Humanity's Last Exam (no tools)

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.

• 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 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 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.

• Claude Haiku 5.5 Table 8.1.A, p.111

  Haiku 5.5 results use adaptive thinking at max effort with default sampling, averaged over five trials. FrontierCode is shown at max effort (Sonnet 5.5 scores 52.1 at xhigh). OSWorld is the offline subset; GPT-6 Luna was run by Anthropic on the same tasks. Other competitor figures come from the developers' own published results.

• Gemini 3.1 Pro p.4

  Results as of February 2026, Gemini at Thinking (High); competitors at their highest reported settings, as listed by Google.

• DeepSeek-V4-Pro model card (0813)

  Code-agent tasks use DeepSeek Harness minimal mode at max effort, temperature 1.0, top_p 0.95. DSBench rows are DeepSeek internal test sets. Competitor figures as published by DeepSeek.

• 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.

• Gemma 4 31B Benchmark results

  Instruction-tuned models, as published in Google's model card. OmniDocBench is edit distance (lower is better). Audio benchmarks are omitted because 31B has no audio input.