Home / Benchmarks

Benchmarks & tests

Published scores for the models we track, side by side. Every number comes from a model card, an official announcement or a public leaderboard — click a score to see its source.

Read this first

Labs run benchmarks with different settings (reasoning effort, tool access, number of attempts), so a 2-point gap rarely means much. Use scores to shortlist, then test the models on your own tasks. Empty cells mean no published score we could verify.

Coding, agents, reasoning and knowledge tests.Language & multimodal models

ModelTerminal-Bench 4.0DeepSWE 1.1GPQA DiamondHumanity's Last ExamTerminal-Bench 2.1OSWorld 2.1GDPval-AA 2.1OSWorld 2.0
Gemini 4 Argon
Google DeepMind
57.4%77.9%——————
Muse Wonder / Muse Wonder Full
Meta (Meta Superintelligence Labs)
—78.8———72.6 / 34.3——
Claude Haiku 5.5
Anthropic
39.2%——57.4%—72.4%1620—
Mistral Large 4
Mistral AI
28.3%61.7%——————
GPT-6.1 Sol
OpenAI
—75.2%—————71.4%
Claude Sonnet 5.5
Anthropic
70.6%——64.5%—80.1%1844—
GPT-6 Luna
OpenAI
16.4%————48.9%1437—
Claude Opus 5.5
Anthropic
66.4%——67.7%—81.8%1846—
Grok 4.7
xAI (SpaceXSI)
37.6%71.0%——————
DeepSeek-V4.1-Flash
DeepSeek
—74.290.9—90.6———
GPT-6 Astra
OpenAI
57.9%——57.2%———73.5%
Gemini 3.8 Flash
Google DeepMind
—73.7%——89.4%——59.0%
Muse Spark 1.3
Meta (Meta Superintelligence Labs)
—75.4——88.8———
Claude Fable 5.1
Anthropic
55.8%——65.0%———77.9%
Qwen3.8-Flash (open weights: Qwen3.8-Flash-Next)
Alibaba (Qwen)
——91.735.9————
Qwen3.8-27B
Alibaba (Qwen)
——89.2—73.0———
Qwen3.8-Max
Alibaba (Qwen)
——92.643.686.6———
GPT-5.6 Sol
OpenAI
37.3%———————
DeepSeek-V4-Pro
DeepSeek
——90.1—87.9———
Gemma 4 (E2B, E4B, 12B, 26B A4B, 31B)
Google DeepMind
——84.3%—————
Llama 4 Maverick (and Llama 4 Scout)
Meta
——69.8—————
DeepSeek-R1
DeepSeek
——71.5—————

Blind-preference arena ratings (Elo) for generative media.Image, video & voice models

GlossaryWhat the tests measure

Terminal-Bench

Tasks the model must complete by running commands in a real shell. Measures agentic, tool-using work.

GPQA Diamond

Graduate-level multiple-choice questions in biology, physics and chemistry, written so that web search does not help. Measures expert reasoning.

Humanity's Last Exam

Very hard questions across many subjects written by domain experts. Low scores are normal; it tests the frontier.

SWE-bench

Real GitHub issues; the model must write a patch that makes the project's tests pass. Measures practical coding and debugging.

OSWorld

The model operates a real desktop computer to complete tasks. Measures computer-use agents.

GDPval

Realistic work tasks from many professions, graded against expert output. Measures economically useful knowledge work.

MMLU-Pro

Harder multiple-choice knowledge test across many academic subjects. Older MMLU is close to saturated.

LiveCodeBench

Fresh competitive-programming problems collected over time, which limits training-data contamination.

Arena Elo

Blind side-by-side comparisons where people vote for the better output (text, image, video or voice); Elo ratings move over time.