| Rank | Row | Model | Score |
|---|
| 2 | Humanity's Last Exam (no tools) | Claude Haiku 5.5 | 45.9% |
| 3 | Humanity's Last Exam (no tools) | Claude Haiku 4.5 | 10.2% |
| 1 | Humanity's Last Exam (no tools) | Claude Sonnet 5.5 | 56.9% |
| 2 | Humanity's Last Exam (with tools) | Claude Haiku 5.5 | 57.4% |
| 3 | Humanity's Last Exam (with tools) | Claude Haiku 4.5 | 18.7% |
| 1 | Humanity's Last Exam (with tools) | Claude Sonnet 5.5 | 64.5% |
Ranks compare reported scores in the same published row; ties share a rank.
All eight results and four comparison columns from Anthropic’s announcement, read 8 October 2026. Haiku 5.5 uses max effort; Sonnet’s FrontierCode uses xhigh. HLE is split into no-tools and with-tools rows. OSWorld uses partial credit on the official 82-task offline subset, differing from earlier combined-subset reports. AA ran GDPval-AA and AA-Briefcase; these rounded quotes from Anthropic remain separate from the directly imported public AA measurements. GPT results retain the source and configuration described in the system card. Missing cells stay blank; the reported Haiku 4.5 Terminal-Bench score is zero. Each rank compares one published row only.
www.anthropic.com/claude-haiku-5-5
| Rank | Row | Model | Score |
|---|
| 2 | Humanity's Last Exam (no tools) | Claude Haiku 5.5 | 45.9% |
| 3 | Humanity's Last Exam (no tools) | Claude Haiku 4.5 | 10.2% |
| 1 | Humanity's Last Exam (no tools) | Claude Sonnet 5.5 | 56.9% |
| 2 | Humanity's Last Exam (with tools) | Claude Haiku 5.5 | 57.4% |
| 3 | Humanity's Last Exam (with tools) | Claude Haiku 4.5 | 18.7% |
| 1 | Humanity's Last Exam (with tools) | Claude Sonnet 5.5 | 64.5% |
Ranks compare reported scores in the same published row; ties share a rank.
Humanity's Last Exam (no tools). §8.8.1 and Table 8.1.A: no tools, auto thinking, 980k task budget, no compaction. Haiku 4.5 has fixed thinking budgets.
Humanity's Last Exam (with tools). §8.8.1: web search/fetch and code execution, 980k task budget, no compaction; contamination sources blocked and cheating regraded incorrect.
Section 8, pages 111–139 of the Haiku 5.5 system card, read 8 October 2026. Values come from exact prose, tables and labelled figures. Unless specified, Haiku uses adaptive thinking at max effort and five trials with evaluation-specific context up to 1M. Each row retains its subset, metric, effort and source notes; no score is assigned to an unspecified SWE-Bench Pro version. Terminal runs use safeguards without fallback and no internet egress. ProgramBench uses filtered tasks and hidden-test pass rate without a time limit. OSWorld partial and strict results use the official 82-task offline subset. CAD uses 0–1 voxel IoU. Healthcare raw and length-adjusted scores stay separate. Life-science results disable Haiku biology safeguards. Independently run figures quoted by Anthropic remain publisher reports here. Missing comparison values are not inferred from rounded charts.
www.anthropic.com/claude-haiku-5-5-system-card
Ranks compare reported scores in the same published row; ties share a rank.
HLE (w/o tools). Full-set HLE without tools. GLM-5.3 reports only the text-only subset, so its full-set cell is unreported rather than ranked here.
The complete 11-row, six-model comparison from DeepSeek’s dated MIT model card, read 17 October 2026 at revision 1bae0e1088993b9b82de08f59de5be5a34e42170. All 63 published comparison cells and both additional parenthesized HLE scores are retained as 65 figures across 12 rows. The HLE text-only scores are split into their own row and benchmark page; GLM-5.3’s dagger-marked 42.0 is text-only and has no full-set score. Unreported values stay null and do not enter ranks. K3 and Opus 5 are displayed as Kimi K3 and Claude Opus 5. Terminal-Bench 4.0 uses Claude Code, DeepSWE v1.1 uses Mini-SWE-V2, and the release notes identify ZeroBench-main as Pass@5. Other effort levels, trial counts and judges are not fully specified. These are DeepSeek publication claims, separate from independent evaluations and earlier publisher snapshots.
huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017
Ranks compare reported scores in the same published row; ties share a rank.
