BenchmarksEvery score sourcedPrices verified
AI benchmarks35
Leaderboards for the tests AI labs use to rank their models, each score linked to the page that published it. Where a benchmark runs its own leaderboard we take its number; otherwise the lab's own report, marked as such. Next to every score: the model's current API price, read on the vendor's page. As of November 8, 2026.
Benchmarks
35
Systems ranked
119
Published scores
672
Independent
262
Sources
27
Newest score
Nov 10
§ 01 / LLM LEADERBOARD
Top models across benchmarks
The models with scores on the most benchmarks, against the 8 benchmarks with the most models. Each cell is the model's score on that benchmark's main leaderboard; the leader of each column is highlighted. Blank means no source has published a score for that model on that test.
| Model | ARC-AGI-2 | ARC-AGI-1 | TB 4.0 | DeepSWE | ARC-AGI-3 | AutomationBench | HLE | TB 2 | Price / 1M |
|---|---|---|---|---|---|---|---|---|---|
| DeepSeek V4.1 Flash DeepSeek | 78.1% | 94.5% | 42.3% | 77% | 0.2% | 60.5% | 63.9% | 90.6% | $0.3 / $1.2 |
| GPT-6 Astra OpenAI | 95% | 98.5% | 58.2% | 74.1% | 62.7% | 41.4% | 57.2% | $10 / $50 | |
| DeepSeek V4 Pro DeepSeek | 61.3% | 90.5% | 12.4% | 62.7% | 43.2% | 60% | 87.9% | $1.32 / $3.96 | |
| DeepSeek V4 Flash DeepSeek | 61.4% | 89% | 7% | 54.4% | 37.7% | 51.5% | 82.7% | $0.44 / $1.32 | |
| Gemini 4 Argon Google DeepMind | 91.3% | 98.5% | 57.4% | 77.9% | 49.8% | 51.3% | $2 / $10 | ||
| GPT-6.1 Sol OpenAI | 94.2% | 98.5% | 58.2% | 75.2% | 52.7% | 36.1% | $2 / $10 | ||
| Claude Opus 5 Anthropic | 90.4% | 97.5% | 53.9% | 30.2% | 26.9% | 63.6% | $5 / $25 | ||
| Gemini 3.8 Flash Google DeepMind | 89.2% | 98.5% | 19.1% | 73.7% | 10.4% | 89.4% | $0.75 / $3.75 | ||
| GPT-6 Sol OpenAI | 89.6% | 95.5% | 49.4% | 68.8% | 4.6% | 33.2% | $2 / $10 | ||
| GPT-5.6 Sol OpenAI | 92.5% | 97.5% | 37.3% | 72.7% | 7.8% | 28.8% | $4 / $20 | ||
| GLM-5.2 Zhipu (Z.ai / GLM) | 22.8% | 77% | 46.2% | 26.2% | 54.7% | 81% | $1.4 / $4.4 | ||
| Gemini 3.1 Pro Preview Google | 77.1% | 98% | 12% | 0.4% | 51.4% | 73.8% | $2 / $12 | ||
| GPT-6 Luna OpenAI | 59.3% | 86.7% | 16.4% | 66.6% | 0.2% | 20.7% | $0.1 / $0.5 | ||
| GPT-5.6 Luna OpenAI | 59.6% | 90.7% | 17.3% | 62.2% | 0.2% | 17% | $0.2 / $1.2 | ||
| Claude Opus 5.5 Anthropic | 93.3% | 98.5% | 64.8% | 40% | 67.7% | $4 / $20 | |||
| Claude Fable 5.1 Anthropic | 90% | 97.5% | 57.9% | 31.4% | 65.6% | $10 / $50 | |||
| Kimi K3 Moonshot (Kimi) | 60.4% | 94.5% | 67.5% | 56% | 88.3% | $3 / $15 | |||
| GLM-5.3 Zhipu (Z.ai / GLM) | 41.8% | 66.9% | 48.2% | 62.5% | 88.2% | $1.4 / $4.4 | |||
| Claude Sonnet 5.5 Anthropic | 83.9% | 96.5% | 61.8% | 24.3% | 64.5% | $2 / $10 | |||
| Gemini 3.7 Flash Google DeepMind | 84.6% | 95.5% | 11.2% | 65.3% | 85.8% | $0.75 / $3.75 | |||
| Qwen3.8 Max Alibaba (Qwen) | 27% | 56.6% | 27.3% | 56.2% | 86.6% | $2 / $6 | |||
| Grok 4.7 SpaceXSI | 61.4% | 90.2% | 37.6% | 71% | 1.8% | $2 / $6 | |||
| Claude Haiku 5.5 Anthropic | 61.3% | 89% | 39.2% | 0.5% | 57.4% | $0.1 / $0.5 | |||
| Grok 4.6 SpaceXSI | 67.1% | 87.5% | 20.3% | 65.9% | 2.1% | $2 / $6 | |||
| Grok 4.5 SpaceXSI | 52.6% | 87.2% | 12.4% | 54% | 0.3% | $2 / $6 | |||
| MiMo-V2.6-Pro Xiaomi MiMo | 34.9% | 71.9% | 53.1% | 89.9% | $0.435 / $0.87 | ||||
| MiMo-V2.6-Flash Xiaomi MiMo | 28.8% | 67.9% | 52.3% | 87.6% | $0.14 / $0.28 | ||||
| Gemini 3.6 Flash Google | 60.4% | 91.2% | 48.6% | 78% | $0.75 / $3.75 | ||||
| Gemini 3.5 Flash Google | 72.1% | 92.5% | 37% | 76.2% | $1.5 / $9 | ||||
| GPT-5.6 Terra OpenAI | 83.9% | 96.5% | 21.5% | 0.8% | $2 / $12 |
§ 02 / CODING
Coding benchmarks
| Benchmark | Leader | Top score | Ranked | Independent | Newest score |
|---|---|---|---|---|---|
| DeepSWE DeepSWE tests coding agents on new features and behaviour changes in real open-source repositories, written as short behaviour-focused requests that usually touch several files. | Gemini 4 ArgonGoogle DeepMind | 77.9% | 27 | vendor-only | 2026-11-02 |
