own model, which we sized up in Muse Spark vs Claude Opus 5.

    Which AI seat should your team buy in 2026?

    Match the seat to the workload, not the org chart. Engineering teams should default to Claude. Mixed-function teams should default to OpenAI. Companies already locked into E5 licensing should audit whether Copilot is duplicating spend before renewing.

    Recommendations by use case

    Your situation Buy this Monthly cost Why
    2–10 engineers, agentic coding daily Claude Team premium $100–$125/seat Claude Code included, no separate metering
    Mixed team, research + docs + some code ChatGPT Business standard $20–$25/seat Widest feature surface per dollar
    Heavy agent workloads, 150+ people Claude Enterprise $20/seat + API usage Pay real token rates, not a 5x flat markup
    Already on Microsoft 365 E5 Copilot, cautiously ~$87–$90 all-in Only if native Office integration is the point
    Under 300 seats, cost-sensitive SMB Copilot Business $18 promo / $21 std Cheapest Microsoft-native route until Dec 31, 2026
    Solo operator or two-person startup Claude Pro or ChatGPT Plus $17–$20 Team seats add governance you do not need yet

    The move most teams should make first

    Do not buy premium seats on day one. Buy standard seats, watch who hits the cap for two consecutive weeks, and upgrade only those people.

    At $1,200 per premium seat annually, guessing wrong on a ten-person team costs $12,000 a year for capacity nobody used.

    Frequently asked questions

    Is Claude Team cheaper than ChatGPT Business?

    No. They are identical: $25 monthly or $20 annually for standard seats, $125 monthly or $100 annually for premium. The differentiator is what each seat includes, not what it costs.

    Do premium seats include the coding agent?

    Claude includes Claude Code on every paid tier per Anthropic’s pricing page. OpenAI bills Codex against your per-seat allowance, so a premium seat buys more Codex headroom rather than separate access.

    What is the seat minimum for each plan?

    Both ChatGPT Business and Claude Team start at two seats. Anthropic caps self-serve Team plans at 150 people, above which you move to Enterprise.

    Is Claude Enterprise better value than Team premium?

    For heavy agentic use, yes. Claude Enterprise bills $20 per seat plus usage at API rates, so you pay the real cost of tokens instead of a flat 5x markup on a capacity estimate.

    Why did OpenAI add premium seats now?

    Agents consume far more tokens than chat. OpenAI announced the tier on August 11, 2026, citing teams tackling more complex tasks — which is another way of saying standard seats were unprofitable for power users.

    Does Microsoft Copilot really cost $30?

    Only as an add-on. A qualifying Microsoft 365 license is required, taking realistic all-in cost to roughly $66–$69 per user on E3 or $87–$90 on E5.

    Should I wait for Meta Hatch?

    No. Hatch has no confirmed public release date, no confirmed price, and appears aimed at consumers rather than teams. Buy the seat you need this quarter.

    The bottom line

    Claude Team is the better buy for engineering-led organizations, and it is not close. Claude Code ships on every paid tier, terminal work is not metered against your product manager’s research queries, and Claude Enterprise offers the only pricing model in this comparison that scales honestly with consumption at $20 per seat plus API rates.

    ChatGPT Business wins exactly one scenario: teams where fewer than half the seats write code. Breadth beats depth when the average user needs research, documents and images more than a terminal.

    Microsoft Copilot is the weakest value in the group unless deep Office integration is a hard requirement, because the stacked-license structure roughly triples the advertised price.

    The strategic read matters more than the seat choice. Two competing labs landed on identical $25 and $125 price points within weeks of each other. That is not coincidence — it is a market discovering the clearing price for agentic compute. Expect the next move to be upward, and lock annual pricing while $100 premium seats exist. For the token-level economics underneath these plans, see our breakdown in Gemini 3.7 Flash vs Claude Sonnet 5 and our guide to choosing a replacement model after a deprecation.

    Sources

  • Best o3 Replacement: What to Use After the August 26 Cutoff

    OpenAI retires o3 from ChatGPT on August 26, 2026. The best o3 replacement for most teams is GPT-5.6 Terra at $2/$12 per million tokens — not the officially recommended Sol at $5/$30. Terra posts 90.4% on GPQA Diamond against o3’s 87.7%, at 60% less output cost. API users are not on the same clock: their o3 shutdown is December 11.

    The reasoning model that defined 2025 is being switched off. And the migration advice OpenAI published is the expensive option.

    Here is what actually changes on Tuesday, what each o3 replacement costs, and which one wins for your workload.

    What exactly happens to o3 on August 26, 2026?

    o3 disappears from the ChatGPT model picker on August 26, 2026. That is a product change, not an API shutdown. OpenAI’s release notes from May 28, 2026 confirm o3 is “retired from ChatGPT on August 26, 2026 following a 90-day sunset period.” Developers calling o3 over the API keep working past that date.

    The ChatGPT side: the picker already stopped naming models

    ChatGPT users lose nothing they can still see. Since the June 10, 2026 model picker update, OpenAI stopped exposing version numbers entirely.

    The picker now offers Instant, Medium, High, and Extra High, plus Pro Standard and Pro Extended on Pro plans. Reasoning is sold as effort, not as a model name.

    So for consumer subscribers, the o3 replacement is already installed. Selecting High is the closest analogue to what o3 Thinking used to do.

    The API side: your real deadline is December 11

    This is where most coverage gets it wrong. OpenAI’s deprecations page lists o3-2025-04-16 with a shutdown date of December 11, 2026, migrating to gpt-5.6-sol. o3-pro-2025-06-10 follows the same date, moving to Sol with reasoning.mode: pro.

    What does die on August 26 is the legacy Assistants API. Anything built on Assistants must move to the Responses and Conversations APIs by Tuesday. That is the deadline worth panicking about.

    Which o3 replacement is best for most workloads?

    GPT-5.6 Terra. OpenAI names Sol as the official successor, but Sol is priced for frontier reasoning at $5/$30 per million tokens. Terra sits at $2/$12 and clears o3 on the benchmarks that mattered to o3 users. For the overwhelming majority of o3 traffic, paying Sol rates is a rounding error you repeat a million times.

    Terra got cheaper on July 30, 2026, when OpenAI cut its price roughly 20% as part of the GPT-5.6 pricing refresh. Luna fell about 80% in the same announcement, to $0.20/$1.20.

    The quality case is not a stretch either. OpenRouter’s provider data puts Terra at 90.4% on GPQA Diamond and 75.3% on TAU-Bench. o3 scored 87.7% on GPQA Diamond at launch.

    Why Terra beats Sol on cost per task

    Reasoning models bill you for tokens you never see. A 500-token visible answer can consume 2,000+ tokens once hidden reasoning is counted.

    That multiplier is exactly why output price dominates your bill. At $12 versus $30 per million output tokens, Terra cuts the expensive half of the invoice by 60%.

    Terra also carries a 1,050,000-token context window with 128,000 max output — over five times o3’s 200K context. You are not trading capability down.

    How much does each o3 replacement cost?

    Prices below are list rates per million tokens as of August 24, 2026, pulled from provider pricing pages. Cached input bills at 10% of standard rates on OpenAI, and the Batch API halves both sides.

    Model Input Output Context Notes
    o3 (retiring) $2.00 $8.00 200K API shutdown Dec 11, 2026
    o3-pro (retiring) $20.00 $80.00 200K API shutdown Dec 11, 2026
    GPT-5.6 Terra $2.00 $12.00 1.05M Cut ~20% on Jul 30, 2026
    GPT-5.6 Sol $5.00 $30.00 1.05M Official o3 successor
    GPT-5.6 Luna $0.20 $1.20 1.05M Cut ~80% on Jul 30, 2026
    Claude Opus 5 $5.00 $25.00 200K Fast Mode is $10/$50
    Claude Sonnet 5 $2.00 $10.00 200K $2/$10 now permanent
    Gemini 3.7 Flash $0.75 $3.75 — Doubles Jan 1, 2027
    Kimi K3 $2.60 $13.00 1M Open weights, 2.8T params

    Read that table one way and the story is obvious: o3 at $2/$8 was cheap, and every direct successor except Luna and Gemini Flash costs more per output token. Migration is a price increase unless you choose deliberately.

    Is GPT-5.6 Sol worth $5/$30 in 2026?

    Only for the top slice of your traffic. Sol is the frontier tier and the only model OpenAI formally maps o3-pro onto, via reasoning.mode: pro. If you were paying o3-pro’s $20/$80, Sol at $5/$30 is a 75% input cut and a 62.5% output cut.

