aiaimodel.cards
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Terminal-Bench 2.0

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

Your workloadRAG over docs · 12K in · 600 out · 50K requests/mo · 30% cached
  1. 1Claude Opus 4.7 · Anthropicbest value
    69.4%
  2. 2Claude Opus 4.6 · Anthropicbest value
    65.4%
  3. 3Claude Opus 4.5 · Anthropicbest value
    59.3%
  4. 4Claude Sonnet 4.6 · Anthropicbest value
    59.1%

“Best value”: no other model here reports a higher score at a lower cost for your workload. Scores are self-reported by each maker with different settings, so treat close calls as ties.

How each maker reported it

  • Claude Opus 4.7 Table 8.1.A, p.191–192

    Opus 4.7 uses adaptive thinking at max effort with default sampling, averaged over 5 trials (Terminal-Bench 2.0 with thinking disabled; Finance Agent at high effort). Competitor figures come from the developers' own published cards or leaderboards.

  • Claude Opus 4.6 Table 2.3.A, p.18

    Opus 4.6 results average 5 trials (SWE-bench: 25) with adaptive thinking at max effort and default sampling. MCP-Atlas is the max-effort score (62.7% at high). Gemini 3 Pro ARC-AGI-2 is Deep Thinking.

  • Claude Opus 4.5 Table 2.3.A, p.19

    Average of 5 trials with a 64k thinking budget, 200k context, default (high) effort and default sampling. SWE-bench, τ²-bench and MCP Atlas are without extended thinking; Terminal-bench uses a 128k budget (57.8% at 64k). GPT-5.1 MMMU is from the MMMU leaderboard.

  • Claude Sonnet 4.6 Table 2.1.A, p.15

    Claude Sonnet 4.6 results are averaged over 10 trials (SWE-bench over 25) with adaptive thinking at max effort and default sampling; Terminal-Bench 2.0 at default thinking. ARC-AGI-2 is 60.4% at high effort. Competitor columns as reported in the card.

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