| FrontierCode FrontierCode judges coding agents the way an open-source maintainer would: on whether the change they submit would be merged, not only whether tests pass. | Claude Opus 5.5Anthropic | 54.4% | 16 | vendor-only | 2026-11-09 |
| NL2Repo-Bench NL2Repo-Bench tests whether a coding agent can build a complete, installable Python library from scratch, given nothing but a natural-language requirements document. | DeepSeek V4.1 FlashDeepSeek | 68.2% | 8 | vendor-only | 2026-10-19 |
| SWE-Bench Pro SWE-Bench Pro tests coding agents on long, real software tasks in professional repositories, including private and copyleft code chosen to be hard to find in training data. | Gemini 3.6 FlashGoogle | 58.7% | 8 | vendor-only | 2026-08-08 |
| FrontierSWE FrontierSWE gives AI agents ultra-long engineering and research projects, such as reimplementing git in Zig or speeding up a compiler, with up to 20 hours to work. | Claude Fable 5.2Anthropic | 69.9% | 8 | vendor-only | 2026-11-10 |
| CursorBench 4.0 CursorBench is Cursor's internal coding benchmark, built from short and often ambiguous requests taken from real Cursor sessions; version 4.0 is the current one. | Claude Opus 5.5Anthropic | 57.8% | 7 | vendor-only | 2026-09-28 |
| ProgramBench ProgramBench asks whether a model can rebuild a program from scratch, from small command-line tools up to FFmpeg, SQLite and the PHP interpreter, given only the program and its documentation. | Kimi K3.1 ProMoonshot (Kimi) | 31.5% | 7 | vendor-only | 2026-11-02 |
§ 03 / AGENTS AND COMPUTER USE
Agents and computer use benchmarks
| Benchmark | Leader | Top score | Ranked | Independent | Newest score |
|---|---|---|---|---|---|
| Terminal-Bench 4.0 Terminal-Bench 4.0 is the current Terminal-Bench release: the 3.0 task format with fixed tasks, saturated tasks removed and resources recalibrated, run on an official leaderboard. | Kimi K3.1 ProMoonshot (Kimi) | 65.7% | 34 | 23 of 46 | 2026-11-10 |
| AutomationBench AutomationBench tests whether an AI agent can carry out a business workflow across several apps through their APIs, finding the endpoints itself and following company rules. | MiniMax M3.1 FlashMiniMax | 62.8% | 25 | vendor-only | 2026-11-10 |
| Agents' Last Exam Agents' Last Exam measures whether AI agents can finish long, economically valuable professional workflows with a checkable result, across 55 sub-fields in 13 industry clusters. | GPT-6 AstraOpenAI | 59.3% | 21 | vendor-only | 2026-11-10 |
| OSWorld 2 OSWorld 2 tests computer-use agents on long, realistic desktop and web workflows that take a person about an hour and a half and an agent hundreds of actions. | Claude Opus 5.5Anthropic | 81.8% | 21 | vendor-only | 2026-11-10 |
| Terminal-Bench 2 Terminal-Bench 2 tests AI agents on hard, realistic jobs done entirely in a command-line terminal: building software, setting up machine learning, security work, data processing. | DeepSeek V4.1 FlashDeepSeek | 90.6% | 18 | vendor-only | 2026-09-22 |
| Terminal-Bench 3.0 Terminal-Bench 3.0 is a harder, longer-running set of terminal tasks than version 2, covering databases, machine learning, hardware design, formal proofs, CAD, science and business processes. | DeepSeek V4.1 FlashDeepSeek | 30% | 9 | vendor-only | 2026-09-10 |
| Toolathlon Toolathlon (the Tool Decathlon) tests language agents on long workflows across real software through MCP tools: calendars and Notion, but also WooCommerce, Kubernetes and BigQuery. | MiMo-V2.6-ProXiaomi MiMo | 76.9% | 9 | vendor-only | 2026-09-22 |
| OSWorld-Verified OSWorld-Verified tests computer-use agents on real tasks in a live Ubuntu desktop: web and desktop apps, files and multi-app workflows, controlled through screenshots, mouse and keyboard. | Kimi K3Moonshot (Kimi) | 84.8% | 8 | vendor-only | 2026-09-22 |