    If you were on standard o3, Sol is a 150% input increase and a 275% output increase. Same model family, opposite financial outcome.

    There is also a long-context trap. Requests beyond the standard threshold reprice: Sol rises to $10/$45, Terra to $4/$18, Luna to $0.40/$1.80. Feeding a million-token repo into Sol is a different product than a 20K-token prompt.

    Which o3 replacement should you pick for your use case?

    Match the model to the job, not to the vendor’s migration note. Below is where each option earns its price, based on published benchmarks and list pricing. Benchmark your own top 20 prompts against Terra before escalating anything to Sol.

    Use case Pick Why
    General o3 traffic GPT-5.6 Terra 90.4% GPQA Diamond at $2/$12; 1.05M context
    Hardest reasoning, o3-pro traffic GPT-5.6 Sol Official reasoning.mode: pro path; 62.5% cheaper output than o3-pro
    High-volume classification GPT-5.6 Luna $0.20/$1.20 after the ~80% July cut
    Agentic coding, terminal work Gemini 3.7 Flash 85.8% on Terminal-bench 2.1 at $0.75/$3.75
    Long document analysis Claude Opus 5 $5/$25 with 200K context, cheaper output than Sol
    Balanced daily driver Claude Sonnet 5 $2/$10 locked permanently
    Self-hosting, data residency Kimi K3 2.8T open weights, 1M context, $2.60/$13 hosted

    What about Claude and Gemini as an o3 replacement?

    Both are live options, and both moved on price in the last two weeks. Anthropic canceled a scheduled increase; Google launched a discount with an expiry date attached. Those two facts change the math more than any benchmark did.

    Claude Sonnet 5’s price freeze quietly killed a 50% increase

    Sonnet 5 launched at $2/$10 as introductory pricing set to expire August 31, 2026, with a jump to $3/$15 scheduled for September 1. Anthropic’s pricing documentation now states that increase “will not occur” and $2/$10 is the standard price.

    That makes Sonnet 5 an exact price match to o3 on input and 25% more on output — the closest financial like-for-like swap available. We broke down how it stacks up against Google’s cheap tier in Gemini 3.7 Flash vs Claude Sonnet 5.

    Gemini 3.7 Flash is the cheapest credible option — until January

    Gemini 3.7 Flash shipped August 13, 2026 with a 50% introductory cut to $0.75/$3.75. On January 1, 2027 it reverts to $1.50/$7.50, and context caching moves from $0.075 to $0.15.

    Its numbers are strong where agents live: 85.8% on Terminal-bench 2.1 and 65.3% on DeepSWE v1.1, though only 43.6% on FrontierCode 1.1 Main. Build your 2027 budget on the standard rate, not the promo.

    How do you migrate off o3 without breaking production?

    Treat this as a pricing audit, not a find-and-replace. The single most expensive mistake is routing all o3 traffic to Sol because the deprecation table said so. Work through it in this order:

    1. Split the Assistants API work out first. It dies August 26, 2026 — 107 days before o3 does. Move to Responses and Conversations now.
    2. Pull your last 30 days of o3 token spend and split it by input versus output. Output volume decides which tier you can afford.
    3. Replay your top 20 prompts against Terra. If quality holds, you are done at $2/$12.
    4. Escalate only the failures to Sol. Route by task difficulty, not by default.
    5. Push bulk classification to Luna or Gemini 3.7 Flash. At $0.20/$1.20, Luna makes some batch jobs nearly free.
    6. Turn on prompt caching and the Batch API. Cache hits bill at 10%; batch halves everything.

    Teams running agentic coding harnesses should also re-check their tooling layer, not just the model. We covered that trade-off in Claude Code vs Codex CLI.

    Frequently asked questions about the o3 replacement

    Is o3 gone from the API on August 26, 2026?

    No. August 26 is the ChatGPT retirement date. OpenAI’s deprecations page lists the o3-2025-04-16 API shutdown as December 11, 2026, with gpt-5.6-sol as the migration target.

    What is the cheapest o3 replacement?

    GPT-5.6 Luna at $0.20/$1.20 per million tokens, following its roughly 80% price cut on July 30, 2026. For work needing more reasoning depth, Gemini 3.7 Flash at $0.75/$3.75 is the next step up.

    Does GPT-5.6 Terra actually beat o3?

    On GPQA Diamond, yes: 90.4% for Terra on OpenRouter’s provider data versus 87.7% for o3 at launch. Terra also carries a 1.05M context window against o3’s 200K.

    What replaces o3-pro?

    GPT-5.6 Sol with reasoning.mode: pro, per OpenAI’s deprecations table. At $5/$30 versus o3-pro’s $20/$80, it is a substantial price cut for that specific tier.

    Will Claude Sonnet 5 get more expensive in September?

    No. Anthropic canceled the September 1, 2026 increase to $3/$15. The $2/$10 rate is now permanent per its pricing docs.

    Is there an open-weights o3 replacement?

    Kimi K3 is the closest: 2.8 trillion parameters, 1M context, released July 16, 2026, and $2.60/$13 hosted on OpenRouter. Chinese open-weight coders are also competitive — see our GLM-5.3 vs DeepSeek V4 Pro breakdown.

    What breaks on August 26 if I do nothing?

    Two things: o3 vanishes from the ChatGPT picker, and the legacy Assistants API stops working. API calls to o3 itself keep running until December 11, 2026.

    The bottom line

    Move general o3 traffic to GPT-5.6 Terra at $2/$12 and stop there. It beats o3 on GPQA Diamond, gives you five times the context, and costs 60% less per output token than the Sol tier OpenAI points you at.

    Send only o3-pro-class work to Sol, where $5/$30 is genuinely a 62.5% output discount on what you were paying. Send bulk work to Luna at $0.20/$1.20.

    The decision depends on exactly one number: your output-token share. Above roughly 30% of spend, tier choice dominates everything else on your invoice. Below that, input caching matters more than which model you pick.

    And fix your Assistants API code before Tuesday. That is the only hard deadline this week. For more on where frontier pricing is heading, see our analysis of DeepSeek’s vision model against Claude Opus 4.8.

    Sources

  • DeepSeek Vision Model vs Claude Opus 4.8: 23x Cheaper Per Image

    DeepSeek’s vision model just split a 2-2 draw with Claude Opus 4.8 on visual benchmarks while charging roughly 23 times less per image. V4-Flash-Vision-Exp processes 1,000 images for about $0.17 at peak rates. Claude Sonnet 4.6 charges around $4 for the same work. If your workload is image-heavy and volume-driven, the math is no longer close.

    The DeepSeek vision model shipped on August 21, 2026. It is experimental, API-only, and it does something DeepSeek has done before: it removes a price floor the incumbents were standing on.

    What is DeepSeek’s new vision model?

    V4-Flash-Vision-Exp is an experimental multimodal extension of DeepSeek’s V4-Flash, adding image understanding to a model that was previously text-only. It is available on DeepSeek’s paid developer platform by setting model='deepseek-v4-flash-vision-exp', per the official API changelog.

    The target is not image generation. It is visual agent work — reading charts, parsing screenshots, driving interfaces, and running multi-step tasks that require seeing what happened.

    DeepSeek’s own framing is careful. The changelog calls it “a significant leap over DeepSeek-V4-Flash, bringing its multimodal agent capabilities close to Opus-4.8,” while noting pure-text performance is unchanged from the base model.

    What’s under the hood

    The foundation is V4-Flash, released in April 2026: a 284-billion-parameter mixture-of-experts model built from multiple 13-billion-parameter networks, according to SiliconANGLE.

    The vision specs are aggressive. A 1-million-token context window, up to 384,000 output tokens, and up to 600 images per request. Each image is capped at 384 tokens regardless of size — that cap is the entire reason the pricing works.

    Supported formats are JPEG, PNG, GIF and WebP, with a maximum edge of 8,192 pixels, dropping to 4,096 pixels once you send 15 or more images in a single call.

    Did the DeepSeek vision model actually beat Claude Opus 4.8?

    On two benchmarks yes, on two no. It is a genuine 2-2 split, not a sweep, and the margins are thin in both directions. DeepSeek wins the agentic and hard-visual tests. Opus 4.8 holds the chart-reading and structured-reasoning tests. Anyone claiming a decisive winner is selling something.

