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Terminal-Bench 4.0

Completing multi-step tasks in a real terminal: shell commands, builds, data wrangling and debugging, graded automatically.

  1. 1Claude Opus 5.5 · Anthropic
    66.4%
  2. 2GPT-6 Astra · OpenAI
    57.9%
  3. 3Claude Fable 5.1 · Anthropic
    55.8%
  4. 4Grok 4.7 · xAI
    38.0%
  5. 5DeepSeek-V4.1-Flash · DeepSeek
    31.2%
  6. 6Gemini 3.8 Flash · Google
    19.1%

How each maker reported it

  • Claude Opus 5.5 Table 8.1.A, p.174

    Claude results use adaptive thinking at max effort, averaged over five trials (Terminal-Bench 4.0 at xhigh). Competitor figures come from the developers' own published results.

  • GPT-6 Astra Launch post (openai.com/index/gpt-6-astra); system card §6, §10.1; GPT-6.1 Sol addendum §5, §9

    Coding and reasoning rows come from the GPT-6 Astra launch post (3 Sep 2026); OpenAI's own comparison table, settings as published there. The system card reports safety and Preparedness evaluations, not general capability benchmarks. Cyber and bio scores are at maximum reasoning effort; bio scores are refusal-adjusted. HealthBench scores are length-adjusted. GPT-6.1 Sol and some GPT-5.6 Sol values come from the GPT-6.1 Sol addendum (29 Sep 2026).

  • Claude Fable 5.1 Table 8.1.A, p.167

    Fable 5.1 and Mythos 5.1 share weights, so column 0 covers both (likewise Fable 5 / Mythos 5). Claude results use adaptive thinking at max effort, averaged over five trials. Terminal-Bench 4.0 values are from §8.6, p.171 (Mythos 5.1: 60.9%; GPT-5.6 Sol from the public leaderboard). Competitor figures come from the developers' own published results or leaderboards.

  • Grok 4.7 §2–6, p.5–16

    Grok 4.7 at xhigh effort except DeepSWE, SWE-Marathon, CADGenBench and CyberGym (high). Grok 4.6 at high except FrontierSWE, EEBench and HealthBench (xhigh). Most results come from third-party evaluators, each running its own harness; competitor results use max effort (CADGenBench GPT-5.6 Sol at xhigh). CyberGym is run without production safeguards.

  • DeepSeek-V4.1-Flash Table 3, p.33

    All models at max reasoning effort. Code-agent benchmarks use DeepSeek Harness Minimal mode with 1M context; DeepSWE uses mini-SWE; visual agent tasks use the Claude Code harness with 512K context. HLE for some models is the text-only subset. The Hugging Face card lists NL2Repo-Bench as 64.0; the report and change log say 65.4.

  • Gemini 3.8 Flash p.5

    Results as of September 2026, as published in Google's model card. Prices in the card list $0.75 / $3.75 with $1.50 / $7.50 as the regular rate. LVBench value for 3.8 Flash is the agentic setting (static: 87.1%).

Only each model's own maker's number is shown; scores a maker reports for rival models are left out.