| BrowseComp BrowseComp tests how well a browsing agent can find a hard-to-locate fact on the web: 1,266 questions whose short answers are buried in tangled information but easy to check. | Kimi K3.1 ProMoonshot (Kimi) | 92.1% | 6 | vendor-only | 2026-11-02 |
§ 04 / REASONING
Reasoning benchmarks
| Benchmark | Leader | Top score | Ranked | Independent | Newest score |
|---|---|---|---|---|---|
| ARC-AGI-2 ARC-AGI-2 tests abstract reasoning on grid puzzles that people solve easily and AI systems find hard: infer a hidden rule from a few examples and apply it to a new grid. | GPT-6 AstraOpenAI | 95% | 105 | 105 of 109 | 2026-11-05 |
| ARC-AGI-1 ARC-AGI-1 is the original Abstraction and Reasoning Corpus: grid puzzles where a model must infer a hidden rule from a few examples and apply it to a new input. | 6 systems tiedincl. Claude Fable 5 | 98.5% | 99 | 99 of 101 | 2026-11-05 |
| ARC-AGI-3 ARC-AGI-3 is an interactive reasoning benchmark: the agent plays turn-based grid games with no instructions and has to work out the rules and the goal by exploring. | GPT-6 AstraOpenAI | 62.7% | 24 | 35 of 37 | 2026-10-17 |
§ 05 / EXPERT KNOWLEDGE
Expert knowledge benchmarks
| Benchmark | Leader | Top score | Ranked | Independent | Newest score |
|---|---|---|---|---|---|
| Humanity's Last Exam Humanity's Last Exam is a set of 2,500 expert-written questions across dozens of academic subjects, built to be hard for frontier models; each has one checkable answer. | Claude Fable 5.2Anthropic | 74.2% | 21 | vendor-only | 2026-11-09 |
| GPQA Diamond GPQA Diamond is a set of 198 graduate-level multiple-choice questions in biology, physics and chemistry, written so that skilled non-experts with web access still get them wrong. | GPT-6 AstraOpenAI | 96% | 11 | vendor-only | 2026-11-02 |
§ 06 / VISION AND DOCUMENTS
Vision and documents benchmarks
| Benchmark | Leader | Top score | Ranked | Independent | Newest score |
|---|---|---|---|---|---|
| Chartography Chartography tests whether a model can read the charts professionals use, in medicine, engineering, finance, manufacturing and science, and answer questions that need several readings and arithmetic. | Kimi K3.1 ProMoonshot (Kimi) | 90.2% | 12 | vendor-only | 2026-11-10 |
| CharXiv Reasoning CharXiv tests chart understanding on real figures from arXiv papers; the reasoning questions need a model to compare, estimate and combine information across a chart. | Kimi K3.1 ProMoonshot (Kimi) | 89% | 10 | vendor-only | 2026-11-02 |
| GDP.pdf GDP.pdf tests whether a model can answer the questions working professionals ask about real PDF documents: leases, datasheets, clinical guidelines, construction plans. | Gemini 3.8 FlashGoogle DeepMind | 35% | 7 | vendor-only | 2026-11-02 |
§ 07 / LONG CONTEXT
Long context benchmarks
| Benchmark | Leader | Top score | Ranked | Independent | Newest score |
|---|---|---|---|---|---|
| MRCR v2 MRCR v2 (multi-round coreference resolution) tests whether a model can find one specific earlier reply in a very long conversation full of near-identical ones and reproduce it exactly. | Gemini 3.7 FlashGoogle DeepMind | 97% | 7 | vendor-only | 2026-08-13 |
§ 08 / CYBERSECURITY
Cybersecurity benchmarks
| Benchmark | Leader | Top score | Ranked | Independent | Newest score |
|---|---|---|---|---|---|
| ExploitGym ExploitGym measures whether an AI agent can turn a known software vulnerability into a working exploit, across userspace programs, Google's V8 engine and the Linux kernel. | Gemini 4 CarbonGoogle DeepMind | 54.8% | 9 | vendor-only | 2026-11-10 |
| CyberGym CyberGym tests whether an AI agent can reproduce real software vulnerabilities: given a description of a historical bug and the unpatched code, write an input that triggers it. | MiMo-V2.6-FlashXiaomi MiMo | 95.1% | 8 | vendor-only | 2026-10-19 |