    Benchmark DeepSeek V4-Flash-Vision-Exp Claude Opus 4.8 Margin
    Agents’ Last Exam (ALE) 27.3 25.7 +1.6 DeepSeek
    ZeroBench (Pass@5) 35.0 34.0 +1.0 DeepSeek
    ApexBench (Pass@1) 36.5 39.4 +2.9 Opus 4.8
    Chartography 64.3 65.0 +0.7 Opus 4.8

    Scores as reported by XenoSpectrum’s breakdown. DeepSeek’s changelog separately lists a Terminal Bench 2.1 score of 83.9.

    Context on the tests matters. ALE contains more than 1,000 multi-step tasks; ZeroBench is 100 image-analysis tasks deliberately built to be hard, SiliconANGLE reports. Those are the two DeepSeek won.

    The pattern is consistent with what we saw when GLM-5.3 went up against DeepSeek V4 Pro: Chinese labs are now trading benchmark wins with frontier Western models rather than trailing them.

    How much does the DeepSeek vision model cost?

    Very little, and the structure is unusual. DeepSeek runs peak and off-peak pricing. Off-peak, input is $0.22 per million tokens and output is $0.66. During peak hours — 01:00–04:00 and 06:00–10:00 UTC — both double, to $0.44 and $1.32. Cache hits cost $0.007 off-peak.

    The image economics fall out of the 384-token cap. A single image costs $0.00008448 off-peak and $0.00016896 at peak.

    Model ~Cost per 1,000 images (1024×1024) Input / output per 1M tokens Context
    DeepSeek V4-Flash-Vision-Exp (off-peak) ~$0.09 $0.22 / $0.66 1M
    DeepSeek V4-Flash-Vision-Exp (peak) ~$0.17 $0.44 / $1.32 1M
    Google Gemini 3.1 Pro ~$0.50 $2.00 / $12.00 1M
    OpenAI GPT-5.4 Vision ~$1.90 $2.50 / $15.00 1.1M
    Anthropic Claude Sonnet 4.6 ~$4.00 — —
    Anthropic Claude Opus 4.8 — $5.00 / $25.00 1M

    Per-image costs are as calculated by BigGo Finance; Opus 4.8 token rates are $5.00 input, $25.00 output and $0.50 cached input, per OpenRouter.

    Set those side by side. On output tokens, DeepSeek off-peak at $0.66 against Opus 4.8 at $25.00 is a 37x spread for a model that just won two of four visual benchmarks against it.

    Why the 384-token image cap matters

    Most vision APIs price images by resolution. Send a bigger image, pay more tokens. That makes cost forecasting miserable and punishes exactly the high-resolution documents that need vision in the first place.

    DeepSeek caps every image at 384 tokens no matter its size, up to an 8,192-pixel edge. A dense scanned contract and a low-resolution thumbnail cost the same.

    For finance and operations teams, that turns a variable line item into a fixed one. You can price a document-processing product per page before you have seen a single customer’s files, which is not something the resolution-scaled competitors let you do.

    Is the DeepSeek vision model cheap enough to switch?

    For high-volume image pipelines, yes — the gap is too large to argue with. For low-volume work where quality variance costs more than tokens, no. The deciding number is how many images you process per month, because below roughly 100,000 images the absolute savings stop mattering.

    Run it concretely. A million images per month costs about $170 at DeepSeek’s peak rate, $90 off-peak, and roughly $4,000 on Claude Sonnet 4.6. That is a $46,000 annual difference on a single pipeline.

    • Document and invoice processing at scale. Switch. The 384-token cap makes cost per page nearly flat.
    • Screenshot-driven agents and RPA. Switch. ALE and ZeroBench are exactly this workload, and DeepSeek won both.
    • Chart and dashboard extraction. Stay. Opus 4.8 leads Chartography 65.0 to 64.3, and errors here are expensive.
    • Regulated or air-gapped work. Stay. This is a Chinese paid API with no open weights announced.
    • Batch archive backfill. Switch, and schedule it off-peak. You halve the bill by choosing when to run.

    That off-peak lever is genuinely underrated. Almost no batch job cares what hour it runs, and DeepSeek is effectively paying you 50% to move it.

    Which vision model should you use for what?

    Match the model to the failure cost, not to the benchmark chart. Where a wrong answer is cheap and volume is enormous, take the cheapest capable model. Where a wrong answer triggers a human review cycle or a compliance event, the token savings are irrelevant.

    Use case Pick Why
    Bulk OCR, receipts, invoices DeepSeek V4-Flash-Vision-Exp ~$0.09–$0.17 per 1,000 images; flat 384-token cost per image
    Visual agents, UI automation DeepSeek V4-Flash-Vision-Exp Wins ALE (27.3) and ZeroBench (35.0) head-to-head
    Financial charts, data extraction Claude Opus 4.8 Leads Chartography 65.0 and ApexBench 39.4
    Mixed text-plus-vision reasoning Claude Opus 4.8 DeepSeek’s text performance is unchanged from V4-Flash
    Mid-volume general vision Gemini 3.1 Pro ~$0.50 per 1,000 images with Western data residency
    Production systems needing stability Claude Opus 4.8 or Gemini DeepSeek’s model is explicitly labeled experimental

    What are the catches?

    Three, and none of them are small. The model is experimental, it is closed, and the benchmark margins are inside the noise band. Anyone budgeting a migration on a 1.0-point ZeroBench edge is over-reading the data.

    Start with the “Exp” in the name. DeepSeek ships experimental endpoints and retires them. Building a production pipeline on one is a scheduling risk, not just a quality risk.

    Second, there are no open weights. SiliconANGLE notes DeepSeek “may release a free version later on,” but nothing is confirmed. That kills the self-hosting escape hatch that made Qwen3.8-Max interesting for teams with data-residency constraints.

    Third, the wins are narrow. +1.6 on ALE and +1.0 on ZeroBench are real but slim, and DeepSeek loses ApexBench by a wider 2.9 points than either of its victories. The honest summary is parity, delivered at a fraction of the price — which is still a devastating result for Anthropic’s pricing power.

    Frequently asked questions

    What is DeepSeek V4-Flash-Vision-Exp?

    An experimental multimodal model released on August 21, 2026, extending DeepSeek’s V4-Flash with image understanding. It handles up to 600 images per request with a 1-million-token context window, available only through DeepSeek’s paid API.

    Is the DeepSeek vision model better than Claude Opus 4.8?

    It splits 2-2. DeepSeek wins Agents’ Last Exam (27.3 vs 25.7) and ZeroBench Pass@5 (35.0 vs 34.0). Opus 4.8 wins ApexBench Pass@1 (39.4 vs 36.5) and Chartography (65.0 vs 64.3).

    How much does the DeepSeek vision model cost per image?

    A single image is capped at 384 tokens, costing $0.00008448 off-peak and $0.00016896 at peak. That works out to roughly $0.09 to $0.17 per 1,000 images.

    What are DeepSeek’s peak and off-peak hours?

    Peak hours run 01:00–04:00 and 06:00–10:00 UTC, when input and output rates double to $0.44 and $1.32 per million tokens. Everything outside those windows bills at $0.22 and $0.66.

    Are the weights open?

    No. V4-Flash-Vision-Exp is available only through DeepSeek’s paid developer platform. Reports suggest a free version may follow, but nothing has been confirmed.

    Does it hurt text performance?

    No. DeepSeek’s changelog states the model stays “on par with the official DeepSeek-V4-Flash” on agent reasoning, world knowledge, and other text-based tasks. The vision capability is additive.

    Should I migrate my production vision pipeline?

    Only if you process more than roughly 100,000 images per month and can tolerate an experimental endpoint. Below that volume, the absolute savings do not justify the migration and stability risk.

    The bottom line

    Move your high-volume image workloads to DeepSeek V4-Flash-Vision-Exp and keep your judgment-heavy visual work on Claude Opus 4.8. That is the split the benchmarks actually support, and it is where the money is.

    The specific trigger: if you process more than 100,000 images a month and a wrong answer costs you less than a human review, switch, and schedule the batch off-peak to halve the bill again. Below that volume, or where a visual error creates a compliance event, the $4-per-1,000 Anthropic bill is cheap insurance.

    The larger read is about pricing power, not vision. Anthropic charges $25 per million output tokens on Opus 4.8. DeepSeek charges $0.66 off-peak and just traded benchmark wins with it. The same compression already hit text and coding models, and multimodal was the last premium tier standing.

    Sources

  • Cerebras vs Groq: Which Fast Inference API Is Worth the Money

    Cerebras vs Groq comes down to one trade. Cerebras serves GPT-OSS-120B at 1,641 tokens per second for $0.75 per million output tokens. Groq serves the same model at roughly 500 for $0.60. You pay about 25% more on output for roughly three times the speed. Buy Cerebras when a human or an agent is waiting. Buy Groq for batch work and overnight jobs.

