Tag: Terminal-Bench

GLM-5.3 vs DeepSeek V4 Pro comes down to one thing: you can download DeepSeek today. Its MIT-licensed weights shipped August 12, 2026, with a 1M-token context, and DeepSeek’s own API lists $0.435 per million input tokens against $0.87 output. GLM-5.3 landed two days later with stronger cyber scores, no published per-token price, and weights held back roughly two weeks.

Two of the most consequential open-weight coding models of the year shipped 48 hours apart. Both vendors published benchmark tables claiming frontier-class performance. Neither number has been independently replicated.

Here is what the money actually says.

What shipped in the GLM-5.3 vs DeepSeek V4 Pro week?

DeepSeek V4 Pro 0813 went generally available on August 12, 2026, after a preview build dated April 24. Z.ai launched GLM-5.3 on August 14. Both target agentic coding and terminal work. Only one of them can be run on your own hardware right now.

DeepSeek V4 Pro 0813: 1.6 trillion parameters, MIT license

V4 Pro is a mixture-of-experts model with 1.6 trillion total parameters and 49 billion active per token, according to TechTimes’ launch coverage. Context window is 1,048,576 tokens. The license is MIT — the most permissive terms in the frontier tier.

Artificial Analysis ranks it third out of 1,075 models evaluated on its Intelligence Index, with a score of 53, and measures output at 75.4 tokens per second with a 1.69-second time to first token.

GLM-5.3: same base model, rebuilt post-training

Z.ai did something unusual. GLM-5.3 reuses the 743-billion-parameter base model from GLM-5.2 with no new pre-training run, per MarkTechPost’s technical breakdown. Every gain came from post-training.

The gains are not small. Terminal-Bench 3.0 went from 4.6 to 28.3. DeepSWE v1.1 climbed from 46.2 to 66.9. That is a roughly 45% relative jump on agentic software engineering from post-training alone.

Which is better for coding, GLM-5.3 or DeepSeek V4 Pro?

On raw self-reported coding scores, DeepSeek V4 Pro leads. It claims 62.7 on DeepSWE and 87.9 on Terminal-Bench 2.1. GLM-5.3 reports 66.9 on DeepSWE v1.1 and 28.3 on Terminal-Bench 3.0. The benchmarks are different versions, so the comparison is not apples to apples — and that is the whole problem.

The benchmark numbers nobody has verified

DeepSeek’s self-reported 87.9 on Terminal-Bench 2.1 would place it first in the world. It does not appear on the benchmark’s own board.

The official Terminal-Bench 2.1 leaderboard is topped by Claude Code running Fable 5 at 83.8% ± 1.2%, followed by Codex with GPT-5.5 at 83.1% and Cursor CLI with Grok 4.5 at 79.3%. No DeepSeek entry exists.

TechTimes noted the same gap: V4 Pro’s scores “have not been independently replicated by any third-party evaluator as of publication.”

Independent evaluator Artificial Analysis runs its own Terminal-Bench v2.1 harness and gets different absolute numbers again: GPT-5.6 Sol at xhigh effort scores 89.5%, Claude Opus 5 at max effort 89.1%, Grok 4.6 at 88.4%.

Three sources, three scales, zero cross-comparability. Treat every vendor coding number as a marketing claim until a third party runs the harness.

Where GLM-5.3 actually wins

Security. GLM-5.3 scores 84.5% on CyberGym, up from 77.2%, ahead of Mythos 5 at 83.8% and GPT-5.6 Sol at 83.6%. DeepSeek V4 Pro self-reports 83.3%.

ExploitBench more than doubled, from 24.4% to 54.4%. Z.ai says the model surfaced 2,436 vulnerabilities across 269 open-source projects, 1,097 of them rated critical or high severity.

If your workload is vulnerability triage or defensive security automation, that is the strongest open-weight number on the board.

How much does each model cost per million tokens?

DeepSeek is cheaper — but only if you buy direct. The same model costs roughly 2.8x more input and 2.8x more output through a marketplace. GLM-5.3 has no published per-token rate at all; Z.ai’s price table still lists GLM-5.2. That pricing opacity is a real cost.

Sources: TechTimes, OpenRouter, OpenRouter GLM 5.2.

The marketplace markup is the hidden tax

Three sources quote three different prices for the identical DeepSeek model. TechTimes lists DeepSeek’s own $0.435 / $0.87. OpenRouter lists $1.218 / $2.436. Artificial Analysis measures $1.32 input and $3.96 output, with a blended rate of $0.69 per million at a 7:2:1 ratio.

On a 500-million-token month at a typical 3:1 input-output split, that spread is real money: roughly $272 buying direct against roughly $685 through OpenRouter. Same weights, same model ID, 2.5x the invoice.

DeepSeek has also signaled another increase is coming, with no timeline. Anyone budgeting off today’s rate should read our breakdown of the DeepSeek price increase that ended the AI price war.

Subscription versus per-token

Z.ai’s answer to pricing volatility is a flat plan. The GLM Coding Plan runs $18/month for Lite, $80 for Pro and $168 for Max, with weekly credit quotas of 10,000, 60,000 and 140,000 respectively.

For a solo developer hammering an agent all day, $18 flat beats metered billing on predictability alone. For an API-backed product with variable load, per-token wins on unit economics.

Can you actually download the weights?

DeepSeek yes, GLM-5.3 not yet. This is the single most decisive difference between the two models and it gets buried under benchmark tables. An open-weight model you cannot download is a closed model with a press release.