| SEC-bench Pro SEC-bench Pro tests whether a model can reproduce a real, disclosed vulnerability by writing a working proof-of-concept input, on the V8 and SpiderMonkey browser engines and the Linux kernel. | Gemini 4 CarbonGoogle DeepMind | 88.2% | 8 | vendor-only | 2026-11-10 |
| ExploitBench ExploitBench grades exploitation as a ladder of 16 capabilities, from reaching buggy code and crashing it up to arbitrary code execution, on real bugs in Chrome's V8 engine. | GPT-6 AstraOpenAI | 100% | 7 | vendor-only | 2026-11-02 |
§ 09 / PROFESSIONAL WORK
Professional work benchmarks
| Benchmark | Leader | Top score | Ranked | Independent | Newest score |
|---|---|---|---|---|---|
| Harvey Legal Agent Benchmark Harvey's Legal Agent Benchmark (LAB) tests whether an AI agent can produce legal work a lawyer could review, from a short instruction and the documents of a closed client matter. | Kimi K3.1 ProMoonshot (Kimi) | 23.7% | 7 | vendor-only | 2026-11-02 |
| APEX-Agents APEX-Agents (AI Productivity Index for Agents) tests whether an agent can do long, cross-application work set by investment banking analysts, management consultants and corporate lawyers. | Kimi K3.1 ProMoonshot (Kimi) | 69.4% | 6 | vendor-only | 2026-11-02 |
| JobBench JobBench tests AI agents on the work that experts in 35 occupations say they most want to hand off, packaged as workspaces of real reference files. | MiMo-V2.6-ProXiaomi MiMo | 62% | 5 | vendor-only | 2026-09-22 |
§ 10 / AI RESEARCH
AI research benchmarks
| Benchmark | Leader | Top score | Ranked | Independent | Newest score |
|---|---|---|---|---|---|
| Terminal-Bench-Science Terminal-Bench-Science tests AI agents on real scientific research workflows, contributed by domain experts, carried out in a terminal: data analysis, simulation, theorem proving, image reconstruction, model fitting. | Claude Mythos 5.2Anthropic | 75.3% | 12 | vendor-only | 2026-11-10 |
| PostTrainBench PostTrainBench tests whether an AI agent can post-train a small base language model on its own, with 10 hours on one H100 GPU, to score as high as possible on a target benchmark. | Gemini 4 ArgonGoogle DeepMind | 45.3% | 6 | vendor-only | 2026-11-02 |
| MLE-bench MLE-bench tests whether an AI agent can do machine-learning engineering end to end on 75 real Kaggle competitions: prepare data, train models and submit. | Gemini 3.6 FlashGoogle | 63.9% | 5 | vendor-only | 2026-07-21 |
Benchmarks FAQ
Answers derived from the 672 published scores in this section.
Q · 01 Which AI model is best on benchmarks right now?
+
It depends on the test. On ARC-AGI-2 GPT-6 Astra leads at 95%; On ARC-AGI-1 6 systems tie for first at 98.5%; On Terminal-Bench 4.0 Kimi K3.1 Pro leads at 65.7%; On DeepSWE Gemini 4 Argon leads at 77.9%; On ARC-AGI-3 GPT-6 Astra leads at 62.7%. Each leaderboard ranks its own test; a model that leads one often trails on another.
Q · 02 Are AI benchmark scores independently verified?
+
Some are. 262 of the 672 scores here come from a benchmark's own leaderboard (ARC-AGI-2, ARC-AGI-1, ARC-AGI-3, Terminal-Bench 4.0), where one team runs every model on the same harness. The rest are reported by the labs that make the models. Every row carries a badge saying which.
Q · 03 Why do two sources give the same model different scores?
+
Different harnesses, effort levels, subsets and dates. A lab may report its model at maximum reasoning effort on its own scaffold, while the benchmark's leaderboard runs a fixed setup for everyone. Each leaderboard here keeps the other numbers and lists them under “Where the sources disagree”.
Q · 04 Where do the prices next to each score come from?
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From our model price index: per-token rates read on each vendor's own pricing page, each with the date it was read. A score never changes the price, and a repricing moves every leaderboard on the next update.