    That trade just got sharper. On August 18, 2026, Cerebras announced the CS-4, a system it claims runs GPT-OSS-120B at more than 4,400 tokens per second per user.

    If that number survives contact with production traffic, the speed gap stops being a nice-to-have and starts being a product feature you can charge for.

    What changed in the Cerebras vs Groq race this week?

    Cerebras shipped a new generation of silicon and Nvidia now owns its main rival’s technology. Those two facts reshape the fast-inference market. The CS-4 raises Cerebras’ ceiling; the Nvidia-Groq deal means Groq’s aggressive pricing is no longer set by a scrappy independent.

    The CS-4 numbers that matter

    Per the Cerebras announcement, the CS-4 delivers 750 PFLOPS of AI compute against the CS-3’s 125 PFLOPS. Memory bandwidth jumps from 21.6 to 129.6 petabytes per second.

    The WSE-3 Turbo processor behind it packs 4 trillion transistors and 900,000 AI cores across 46,225 square millimeters of silicon, with 44GB of on-chip SRAM.

    Wafer-to-wafer latency drops from 5 microseconds to 2. Cerebras also claims up to 10x the throughput per watt versus the CS-3, and support for models above 50 trillion parameters.

    The CS-4 product page adds a second claim worth watching: more than 1,000 tokens per second on models exceeding 10 trillion parameters. First shipments began in Q3 2026.

    CTO Sean Lie framed the pitch in agent terms: “Being 30 times faster gives an agentic system room for significantly more reasoning, verification, or tool use in the same wall-clock time.”

    Why Nvidia now sits on both sides

    Groq is no longer an independent challenger. CNBC reported on December 24, 2025 that Nvidia agreed to buy Groq’s assets for about $20 billion — its largest deal on record.

    The Groq API still runs and still undercuts Cerebras. But the pricing that made Groq attractive is now a line item inside the company that also sells the GPUs Groq was built to beat.

    That matters for anyone building a business on a specific cost per token. Cerebras is the last large pure-play fast-inference vendor with its own silicon and its own incentive to keep prices down.

    How much does fast AI inference cost per million tokens?

    Cerebras is the most expensive way to run GPT-OSS-120B among mainstream providers. Groq sits mid-pack. Commodity GPU serverless tiers cost a fraction of both. The spread on the identical open-weights model is roughly 7x on output tokens, which is far wider than most teams assume.

    Here is the pricing snapshot for GPT-OSS-120B as tracked by PricePerToken on August 21, 2026, with measured speed where it is published.

    Provider Input $/M Output $/M 1M in + 1M out Measured output speed
    Cerebras $0.35 $0.75 $1.10 1,641 tok/s
    Groq $0.15 $0.60 $0.75 ~500 tok/s
    SambaNova $0.14 $0.95 $1.09 Not published
    Together AI $0.15 $0.60 $0.75 Not published
    Amazon Bedrock $0.15 $0.60 $0.75 Not published
    Baseten $0.10 $0.50 $0.60 Not published
    Google $0.09 $0.36 $0.45 Not published
    Fireworks $0.10 $0.10 $0.20 Not published
    DeepInfra $0.037 $0.170 $0.207 Not published
    OpenRouter $0.030 $0.170 $0.200 Not published
    Cerebras speed from Artificial Analysis; Groq speed from CloudZero, May 2026.

    Run a balanced million-in, million-out workload and Cerebras costs $1.10 against Groq’s $0.75 — a 47% premium. On output tokens alone the gap narrows to 25%.

    Both are expensive next to Fireworks at $0.20 or the OpenRouter route at $0.20 for the same weights.

    The discount lever most teams forget

    Groq’s list price is not its real price. CloudZero notes that Groq’s Batch API and prompt caching each cut rates by 50%, and the two stack to roughly 25% of on-demand pricing.

    Applied to GPT-OSS-120B, that pushes effective output cost toward $0.15 per million. Cerebras publishes no equivalent public stacking discount; its pricing page lists a $5 free trial, a $10 self-serve developer tier, and custom enterprise rates.

    For any workload that tolerates a batch window, Groq is not 20% cheaper. It is closer to 5x cheaper.

    Which is faster in practice, Cerebras or Groq?

    Cerebras wins today, and not narrowly. Independent measurement puts Cerebras at 1,641 tokens per second on GPT-OSS-120B with 0.46 seconds to first token. Groq’s published figure for the same model is around 500 tokens per second. That is a 3.3x throughput advantage before the CS-4 ships at volume.

    Artificial Analysis also clocks Cerebras at 1,402 tokens per second on Gemma 4 31B in reasoning mode, at a blended $0.24 per million.

    What the speed gap costs in real money

    Take a 100,000-token generation — a long agent trace or a full document rewrite.

    • Cerebras today: 61 seconds at 1,641 tok/s, costing $0.075 in output tokens.
    • Groq today: 200 seconds at 500 tok/s, costing $0.060.
    • Cerebras CS-4 claim: 23 seconds at 4,400 tok/s.
    • The math: 1.5 cents buys back 139 seconds — roughly 11 cents per minute of latency removed.

    Eleven cents a minute is trivial if a customer is watching a cursor blink. It is indefensible if the job runs at 3 a.m. and nobody reads the output until morning.

    One honest caveat: the 4,400 tok/s figure is a vendor claim tied to hardware that only started shipping this quarter. The 1,641 figure is measured on the live API. Do not budget against the former.

    Is Cerebras worth the premium for AI agents?

    Yes, for interactive and multi-step agent work — and this is the only case where the premium clearly pays. Agent loops multiply latency: ten sequential tool calls at 200 seconds each is a 33-minute task. The same loop at Cerebras speed finishes in about 10 minutes.

    That compounding is the whole argument. A single completion at 500 tokens per second feels fine. Twenty of them chained behind a task does not.

    Who should buy what

    Use case Pick Why
    Live chat, copilots, voice Cerebras 0.46s TTFT and 1,641 tok/s; latency is the product
    Multi-step agents with tool calls Cerebras Per-step latency compounds across the loop
    Overnight batch, evals, data labeling Groq (Batch API) Stacked discounts reach ~25% of list
    High-volume, cost-capped production Fireworks / DeepInfra $0.20 per 1M in + 1M out on identical weights
    Closed frontier models Neither Both serve open weights only
    Sustained 24/7 single-model load Self-host Fixed GPU cost beats per-token above a break-even

    That last row matters more as open weights close the quality gap. We ran the self-hosting break-even in our Qwen3.8-Max open weights breakdown, and the logic holds here.

    Note the hard limit on both vendors: neither serves closed frontier models. If your stack depends on the newest proprietary coder, this comparison does not apply — see our GLM-5.3 vs DeepSeek V4 Pro comparison for the open-weight options that do run here.

    What do the financials say about who wins?

    Cerebras has the better technology and the shakier income statement. It went public on May 14, 2026, raising $5.5 billion. The stock priced at $185, opened at $385 for a 108% pop, and closed at $311 — a $66 billion valuation. Three months later the market repriced it hard.

    The Q2 miss

    On August 12, 2026, Cerebras reported Q2 revenue of $180.1 million against a $193.6 million consensus, per Investing.com. Adjusted EPS came in at a $2.98 loss versus an expected $0.18 loss. Shares fell 14% after hours.

    Core revenue still grew 103% year over year to $209.9 million, and the company guided FY2026 core revenue to $880–890 million. Growth is not the problem. Margin is: guided core operating margin sits at negative 19% to negative 17%.

    Why that should affect your buying decision

    A vendor losing money on every wafer has two exits: raise prices or get acquired. Cerebras’ 2025 revenue was $510 million on 76% growth with $237.8 million of net income, so the balance sheet is not fragile — but the 2026 trajectory is being funded, not earned.

    TrendForce values the company’s three-year OpenAI partnership at over $20 billion. That is concentration risk dressed as a moat.

    The broader point TrendForce makes is the one to internalize: inference is a recurring cost tied directly to revenue, while training is a one-time R&D expense. Its example is brutal — Taalas’ HC1 delivers Llama 3.1 8B at 0.75 cents per million tokens against 3.79 cents on an Nvidia B200.

    Specialized silicon is roughly five times cheaper per token than general-purpose GPUs at that scale. That is why the price you lock in today is unlikely to be the price in twelve months.