Z.ai says GLM-5.3 weights land roughly two weeks after the August 14 launch, once “safety evaluation and hardening” finish. Until then, access is API, GLM Coding Plan, or ZCode only.

That delay is defensible given the model’s exploit-generation scores. It is still a delay, and it has three concrete consequences:

• No air-gapped deployment. Regulated teams that cannot send code to a Chinese API endpoint are locked out entirely.
• No cost floor. You cannot benchmark self-hosted cost per token against the API rate, which is the entire argument for open weights.
• No fork risk protection. If Z.ai changes pricing or terms, there is no downloaded checkpoint to fall back on.

DeepSeek’s MIT license carries none of those problems. For the economics of running weights yourself, see our analysis of what self-hosting Qwen3.8-Max really costs.

Which model should you pick for your use case?

Pick DeepSeek V4 Pro for long-context work, self-hosting and cost-sensitive production. Pick GLM-5.3 for security workloads and for flat-rate interactive coding. Neither beats Claude Fable 5 or GPT-5.5 on the independently verified Terminal-Bench leaderboard, so neither is the right call if raw capability is your only constraint.

Is GLM-5.3 worth it in 2026?

Yes, but narrowly. GLM-5.3 is worth paying for if you are doing security work or want a fixed monthly bill. It is not worth waiting for if you need weights on your own GPUs this quarter, and it is not the best coder available at any price.

Z.ai calls it “the strongest open-weights coder on the market.” Its own numbers complicate that. On Terminal-Bench 3.0, GLM-5.3’s 28.3 trails Claude Fable 5 at 33.7 and GPT-5.6 Sol at 34.6. On DeepSWE v1.1, its 66.9 trails Kimi K3 at 67.5 and Fable 5 at 69.7.

The one benchmark where GLM-5.3 leads outright is Z.ai Code Bench — Z.ai’s own benchmark. It scores 31.4% at roughly 50,000 tokens per task against Claude Opus 4.8 at 29.5% using 120,000 tokens. Efficient, and self-graded.

Efficiency is the real story there. Beating a frontier closed model while spending 58% fewer tokens per task is a genuine margin advantage — if the benchmark holds up externally.

Frequently asked questions

Is DeepSeek V4 Pro open source?

The weights are MIT-licensed and available, which permits commercial use, modification and redistribution. Training data and code are not released, so it is open-weight rather than fully open-source.

When do GLM-5.3 weights release?

Z.ai said roughly two weeks after the August 14, 2026 launch, pending safety evaluation and hardening. That points to late August. No license has been confirmed.

What is the cheapest way to run DeepSeek V4 Pro?

Direct through DeepSeek’s API at $0.435 per million input tokens on a cache miss and $0.87 output. Cache hits drop input to $0.003625. Marketplace routing costs roughly 2.5x more.

Does GLM-5.3 beat Claude on coding?

No, on the numbers Z.ai published. GLM-5.3 scores 28.3 on Terminal-Bench 3.0 against Claude Fable 5’s 33.7. It leads only on Z.ai’s internal Code Bench and on CyberGym.

Why do Terminal-Bench scores differ between sources?

Because the harness and agent scaffold change the result. Claude Code with Fable 5 scores 83.8% on the official 2.1 board, while Artificial Analysis’s own v2.1 run puts GPT-5.6 Sol at 89.5%. Only compare scores measured by the same evaluator.

Which model has the bigger context window?

DeepSeek V4 Pro, at 1,048,576 tokens confirmed by both OpenRouter and Artificial Analysis. Z.ai has not disclosed GLM-5.3’s context length.

Is GLM-5.3’s cyber capability a risk?

Z.ai treated it as one, holding weights for safety hardening after ExploitBench scores doubled to 54.4%. The model reportedly found 1,097 critical or high-severity vulnerabilities across 269 open-source projects.

The bottom line

Buy DeepSeek V4 Pro. Today, direct from DeepSeek, at $0.435 in and $0.87 out.

It has the weights you can actually download under MIT, the 1M-token context you can actually verify, and a price roughly 3x below what the same model costs through a marketplace. Third place out of 1,075 models on Artificial Analysis’s Intelligence Index is enough capability for the overwhelming majority of production coding work.

Buy GLM-5.3 in exactly two situations: your workload is security-focused, where 84.5% on CyberGym is the best open-weight number published; or you want a $18-to-$168 monthly ceiling instead of metered billing that DeepSeek has already warned will rise again.

And discount both vendors’ headline coding claims. DeepSeek’s 87.9 on Terminal-Bench 2.1 would top the world leaderboard, and DeepSeek is not on that leaderboard. Until a third party runs the harness, those are sales figures, not results. For a comparison where the numbers were independently checked, see our breakdown of cost per coding point across Gemini 3.7 Flash and Claude Sonnet 5, and our look at Meta’s 4x cheaper coding model.

Sources

• Terminal-Bench 2.1 official leaderboard — tbench.ai
• DeepSeek V4 Pro 0813 intelligence and price analysis — Artificial Analysis
• Terminal-Bench v2.1 evaluation — Artificial Analysis
• DeepSeek V4 Pro 0813 API pricing — OpenRouter
• GLM 5.2 API pricing — OpenRouter
• Z.ai ships GLM-5.3 without retraining the base model — MarkTechPost
• DeepSeek V4 Pro 0813 goes GA — TechTimes
• Z.ai launches GLM-5.3 with frontier coding — Unite.AI