AI leaderboard
AI Leaderboard: The Best LLMs Across Benchmarks
One index from 0 to 100, built from independent benchmarks by Artificial Analysis, Vals AI, Epoch AI, LMArena and other evaluators. The latest results were retrieved on October 19, 2026.
Overall leader
Highest point estimate in the overall index. A model is placed once its score rests on at least eight independent test series and its uncertainty is small enough.
Overall index
94.3
Win chance 76%
Standard error ± 2.4 points. 29 test series from 3 sources. Estimated without own results in: Reasoning and Knowledge.
Statistically level with: Claude Fable 5.1, Claude Sonnet 5.5, and GPT-6 Astra
5.7 points ahead of GPT-6.1 Sol.
- models in the overall index
- 91
- areas with a ranking
- 12
- new models in 45 days
- 37
Winners by area
Which model leads where
The winner is the ranked model with the highest point estimate in the area, with own results from at least two test families. Models that are level within their uncertainty are listed below it.
Coding
Win chance 81%
Statistically level with: GPT-6 Astra and GPT-6.1 Sol
3.4 points ahead of Claude Sonnet 5.5
Reasoning
Win chance 60%
Statistically level with: Gemini 4 Argon, GPT-6.1 Sol, GPT-5.6 Sol and 2 more models
10.1 points ahead of Gemini 3.7 Flash
Knowledge
Win chance 83%
Statistically level with: Claude Fable 5
1.8 points ahead of GPT-6.1 Sol
Agents
Win chance 81%
Statistically level with: Claude Fable 5.1 and Claude Opus 5.5
5.1 points ahead of Muse Wonder
General
Win chance 40%
Statistically level with: Claude Opus 5.5, GPT-6.1 Sol, Claude Sonnet 5.5 and 4 more models
16.7 points ahead of Muse Wonder Full
Finance
Win chance 46%
Statistically level with: Claude Opus 5.5, Claude Opus 5, Claude Sonnet 5.5, and Muse Wonder Full
10.0 points ahead of Claude Fable 5
Legal
Win chance 50%
Statistically level with: Muse Wonder, Claude Fable 5.1, Muse Spark 1.3 and 4 more models
11.2 points ahead of Grok 4.6
Health
Win chance 71%
Statistically level with: Claude Fable 5.1, Claude Sonnet 5.5, and Claude Opus 5.5
8.5 points ahead of Claude Fable 5
Multimodal
Win chance 69%
Statistically level with: GPT-6.1 Sol, Claude Fable 5.1, GPT-6 Astra, and Claude Opus 5
9.2 points ahead of Gemini 3.7 Flash
Education
Win chance 72%
Statistically level with: GPT-5 and Gemini 2.5 Pro
15.0 points ahead of o3
Just released
New LLMs and where they land
Language models released in the last 45 days. They join the ranking as soon as enough independent tests are published.
Meta · Released October 12, 2026
Rank 7 to 9 of 91 · Index 80.0
Meta · Released October 12, 2026
Rank 4 to 8 of 91 · Index 86.9
Anthropic · Released October 7, 2026
Rank 11 to 20 of 91 · Index 59.3
Mistral AI · Released October 6, 2026
Rank 23 to 35 of 91 · Index 32.4
OpenAI · Released September 29, 2026
Rank 4 to 8 of 91 · Index 88.6
Anthropic · Released September 28, 2026
Rank 1 to 6 of 91 · Index 91.5
Anthropic · Released September 22, 2026
Rank 1 to 4 of 91 · Index 94.3
OpenAI · Released September 22, 2026
Rank 24 to 32 of 91 · Index 38.6
OpenAI · Released September 22, 2026
Rank 10 to 15 of 91 · Index 70.4
xAI · Released September 21, 2026
Rank 16 to 22 of 91 · Index 54.6
DeepSeek · Released September 10, 2026
Rank 20 to 30 of 91 · Index 42.4
Inception · Released September 8, 2026
18 test series, but all from a single source so far
No own results in: Reasoning, Knowledge, and General
Show 25 more new models without independent benchmarks
Technology Innovation Institute · Released October 6, 2026
No independent benchmarks yet. Vendor results in the profile
Reflection AI · Released October 5, 2026
No independent benchmarks yet. Vendor results in the profile
Aleph Alpha · Released October 3, 2026
No independent benchmarks yet. Vendor results in the profile
Bilibili Index Team · Released September 30, 2026
No independent benchmarks yet. Vendor results in the profile
Bilibili Index Team · Released September 30, 2026
No independent benchmarks yet. Vendor results in the profile
Bilibili Index Team · Released September 30, 2026
No independent benchmarks yet. Vendor results in the profile
Inception · Released September 29, 2026
No independent benchmarks yet. Vendor results in the profile
H Company · Released September 28, 2026
No independent benchmarks yet. Vendor results in the profile
H Company · Released September 28, 2026
No independent benchmarks yet. Vendor results in the profile
H Company · Released September 28, 2026
No independent benchmarks yet. Vendor results in the profile
Aion Labs · Released September 23, 2026