    Frequently asked questions about Cerebras vs Groq

    Is Cerebras faster than Groq?

    Yes. Artificial Analysis measures Cerebras at 1,641 tokens per second on GPT-OSS-120B; Groq’s published figure for the same model is around 500. Cerebras also leads on time to first token at 0.46 seconds.

    Is Groq cheaper than Cerebras?

    Yes. On GPT-OSS-120B, Groq charges $0.15 input and $0.60 output per million tokens versus Cerebras at $0.35 and $0.75. With Groq’s stacked batch and cache discounts, the effective gap widens sharply.

    Does Nvidia own Groq now?

    Nvidia agreed in December 2025 to acquire Groq’s assets for about $20 billion, CNBC reported. The Groq API continues to operate and continues to publish its own pricing.

    Can I run Claude or GPT-5 on Cerebras or Groq?

    No. Both providers serve open-weights models only — GPT-OSS, Llama, Qwen, Gemma and similar. Closed frontier models stay on their vendors’ own APIs.

    When does the CS-4 speed actually arrive for API users?

    Cerebras says first CS-4 shipments began in Q3 2026. The 4,400 tokens per second per user figure is a vendor claim on new hardware, not yet an independently measured API result.

    Is paying for faster inference ever worth it?

    Only when latency is visible to a customer or compounds across an agent loop. At roughly 11 cents per minute of latency removed, speed is cheap for interactive products and pure waste for background jobs. We covered the same trade at the frontier-model layer in OpenAI Ultrafast vs Claude Fast Mode.

    The bottom line

    Route by whether something is waiting. If a human or an agent loop blocks on the token stream, Cerebras is worth its 25% output premium — 3.3x measured throughput for that price is one of the better deals in AI infrastructure, and the CS-4 should widen it.

    If nothing is waiting, Cerebras is a rounding-error upgrade you are overpaying for. Send batch and evaluation traffic to Groq’s Batch API at roughly a quarter of list, or to Fireworks and DeepInfra at $0.20 per million in and out.

    The strategic read is less comfortable. Nvidia owns Groq’s technology, Cerebras is losing money at negative 17% to 19% core operating margin, and specialized silicon is already showing five-fold cost advantages per token. Sign nothing longer than twelve months.

    Sources

  • Claude Code vs Codex CLI: Which Coding Agent Wins on Cost

    Claude Code vs Codex CLI comes down to token math, not vibes. Codex bills GPT-5.6 Sol at $5/$30 per million tokens. Claude Opus 5 runs $5/$25 — but Anthropic’s own docs warn its newer tokenizer emits roughly 30% more tokens for the same text. That erases the discount. Codex wins on cost per solved task. Claude Code wins on agent orchestration.

    Both agents shipped meaningful releases this month, and both are now close enough on quality that price is the deciding variable. Here is the full breakdown, with every figure sourced.

    What changed for Claude Code vs Codex CLI in the last week?

    Anthropic shipped four Claude Code builds between August 13 and August 18, 2026. The headline change is subagent forking on by default. OpenAI’s Codex CLI moved earlier in the month, adding a one-command importer that pulls your Claude Code setup straight into Codex.

    Claude Code v2.1.232 made subagent_type: "fork" the default, so spawned agents inherit the full conversation and prompt cache. It also added cross-session @ mentions, letting one session message another by name.

    v2.1.233 added GitLab merge request support on August 15. v2.1.234, first seen August 18, added automatic session continuation when you hit a usage limit — a direct answer to the biggest complaint about subscription-metered agents.

    Codex CLI v0.147.0, dated August 4, 2026, added --approve-for-me for auto-reviewed approvals, opt-in support for the MCP 2026-07-28 protocol, and /import, which pulls supported setup and recent chats from Claude Code and Cursor. OpenAI is openly courting defectors.

    Which is better for coding, Claude Code or Codex CLI?

    On raw capability the two are inside each other’s error bars. The official Terminal-Bench harness leaderboard puts Claude Code first by 0.7 points. Artificial Analysis, running the same benchmark against models rather than harnesses, puts OpenAI first by 0.4 points. Neither gap is decisive.

    The harness leaderboard gives Claude Code the edge

    On the official Terminal-Bench 2.1 leaderboard, Claude Code running Fable 5 sits at 83.8% (±1.2), submitted June 7, 2026. Codex with GPT-5.5 follows at 83.1% (±1.1).

    Below that, Cursor CLI on Grok 4.5 scores 79.3%, Claude Code on Opus 4.8 hits 78.9%, and Codex on GPT-5.6 Terra lands at 78.4%. Gemini CLI on Gemini 3.1 Pro trails at 65.8%.

    Read that spread carefully: the harness matters less than the model inside it. Swapping Fable 5 for Opus 4.8 inside the same Claude Code wrapper costs 4.9 points.

    The model leaderboard gives OpenAI the edge

    Artificial Analysis runs Terminal-Bench v2.1 across 89 curated tasks spanning software engineering, sysadmin, data processing, model training and security. GPT-5.6 Sol at xhigh effort tops it at 89.5%.

    Claude Opus 5 at max effort scores 89.1%. Grok 4.6 at high effort takes third with 88.4%. A 0.4-point gap on an 89-task benchmark is roughly one third of a task. Call it a tie.

    How much do Claude Code and Codex CLI cost in 2026?

    Anthropic prices Claude Opus 5 at $5 input and $25 output per million tokens. OpenAI prices GPT-5.6 Sol at $5 and $30. On the sticker, Anthropic is 17% cheaper on output. Subscription tiers land within $20 of each other at every level.

    Token prices, side by side

    ModelInput / 1MOutput / 1MCached inputContext
    Claude Opus 5$5$25$0.50200K (1M available)
    Claude Sonnet 5$2$10$0.20200K
    Claude Fable 5$10$50$1200K
    GPT-5.6 Sol$5$30$0.501.05M
    GPT-5.6 Terra$2$12$0.201.05M
    GPT-5.6 Luna$0.20$1.20$0.021.05M
    Sources: Claude Platform pricing docs; OpenAI GPT-5.6 announcement (July 30, 2026) and published API rate card, August 18, 2026 snapshot.

    The tier structures mirror each other almost exactly. Sonnet 5 and Terra both cost $2 input. Anthropic has no answer to Luna’s $0.20/$1.20, which OpenAI claims beats Fable 5 on Agents’ Last Exam at an estimated cost per task nearly 99% lower.

    Subscription plans and what they actually buy

    • Claude Free — $0. Claude Code included, limits reset on a rolling five-hour window.
    • Claude Pro — $20/month, or $17/month billed annually. At least 5x Free usage per session.
    • Claude Max — from $100/month, in 5x and 20x tiers above Pro.
    • Codex Go — $8/month, web and CLI access only.
    • Codex Plus — $20/month, adds IDE and iOS.
    • Codex Pro — $100/month (5x) and $200/month (20x).

    One structural difference matters. Anthropic pools your usage: web, desktop, mobile and Claude Code all draw from the same bucket. Chat with Claude all afternoon and your coding agent gets less runway.

    OpenAI meters Codex in credits. Its published rate card charges 125 credits per million Sol input tokens and 750 per million output. At the standard $0.04 per credit, that is exactly the $5/$30 API rate — no subscription markup, no discount. OpenAI’s own guidance puts real-world spend at roughly $100 to $200 per developer per month.

    What is the real cost per solved task?

    Price per token is the wrong metric. What you pay is price per token multiplied by tokens consumed, and on that measure Codex currently wins by a wide margin. Two multipliers work against Claude: a heavier tokenizer and a chattier agent loop.

    The tokenizer tax nobody prices in

    Anthropic’s own pricing documentation carries a warning most buyers skip: Claude 4.7 and later use a newer tokenizer that produces about 30% more tokens for the same text than earlier models.

    Run the arithmetic. Opus 5 output at $25 per million, inflated 30%, is an effective $32.50 for the text volume that Sol bills at $30. Anthropic’s 17% headline discount becomes an 8% premium.

    The token-efficiency gap is the bigger number

    Independent head-to-head testing published by Morph puts the consumption gap at 3.2x to 4.2x. On a Figma plugin build, Codex burned 1,499,455 tokens against Claude Code’s 6,232,242. On a scheduler app, 72,579 against 234,772.

    Combine the two effects and the verdict is brutal. A 17% per-token discount against a 4.2x volume gap leaves Claude Code roughly 3.5x more expensive per completed task in that test set.

