Ranks compare reported scores in the same published row; ties share a rank.
AutomationBench (v1.0.6). Published as Automation-Bench v1.0.6; retain this version, separate from AutomationBench-AA and public-split reports.
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.
AutomationBench. Private-set score quoted from Zapier’s public leaderboard.
Page 5 of Google DeepMind’s model evaluation, read 1 October 2026. Gemini runs use the API at the highest thinking setting, generally pass@1 without majority voting or parallel test-time compute; smaller benchmarks average multiple trials. Other models use their highest available published reasoning results. Google quotes Vals AI for knowledge-work and Vibe Code results, Zapier’s private-set AutomationBench leaderboard, Proximal for FrontierSWE v2, Surge AI for RiemannBench and no-tools Chartography, and the CWE-bench v1 leaderboard. Argon’s DeepSWE uses mini-swe; Terminal-Bench 4.0 is self-computed for Argon and quoted from the author board for other models. PostTrainBench v1.1 uses OpenCode with one H100 and a 10-hour budget for all models. Terminal-Bench Science uses a 6x verifier timeout for Argon. LABBench 2 uses terminal, bioinformatics tools, Python, R and internet access for all models. GraphWalks uses BFS F1 on two separate subsets: 650 items up to 128k tokens and 200 items from 256k to 1M. Agent’s Last Exam uses ALE-Claw, a 5-hour window and binary pass rate, with safety filters enabled for Argon. OSWorld 2.0 uses offline partial reward, the best of three single-attempt runs for Argon, safety filters, 1080p, 500 steps, Gemini CUA with parallel batch tools and compaction, screenshot-only observation and the 08.08 patch; Claude’s combined online/offline results are omitted. LVBench runs without tools, using 1 FPS for Gemini, 800 frames for Astra, 300 for Fable and 600 for Opus. Missing cells remain blank. These are the report’s published figures, preserved separately from this site’s independent source snapshots.
storage.googleapis.com/deepmind-media/gemini/gemini_4_argon_model_evaluation.pdf
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.
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.
AutomationBench 1.0.6. Maximum effort for every column. OpenAI's own AutomationBench comparison is at medium effort instead, where GPT-6.1 Sol scores 31.7% against Claude Opus 5.5's 29.5% and GPT-6 Sol's 26.9% — the 2.2 and 4.8 points it cites — so at maximum effort Opus 5.5 is the higher figure and both readings stay visible.
OpenAI publishes this release as score-versus-cost charts at five reasoning efforts rather than as a table, and reads the top of each chart as the model's score, so every cell here is the highest figure OpenAI prints for that model and the effort it belongs to is in the row detail. OpenAI ran its own models in its research environment or through its API and quotes the Claude figures from those labs rather than re-running them, which makes the columns a comparison of publication claims rather than of runs. Three more charts on the same page are not carried here: GDP.pdf, the factuality error rate, and the four alignment charts, which are safety measurements rather than benchmark scores. Claude Opus 5.5 is charted with its production fallbacks and Claude Fable 5.1 with an Opus 5 fallback, as OpenAI labels them, and the column names are restated in the form this site uses. One number here repeats a figure the lab published earlier at a different value: OpenAI printed 72.6% for Astra on OSWorld 2.0 with its Astra table of 3 September, and the 29 September chart reads 73.5% at maximum effort, so both readings stand as published instead of one being reconciled into the other.
openai.com/index/introducing-gpt-6-1-sol/