No independent benchmarks yet. Vendor results in the profile
Aion Labs · Released September 23, 2026
No independent benchmarks yet. Vendor results in the profile
Fireworks Research · Released September 23, 2026
No independent benchmarks yet. Vendor results in the profile
Xiaomi · Released September 22, 2026
No independent benchmarks yet. Vendor results in the profile
Xiaomi · Released September 22, 2026
No independent benchmarks yet. Vendor results in the profile
Xiaomi · Released September 22, 2026
No independent benchmarks yet. Vendor results in the profile
Xiaomi · Released September 22, 2026
No independent benchmarks yet. Open the profile
Yandex · Released September 21, 2026
No independent benchmarks yet. Vendor results in the profile
Alibaba · Released September 17, 2026
No independent benchmarks yet. Vendor results in the profile
PrismML · Released September 17, 2026
No independent benchmarks yet. Vendor results in the profile
Sakana AI · Released September 11, 2026
No independent benchmarks yet. Vendor results in the profile
Sakana AI · Released September 11, 2026
No independent benchmarks yet. Vendor results in the profile
Moonshot AI · Released September 11, 2026
No independent benchmarks yet. Open the profile
Cohere · Released September 10, 2026
No independent benchmarks yet. Vendor results in the profile
Cognition · Released September 10, 2026
No independent benchmarks yet. Vendor results in the profile
Ranking
Every model in the index
Pick an area or search by model and provider. Ranks always refer to the full list, even while you search.
± 2.4 points29 test series3 sourcesestimated without own results in: Reasoning and Knowledge
± 2.0 points37 test series3 sources
± 2.8 points26 test series3 sourcesestimated without own results in: Reasoning and Knowledge
± 2.0 points45 test series4 sources
± 1.9 points42 test series4 sources
± 2.2 points52 test series4 sources
± 2.0 points30 test series2 sources
± 3.4 points47 test series5 sources
± 3.5 points29 test series4 sources
± 2.7 points51 test series4 sources
± 3.5 points46 test series4 sources
± 3.6 points42 test series4 sources
± 3.0 points54 test series4 sources
± 5.1 points30 test series3 sources
± 2.9 points51 test series4 sources
± 5.8 points38 test series3 sources
± 4.2 points49 test series4 sources
± 6.6 points47 test series5 sources
± 4.4 points45 test series4 sources
± 6.3 points38 test series4 sourcesestimated without own results in: Mathematics
20 of 96 models shown
Methodology
How the leaderboard works
Only reviewed, active results from independent evaluators count. Numbers that vendors publish in their own model cards stay out. Within each test series the values are standardized, which puts accuracy rates, Elo ratings and pass rates on one shared scale. Percentages and rates are stretched first, so gaps near 0 and 100% are not compressed.
A one-factor statistical model then estimates one shared strength per model and accounts for how sharply each series separates the models. Area scores move away from that strength only as far as the model's own results in the area support. Where a model has no own results in a core area, its overall strength stands in. The note “estimated without own results in …” makes this visible.
The overall score weights core areas such as coding, reasoning, math and knowledge at 75% and the specialist domains finance, law and health at 25%. A model receives an overall score once it has eight test series from at least two sources and three areas.
The uncertainty comes from 200 recalculations in which the test families are drawn again at random. A model is placed once its uncertainty is small enough. A rank range such as “rank 1 to 3” covers the places the model takes in 90% of these recalculations. The winner is the ranked model with the highest point estimate. The win chance is the share of 200 recalculations with randomly redrawn test families in which the model comes first. A recalculation without the model counts as no win for it. A model is statistically level with the winner when it is ahead of the winner in at least 5% of the recalculations that include both.
The index runs from 0 to 100 and is not a percentage. A score of 80 does not mean a model solves 80% of all tasks. It describes the position relative to a fixed reference group of 29 language models whose average sits at 50.
The full calculation with every weight and raw result is available as a JSON export. For sources, test settings and pricing of a single model, browse the AI model directory.