    That gap is a workflow choice, not a defect. Claude Code explores more, forks more subagents, and reads more of your repo. You are paying for breadth. Whether breadth is worth 3.5x depends entirely on whether your tasks are ambiguous or well specified.

    Which coding agent should you buy in 2026?

    Pick Codex if your work is well-specified and high-volume. Pick Claude Code if your work is exploratory, multi-repo, or heavy on parallel agents. Pick Luna or Sonnet 5 for anything routine — the flagship tier is wasted on boilerplate, and that is where most teams overspend.

    Your situationBuyMonthlyWhy
    Solo dev, tight budgetCodex Go$8Cheapest real CLI agent; token efficiency stretches it furthest
    Solo dev, daily driverClaude Pro (annual)$17Pooled usage across chat and code; cheapest flagship access
    High-volume refactorsCodex Plus or Pro$20–$2003–4x fewer tokens per task at near-identical benchmark scores
    Parallel agent swarmsClaude Max 20x$200Fork-by-default subagents, no published parallel-agent cap
    Large-context monorepoCodex on SolUsage-based1.05M-token context at flat pricing under the long-context threshold
    Routine boilerplateCodex on LunaUsage-based$0.20/$1.20 per million; 96% cheaper output than Sol
    Recommendations based on August 2026 published pricing and benchmark data.

    Is Claude Code worth $200 a month in 2026?

    Yes, if you run agents in parallel. Max 20x buys 20x the per-session allowance of Pro, and Claude Code’s fork-by-default subagents inherit the prompt cache, so parallelism is cheap in wall-clock terms. Codex reportedly caps developers at eight parallel agents.

    No, if you are a single developer running one session at a time. At that duty cycle you will not exhaust Pro’s $17 annual tier, and the extra $183 buys headroom you never touch.

    The August 18 addition of automatic session continuation at usage limits changes the calculus slightly. Hitting a cap now costs you a pause rather than a lost session, which makes the cheaper tiers more tolerable.

    For the model layer underneath these harnesses, our breakdowns of Muse Spark vs Claude Opus 5 and Gemini 3.7 Flash vs Claude Sonnet 5 cover the cost-per-coding-point math in detail.

    Frequently asked questions

    Can I switch from Claude Code to Codex CLI without redoing my setup?

    Yes. Codex CLI v0.147.0, released August 4, 2026, added /import, which pulls supported setup files and recent chats from both Claude Code and Cursor.

    Which has the larger context window?

    Codex. All three GPT-5.6 tiers carry a 1.05M-token window. Anthropic’s pricing page lists 200K as standard, noting that 4.5+ models include a 1M-token window at standard pricing.

    Does Claude Code cost extra on top of a Claude subscription?

    No. Claude Code is included on Free, Pro and Max, and shares the same usage pool as web, desktop and mobile. You can enable pay-as-you-go credits on paid plans once you hit a limit.

    Is the cheapest model good enough for coding?

    For routine work, often yes. OpenAI states Luna outperforms Fable 5 on Agents’ Last Exam at an estimated cost per task nearly 99% lower. On Terminal-Bench 2.1, Codex on Luna still scored 75.7%.

    Which agent tops the Terminal-Bench leaderboard right now?

    Claude Code with Fable 5, at 83.8%, leads the official harness leaderboard. On Artificial Analysis’s model-level run of the same benchmark, GPT-5.6 Sol leads at 89.5%.

    Do open-weight models beat either of these on cost?

    On price per token, easily. On benchmark parity, not yet at the top tier. We compared the leading options in GLM-5.3 vs DeepSeek V4 Pro.

    Will these prices hold?

    Assume not. The DeepSeek price increase showed how fast the floor can move. OpenAI cut Luna’s rate on July 30 and raised long-context rates at the same time.

    The bottom line

    Buy Codex if you are optimizing spend. The benchmark gap between the two is smaller than the measurement error, and Codex delivers that parity at roughly a third to a quarter of the token consumption. That is not a rounding difference — on a $200/month budget it is the gap between running out on day 20 and finishing the month.

    Buy Claude Code if you are optimizing throughput on ambiguous work. Fork-by-default subagents, cross-session mentions and an uncapped parallel-agent model make it the better orchestrator, and orchestration is where the next year of agent productivity gets won.

    What it depends on, precisely: if your average task ships with a clear spec and acceptance criteria, Codex is cheaper for identical output. If your average task starts with “figure out why this breaks,” Claude Code’s extra tokens are buying you something real.

    The move most teams should make is neither: run Luna or Sonnet 5 for 80% of tasks and reserve the flagship tier for the 20% that need it. That single routing decision saves more money than picking the right vendor.

    Sources

  • Best AI Image Generation API in 2026: What to Use After Imagen 4

    Google shut down the Imagen 4 API on August 17, 2026. The best AI image generation API for most teams is now Gemini 3.1 Flash Lite Image at $0.0336 per 1K image — 16% cheaper than the Imagen 4 Standard tier it replaces. If you need top-ranked quality, GPT Image 2 leads the Artificial Analysis arena at 1,370 Elo. Google’s own recommended migration path costs 67.5% more.

    What exactly shut down on August 17, 2026?

    Google turned off three Imagen 4 endpoints yesterday: imagen-4.0-generate-001, imagen-4.0-ultra-generate-001 and imagen-4.0-fast-generate-001. There is no grace period and no read-only fallback. Google’s own documentation tells developers to move to Gemini 3.1 Flash Image “to avoid service interruptions.”

    That is a support answer, not a procurement answer. It ignores what the replacement costs.

    Every product that called those endpoints — thumbnail pipelines, e-commerce mockups, ad variant generators — returned an error this week unless someone repointed the client.

    The prices you just lost

    Imagen 4 was cheap. Fast ran $0.02 per image, Standard $0.04, and Ultra $0.06, per Google’s Imagen documentation and the migration notices that followed it.

    Nothing in Google’s current image lineup matches the Standard tier at that price. The company retired its own price leader.

    Which is the best AI image generation API right now?

    There is no single winner. GPT Image 2 takes raw quality at 1,370 Elo. Gemini 3.1 Flash Lite Image takes value at $0.0336 per 1K image and 1,291 Elo. FLUX.2 [klein] takes the price floor, from $0.014. Your budget picks the model, not your taste.

    The Artificial Analysis Image Arena ranks models on blind user votes. Prices below come from each provider’s published rate card.

    Price and quality, side by side

    Model Provider Arena Elo Per 1K image Per 10,000 images
    GPT Image 2 (high) OpenAI 1,370 $0.165–$0.211 $1,650–$2,110
    Reve 2.1 (v2 endpoint) Reve 1,323 ~$0.20 ~$2,000
    Gemini 3.1 Flash Image Google 1,321 $0.067 $670
    Gemini 3 Pro Image Google 1,297 $0.134 $1,340
    Gemini 3.1 Flash Lite Image Google 1,291 $0.0336 $336
    GPT Image 2 (low) OpenAI not separately ranked $0.005–$0.006 $50–$60
    FLUX.2 [pro] Black Forest Labs outside arena top 10 from $0.03 from $300
    FLUX.2 [klein] 4B Black Forest Labs outside arena top 10 from $0.014 from $140
    Imagen 4 Standard Google retired Aug 17, 2026 $0.04 $400

    Read the last row against the fifth. The only Google model that undercuts the Imagen 4 tier you were paying for is Flash Lite — and it is the one Google did not recommend.

    Is Google’s recommended migration the right one?

    No. Google points Imagen 4 users at Gemini 3.1 Flash Image, which runs $0.067 per 1K image against Imagen 4 Standard’s $0.04. That is a 67.5% increase: $670 versus $400 per 10,000 images. For a team shipping 500,000 images a year, the default migration adds roughly $13,500 in annual spend.

    Google’s Gemini API pricing page bills image output by token. Flash Image costs $60 per million output tokens, and a 1K image consumes 1,120 tokens.

    The Flash Lite loophole

    Gemini 3.1 Flash Lite Image bills at $30 per million output tokens — half of Flash Image. Same 1,120 tokens for a 1K image, so $0.0336 apiece.

    It scores 1,291 Elo against Flash Image’s 1,321. You give up 30 Elo points and cut your bill by half.

    Batch mode halves it again. Google discounts batch output to $15 per million tokens, which puts a 1K image at roughly $0.0168 — about 58% below the Imagen 4 Standard price you lost.

    How much does an AI image generation API cost per 10,000 images?

    Between $50 and $2,110, depending entirely on which tier you call. That 42x spread is the single most consequential decision in an image pipeline — larger than any quality gap between the top and bottom of the leaderboard.