HLE no tools. No tools; distinct from tool-enabled HLE publisher rows.
Reflection AI’s four capability tables, read 6 October 2026. All 21 rows and eight model columns are transcribed; NR means not reported and remains blank. The source’s Qwen 3.8 Max column uses this catalogue’s Qwen3.8 Max spelling. Beam results are publisher reports; Reflection AI cites Artificial Analysis and Datacurve for comparison-model evaluations. The announcement does not specify a common harness, effort or trial count for every row. Ranks are derived within each published row from reported values only, with ties sharing a rank; they do not establish an independent or overall model ranking. AutomationBench uses the public split; AA-Omniscience is an index on the public split only, calculated by Reflection AI. BrowseComp and DeepSearchQA use context management. AA-LCR has no stated version. These settings remain separate from other source fields.
reflection.ai/blog/introducing-beam
Ranks compare reported scores in the same published row; ties share a rank.
The comparison table Z.ai publishes with GLM-5.3, read on the model card itself. Z.ai states that GLM-5.3 uses the same base model as GLM-5.2 and that every gain comes from post-training, so the GLM-5.2 column is the same architecture one training round earlier. Column names were restated in the form this site uses, and Z.ai labels its own Fable column "Fable 5 (w/ fallback)", which is recorded here as Claude Fable 5 and is the only column whose runs used a fallback model. Rows whose scale is not a percentage keep their own unit: ExploitGym is reported as tasks completed with a 2-hour and a 6-hour budget and is split into two rows so both numbers stay readable, and GDPval-AA v2 is a score rather than a percentage. The table is the only source for several benchmarks on this site, and nothing in it is an independent run.
z.ai/blog/glm-5.3
Ranks compare reported scores in the same published row; ties share a rank.
HLE (Pass@1). Daggers in the report mark the text-only subset of HLE, which is easier than the full set: GLM-5.3 and both DeepSeek V4 models are marked, and DeepSeek reports 39.1% for V4.1 Flash on that subset against 36.8% overall.
Table 3 of the V4.1 technical report, published with the model release. Every column is the vendor's own run at its maximum reasoning effort, each on that vendor's own harness, so this compares publication claims rather than one shared run. DeepSeek renamed nothing and disclosed no fallback rates; the report instead documents the opposite problem, agents searching for ways to game the harness, and says its visual-agent rows were run with a 512k-token context. The benchmark names are reproduced as DeepSeek wrote them — including rows this site keeps apart, such as HLE with and without tools — and the model columns are renamed to the names used on this site. DeepSeek's table is also republished, unchanged, on the model page at ollama.com; that copy prints 64.0 for the V4.1 row of NL2Repo-Bench where the report prints 65.4, and this site follows the report.
huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/resolve/main/DeepSeek_V41_Tech_Report.pdf
Ranks compare reported scores in the same published row; ties share a rank.
OpenAI reports the maximum score at any effort level, in its own research environment or through its API. Claude and Gemini figures are taken by OpenAI from those vendors' published reports, so this table is a comparison between publication claims, not between runs.
openai.com/index/gpt-6-astra/
Ranks compare reported scores in the same published row; ties share a rank.
Anthropic runs its own harness and states that margin size is a weakening guide to real differences at this level. Two rows rely on other publishers: AutomationBench figures come from Zapier, and the GPT figures are as reported by OpenAI. The Claude Opus 5.5 row was run with production safeguards enabled, so some security, biology and frontier-development tasks were completed by a fallback model.
www.anthropic.com/news/claude-opus-5-5
Ranks compare reported scores in the same published row; ties share a rank.
Anthropic publishes this table with Sonnet 5.5, the second model in its Claude 5.5 line, and states that Sonnet 5.5 at Max effort performs comparably to Opus 5.5 on several evaluations while Opus 5.5 remains stronger on open-ended work. The GPT-6 Sol column is quoted from OpenAI, not re-run, and Anthropic notes an OpenAI image-understanding bug that may not yet be reflected in third-party scores. Two rows come from other publishers: Artificial Analysis ran GDPval-AA and AA-Briefcase on a pre-release Sonnet 5.5 deployment that Anthropic later found had a structured-output bug, and Surge AI runs Chartography. Sonnet 5.5 launched with the cyber safeguards first built for Opus-class models, so some security work was completed by a fallback model.
www.anthropic.com/claude-sonnet-5-5