AutomationBench

How each maker reported it

• Mistral Large 4 launch post

  Preliminary, vendor-reported preview results. Many charts in the post give no numbers; only figures stated in text are listed. Blind human coding eval run by Surge AI on a 1–5 scale.

• 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.

• Claude Sonnet 5.5 Table 8.1.A, p.109

  Claude results use adaptive thinking at max effort with default sampling, averaged over five trials. Competitor figures come from the developers' own published system cards or leaderboards.

• 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.

• 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.

• Claude Opus 5 Table 8.1.A, p.148–149

  Claude Opus 5 results use adaptive thinking at max effort, averaged over five trials (ARC-AGI-3 at high effort). GPT-5.6 Sol's FrontierBench figure uses Codex and its ARC-AGI-1 figure is at xhigh. The Fable 5 HealthBench Professional figure carries a footnote reading "Mythos 5". Competitor figures come from the developers' own published results or leaderboards.