    Here is the real monthly math at 10,000 1K images:

    • FLUX.2 [klein] 4B: from $140 — the cheapest hosted option from a major lab
    • Gemini 3.1 Flash Lite Image, batch: ~$168
    • FLUX.2 [pro]: from $300
    • Gemini 3.1 Flash Lite Image, standard: $336
    • Imagen 4 Standard (now dead): $400
    • Gemini 3.1 Flash Image: $670
    • Gemini 3 Pro Image: $1,340
    • GPT Image 2 (high): $1,650–$2,110

    GPT Image 2’s low-quality tier at $0.005–$0.006 undercuts everything on this list. It is the right call for internal previews and A/B thumbnails where nobody zooms in.

    This is the same cost-per-unit discipline we applied to video in our breakdown of the cheapest AI video APIs before the Sora 2 sunset. Image workloads punish sloppy tier selection harder, because volume is 100x higher.

    Which is better for quality, GPT Image 2 or Nano Banana 2?

    GPT Image 2, by 49 Elo points — 1,370 versus 1,321 in the text-to-image arena. But you pay $0.165 to $0.211 per high-quality image against $0.067 for Gemini 3.1 Flash Image. That is 2.5x to 3x the price for a roughly 3.7% Elo gain.

    OpenAI released GPT Image 2 on June 24, 2026, and it has held the arena’s top slot since. Widescreen and portrait ratios price slightly below square at the medium and high tiers.

    The verdict depends on who sees the output. Customer-facing hero images justify GPT Image 2. Bulk catalog art does not.

    It is the same trade we mapped when comparing Gemini 3.7 Flash against Claude Sonnet 5 on cost per coding point. Frontier tiers sell small quality deltas at large multiples.

    What about image editing, not just generation?

    The ranking flips. Reve 2.1 leads the Artificial Analysis editing arena at 1,263 Elo, ahead of GPT Image 2 (high) and MAI-Image-2.5, tied at 1,257. Gemini 3.1 Flash Image sits fifth at 1,249.

    Reve’s pricing is bifurcated in a way most buyers miss. The v2 create and edit endpoints run 150 credits, roughly $0.20 per call.

    The fast edit endpoint runs 5 credits — about $0.007. That is 28x cheaper than the v2 endpoint from the same vendor holding the top editing score.

    FLUX.2 [pro] editing starts at $0.045 per image, a $0.015 premium over its generation price.

    Which AI image generation API should you pick for your use case?

    Match the model to who consumes the image. Bulk internal assets go to the cheapest tier that clears your bar. Customer-facing work goes to the arena leader. Editing-heavy workflows go to Reve, not to whichever model you already use for generation.

    Use case Pick Why 10,000 images
    Direct Imagen 4 replacement Gemini 3.1 Flash Lite Image Cheaper than the tier you lost, minimal code change $336
    Highest possible quality GPT Image 2 (high) 1,370 Elo, the arena leader $1,650–$2,110
    Bulk catalog / e-commerce FLUX.2 [klein] 4B Lowest floor price from a major lab from $140
    Internal previews, thumbnails GPT Image 2 (low) $0.005 per image, same API as your high tier $50–$60
    Image editing at scale Reve 2.1 fast edit Top editing Elo, ~$0.007 per edit ~$70
    Offline / overnight jobs Gemini 3.1 Flash Lite, batch 50% batch discount, no quality change ~$168
    4K marketing assets Gemini 3 Pro Image $0.24 at 4K, cheaper than GPT Image 2 high $2,400 at 4K

    Frequently asked questions

    Can I still call the Imagen 4 API?

    No. Google shut down the standard, ultra and fast endpoints on August 17, 2026. Once a model is shut down it is fully off — requests fail rather than degrade.

    What is the cheapest AI image generation API in 2026?

    GPT Image 2’s low-quality tier at $0.005–$0.006 per image is the cheapest from a top-tier lab. Among models that hold a competitive arena score, FLUX.2 [klein] 4B from $0.014 and Gemini 3.1 Flash Lite Image at $0.0336 lead.

    Is Nano Banana 2 the same as Gemini 3.1 Flash Image?

    Yes. Nano Banana 2 is the informal name for Gemini 3.1 Flash Image, listed on the arena as Gemini 3.1 Flash Image Preview. Nano Banana Pro refers to Gemini 3 Pro Image.

    Why did my Google image bill go up after migrating?

    Because Google’s recommended target, Gemini 3.1 Flash Image, costs $0.067 per 1K image versus Imagen 4 Standard’s $0.04 — a 67.5% increase. Switching to Flash Lite Image instead brings you to $0.0336.

    Does resolution change the price?

    Yes, because Gemini bills images by output token. On Flash Image, 0.5K costs $0.045, 1K costs $0.067, 2K costs $0.101 and 4K costs $0.151. Do not request 4K for assets you display at 800 pixels.

    Is GPT Image 1 still safe to build on?

    No. GPT Image 1 is scheduled for deprecation on October 23, 2026. Anything new should target GPT Image 2 directly.

    Should I self-host an open-weights image model instead?

    Only above roughly 100,000 images a month, once GPU amortization beats per-image API pricing. We ran that calculation for language models in the real cost of self-hosting Qwen3.8-Max, and the crossover logic is identical.

    The bottom line

    Migrate to Gemini 3.1 Flash Lite Image, not Gemini 3.1 Flash Image. It costs $0.0336 per 1K image against Flash Image’s $0.067 and Imagen 4 Standard’s old $0.04. You end up 16% cheaper than before the shutdown, while Google’s recommended path leaves you 67.5% more expensive.

    The 30 Elo points you surrender — 1,291 versus 1,321 — are invisible in catalog images, thumbnails and background art. That is where the volume lives.

    Reserve GPT Image 2 (high) for the images a paying customer actually looks at. At $0.165 to $0.211 each, it is a marketing line item, not an infrastructure one.

    If your workload is edit-heavy, move it to Reve 2.1’s fast edit endpoint at roughly $0.007 per call. It holds the top editing score at 1,263 Elo and costs a fraction of the v2 endpoint the same vendor sells.

    One rule beats all of this: bill by tier, not by brand. The gap between a provider’s cheapest and priciest tier is 42x. The gap between providers at the same tier is rounding error. Vendors keep proving that — as we saw when DeepSeek raised prices up to 1,100% overnight. Price your pipeline on the tier, and the brand stops mattering.

    Sources

  • Sora 2 Alternatives: Cheapest AI Video API Before the Sept 24 Sunset

    Sora 2 alternatives are now a deadline, not a preference. OpenAI kills the Sora API on September 24, 2026 — 38 days away. The cheapest replacement is Veo 3.1 Lite at $0.03 per second, video-only. For audio-native output, Veo 3.1 Lite runs $0.05. For visual quality per dollar, Kling 3.0 at $0.084 already beat Sora 2’s $0.10. Migrate now.

    OpenAI is walking away from video. That is the story buried inside a support-page sentence, and it forces every product team still calling the Sora endpoint to pick a replacement before the end of September.

    The good news for your budget: the market moved past Sora while OpenAI was deciding to leave it. The replacements are cheaper, longer, and in several cases score higher with human raters.

    Why is the Sora 2 API shutting down, and when?

    OpenAI confirmed the timeline in its own help center: the Sora web and app experiences were discontinued on April 26, 2026, and the Sora API will be discontinued on September 24, 2026. After a stated export window, OpenAI will permanently delete data associated with your Sora usage.

    What OpenAI is actually deleting

    This is not a version bump. There is no Sora 3 waiting behind it. OpenAI’s discontinuation notice offers no replacement product and instead points users to sora.chatgpt.com/sunset to export their generations.

    Unused Sora credits can be redirected toward other OpenAI services such as Codex. That is a tell. OpenAI is reallocating spend toward agents and coding, the same direction we tracked when Meta shipped Muse Spark at 4x cheaper coding economics.

    Why Sora lost

    Sora stopped appearing on public video leaderboards well before the sunset was announced. On the Artificial Analysis text-to-video arena — blind human voting — the August 2026 top three are Gemini Omni Flash at roughly 1,238–1,245 Elo, MiniMax H3 at 1,235–1,242, and ByteDance’s Seedance 2.0 at 1,220–1,225.

    Sora 2 is not in that list. It is not in the top ten. A model that is neither cheapest nor best is a model with no reason to exist.

    Which Sora 2 alternatives are cheapest per second?

    Veo 3.1 Lite is the cheapest credible option at $0.03 per second video-only and $0.05 with native audio. Kling 3.0 Standard sits at $0.084, and ByteDance’s brand-new Seedance 2.5 lists from $0.1028. Sora 2’s old $0.10 rate is now mid-pack, not competitive.

    Model Price/sec (video only) Price/sec (with audio) Notes
    Veo 3.1 Lite $0.03 $0.05 720p; cheapest audio-native option
    Runway Gen-4 Turbo $0.05 — 5 credits/sec, no native audio
    Kling 3.0 Standard $0.084 $0.126 720p; $0.112–$0.168 at 1080p
    Veo 3.1 Fast $0.10 $0.15 720p production tier
    Sora 2 (dying) $0.10 n/a 720p; $0.05 batch. Off Sept 24
    Seedance 2.5 from $0.1028 optional Released Aug 7, 2026; 30s clips
    Kling 3.0 Turbo $0.112 — $0.56 per 5-second clip
    Runway Gen-4.5 $0.12 — 12 credits/sec
    FLUX 3 Video $0.17 included 20s clips, native dialogue
    Veo 3.1 Quality $0.20 $0.40 720p/1080p flagship
    Sora 2 Pro (dying) $0.30–$0.70 n/a 720p to 1080p
    Sources: CometAPI pricing index (updated Aug 16, 2026), CostGoat Veo and Sora calculators, OpenRouter model pages, Renderful Kling pricing.

    Read that table as a verdict, not a menu. Sora 2 was charging $0.10 per second for a model that did not rank, while Google was selling audio-native video at half the price.

    Is the cheapest AI video API also the best?

    No — but the gap is smaller than the price gap. Veo 3.1 ranks around #11 overall on the Artificial Analysis arena despite being the cheapest audio-native option. Kling 3.0 1080p Pro sits at 1,107 Elo and Kling 3.0 720p at 1,099, both inside the top ten while costing under $0.13 per second.

    What the arena actually measures

    The Artificial Analysis leaderboard is blind human preference voting, not a technical benchmark. It rewards prompt adherence and perceived realism. It does not measure API reliability, rate limits, or how a model handles your specific reference images.

    Rankings also move weekly. Alibaba’s Wan2.7 sits at 1,158 Elo and Skywork’s SkyReels V4 at 1,103 — close enough that a single release reshuffles the middle of the board.

    Which Sora 2 alternative should you pick for your use case?

    Match the model to the job, not the leaderboard. Audio-synced marketing video goes to Veo 3.1. Multi-shot narrative sequences go to Kling 3.0. Image-anchored generation goes to Seedance. High-volume social clips go to Veo 3.1 Lite, where the per-second price is the whole argument.

    Your Sora 2 use case Migrate to Cost for a 10s clip Why
    High-volume social clips Veo 3.1 Lite $0.50 with audio Half of Sora 2’s rate, audio included
    Ads and audio cinematics Veo 3.1 Quality $4.00 with audio Always-on audio, 4K available
    Multi-shot storytelling Kling 3.0 $0.84–$1.26 Native support for up to 6 labeled shots
    Image-to-video fidelity Seedance 2.5 ~$1.03 Up to 50 reference assets; 30s single takes
    Long-form dialogue scenes FLUX 3 Video $1.70 20s clips with native dialogue
    Cheapest possible pipeline Veo 3.1 Lite (no audio) $0.30 $0.03/sec is the floor right now
    Batch jobs you ran overnight Veo 3.1 Lite $0.30–$0.50 Matches Sora 2’s $0.05 batch rate
    Clip costs calculated from the per-second rates in the table above.

    How much does migrating off Sora 2 actually cost?

    For most teams, migration is a price cut. At 1,000 ten-second clips per month — a modest content pipeline — Sora 2 at $0.10 per second cost $1,000. Veo 3.1 Lite with audio costs $500 for the same volume. The exceptions are batch users and Sora 2 Pro users.

    • Sora 2 standard, 1,000 clips × 10s: $1,000/month. Veo 3.1 Lite: $500. You save $6,000 a year.
    • Sora 2 batch at $0.05/sec: $500/month. Veo 3.1 Lite with audio matches it exactly at $0.05 — a wash, and you gain audio.
    • Sora 2 Pro 1080p at $0.70/sec: $7,000/month. Veo 3.1 Quality with audio at $0.40: $4,000. That is a 43% cut.
    • Kling 3.0 Standard route: $840/month, plus a separate audio step that Veo bundles for free.
    • Seedance 2.5 route: roughly $1,028/month — slightly above Sora 2, but you get 30-second single takes instead of short clips.

    The engineering cost is the real line item. Budget two to four days of developer time for endpoint changes, prompt re-tuning, and regression checks on your existing library.

    Is Veo 3.1 Quality worth 4x the price of Kling 3.0?

    Only if audio is non-negotiable. Veo 3.1 Quality with audio costs $0.40 per second against Kling 3.0 Standard’s $0.084 — a 4.8x premium. Veo is the only major model shipping native audio inside the video output. Every alternative needs a separate audio generation step.

    That separate step is not free. It adds latency, a second vendor, and a sync problem. If you are producing narrated ads, Veo’s bundled audio is worth the premium.

    If you are producing silent B-roll, product loops, or background video, paying $0.40 for audio you will mute is the single worst decision available in this market. Use Veo 3.1 Lite at $0.03 and keep the difference.

    How do you migrate off Sora 2 without breaking production?

    Export first, then swap endpoints, then re-tune prompts. The export window is the only irreversible deadline — OpenAI deletes Sora-associated data after it closes. Everything else can be fixed after September 24. Losing your generation history cannot.

    1. Export today. Pull your full Sora library from the sunset page before the window closes. This takes an hour and cannot be undone later.
    2. Inventory your prompts. Sora prompts do not transfer cleanly. Veo and Kling weight camera language and shot structure differently.
    3. Run a 20-clip bake-off. Generate the same 20 prompts on Veo 3.1 Lite, Kling 3.0, and Seedance 2.5. At these prices the whole test costs under $30.
    4. Check audio separately. If you pick Kling or Seedance, price your audio vendor into the per-second math before you commit.
    5. Keep a second provider wired. Sora’s shutdown is the argument for never having one video vendor again.

    Frequently asked questions about Sora 2 alternatives

    When exactly does the Sora 2 API stop working?

    September 24, 2026, per OpenAI’s own help center. The consumer app already shut down on April 26, 2026.

    Is there a Sora 3 coming?

    OpenAI has announced no replacement video model. Its discontinuation notice offers no successor product and suggests redirecting unused credits to services like Codex.

    What is the cheapest Sora 2 alternative?

    Veo 3.1 Lite at $0.03 per second video-only, or $0.05 with native audio. That is half of Sora 2’s $0.10 standard rate.

    Which AI video model ranks highest right now?

    Gemini Omni Flash leads the Artificial Analysis text-to-video arena at roughly 1,238–1,245 Elo, followed by MiniMax H3 and ByteDance’s Seedance 2.0 at about 1,220–1,225.

    Is Seedance 2.5 worth switching to?

    If you work from reference images. Released August 7, 2026, it accepts up to 50 image, video, and audio reference assets and generates 30-second single takes, listed from $0.1028 per second on OpenRouter.

    Do I lose my old Sora videos?

    Yes, unless you export them. OpenAI states it will permanently delete data associated with your Sora usage after the export window closes.

    Which model has native audio built in?

    Veo 3.1 across all tiers, Kling 3.0 at a $0.042 per-second premium, and FLUX 3 Video, which ships native dialogue in its 20-second clips.

    The bottom line

    Migrate to Veo 3.1 Lite. At $0.05 per second with native audio, it is half of what Sora 2 charged without audio, and it matches Sora 2’s batch rate at full standard pricing. For 90% of teams that is the answer, and the migration pays for itself in the first month.

    Choose Kling 3.0 instead if you need multi-shot narrative control and already own an audio pipeline. Choose Seedance 2.5 if your workflow is image-anchored and 30-second takes matter more than $0.02 per second.

    Choose Veo 3.1 Quality only when a client is paying for broadcast-grade audio cinematics. At $0.40 per second it is 13x the price of the Lite tier.

    The larger lesson is the one we flagged when DeepSeek raised prices up to 1,100% overnight: inference pricing is not a stable input. Sora went from flagship to deleted in sixteen months. Build your stack so the next sunset costs you a config change, not a quarter.

    Related reading: Gemini 3.7 Flash vs Claude Sonnet 5 on cost per coding point and the real cost of self-hosting open-weights models.

    Sources