Classement IA
Quel modèle est en tête, au global et par catégorie. Ce sont les benchmarks publics à partir desquels sont construits les graphiques que vous voyez sur YouTube et X. Nous ne publions aucun score propre : chaque chiffre appartient au benchmark qui l'a produit, capturé à une date et crédité à sa source.
Real people are shown the same prompt answered by two anonymous models side by side and vote for the better answer. Millions of these blind head-to-head votes are fed into a Bradley-Terry statistical model (the successor to the Elo system it started with) which converts win/loss pairs into a single rating per model. A higher rating means people picked that model more often against strong opposition. This snapshot uses the 'style control' variant, which is the site's default: it statistically adjusts for answer length and formatting so a model cannot climb simply by writing longer, prettier replies.
Limitations: It measures which answer people LIKE, not which answer is CORRECT - a confident, well-written wrong answer can still win a vote. Voters are self-selected volunteers rather than a representative sample, prompts skew toward what that crowd chooses to type, and models with few votes have wide confidence intervals (ci_low/ci_high) that often overlap the models ranked above and below them. Treat small rank gaps as ties.
Source: LMArena (formerly LMSYS Chatbot Arena) ↗ · publié 2026-08-11 · données sous-jacentes ↗ · Dataset released under Creative Commons Attribution 4.0 (CC BY 4.0). Reuse is permitted with attribution - credit LMArena and link to the leaderboard.
| # | Modèle | Organisation | Arena score (human preference) |
|---|---|---|---|
| 1 | claude-fable-5 | Anthropic | 1506 95% CI 1501 to 1512 21,304 votes |
| 2 | claude-opus-4-6-thinking | Anthropic | 1505 95% CI 1501 to 1508 72,425 votes |
| 3 | claude-opus-4-7-thinking | Anthropic | 1502 95% CI 1498 to 1506 60,222 votes |
| 4 | muse-spark-1.2 (xHigh) | Meta | 1498 95% CI 1488 to 1509 3,278 votes |
| 5 | claude-opus-4-6 | Anthropic | 1498 95% CI 1494 to 1501 76,386 votes |
| 6 | claude-opus-5-high | Anthropic | 1494 95% CI 1489 to 1499 19,498 votes |
| 7 | claude-opus-4-7 | Anthropic | 1494 95% CI 1490 to 1498 61,308 votes |
| 8 | claude-opus-5-max | Anthropic | 1490 95% CI 1483 to 1497 9,419 votes |
| 9 | qwen3.8-max | Alibaba | 1490 95% CI 1482 to 1498 6,789 votes |
| 10 | muse-spark-1.1 | Meta | 1489 95% CI 1483 to 1494 16,648 votes |
| 11 | muse-spark | Meta | 1488 95% CI 1482 to 1494 13,600 votes |
| 12 | kimi-k3-max | Moonshot AI | 1487 95% CI 1481 to 1493 11,762 votes |
| 13 | gemini-3.1-pro-preview | 1486 95% CI 1483 to 1490 94,814 votes | |
| 14 | gemini-3-pro | 1486 95% CI 1482 to 1489 41,509 votes | |
| 15 | gemini-3.6-flash | 1484 95% CI 1478 to 1490 13,559 votes | |
| 16 | gpt-5.5-high | OpenAI | 1482 95% CI 1477 to 1486 55,210 votes |
| 17 | claude-opus-4-8-thinking | Anthropic | 1481 95% CI 1477 to 1486 40,409 votes |
| 18 | gpt-5.6-sol-xhigh | OpenAI | 1481 95% CI 1475 to 1487 15,304 votes |
| 19 | gemini-3.5-flash-high | 1477 95% CI 1473 to 1482 25,613 votes | |
| 20 | gpt-5.5 | OpenAI | 1477 95% CI 1473 to 1481 56,513 votes |
Les barres sont mises à l'échelle sur la plage visible afin que les petites différences restent lisibles. Elles ne partent pas de zéro. Lorsque les intervalles de confiance se chevauchent, les modèles sont statistiquement à égalité : lisez le groupe de tête comme un groupe, et non dans un ordre strict.
Comment lire ces données, et pourquoi elles divergent
LMArena (formerly LMSYS Chatbot Arena)
Real people are shown the same prompt answered by two anonymous models side by side and vote for the better answer. Millions of these blind head-to-head votes are fed into a Bradley-Terry statistical model (the successor to the Elo system it started with) which converts win/loss pairs into a single rating per model. A higher rating means people picked that model more often against strong opposition. This snapshot uses the 'style control' variant, which is the site's default: it statistically adjusts for answer length and formatting so a model cannot climb simply by writing longer, prettier replies.
Méfiez-vous de: It measures which answer people LIKE, not which answer is CORRECT - a confident, well-written wrong answer can still win a vote. Voters are self-selected volunteers rather than a representative sample, prompts skew toward what that crowd chooses to type, and models with few votes have wide confidence intervals (ci_low/ci_high) that often overlap the models ranked above and below them. Treat small rank gaps as ties.
Publié 2026-08-11 · classement en direct ↗ · Data: LMArena leaderboard dataset (CC BY 4.0).
LiveBench
A fixed set of test questions with objectively verifiable answers is run against each model and scored automatically against ground truth - no human voting and no AI judge, so the score is repeatable. This release spans 23 tasks grouped into 7 categories. Each category score is the average of its tasks, and the headline 'global average' is the average of the 7 category scores, so every category counts equally regardless of how many tasks it contains. Scores are percentages: 100 is perfect.
Méfiez-vous de: Contamination-LIMITED, not contamination-proof: questions are refreshed from recent sources to reduce the chance a model simply memorised them during training, but that cannot be guaranteed. Scores reflect only these 23 tasks - they say nothing about tone, safety, speed or cost. Many entries are effort/thinking variants of the same underlying model (model_id shows the exact configuration tested), and a variant given more reasoning budget will usually outscore the cheaper default that most people actually use.
Publié · classement en direct ↗ · Data: LiveBench 2026-06-25 release, livebench.ai.
Deux unités différentes, jamais un seul graphique
Une note de préférence humaine et un score de réponses correctes ne peuvent pas partager le même axe, cette page ne les mélange donc jamais dans un seul tableau. Un modèle peut dominer l'un sans dominer l'autre, et c'est un signal réel sur ses points forts, non une contradiction : l'un demande « quelle réponse les gens ont-ils préférée ? », l'autre demande « quelle réponse était juste ? ».
Ce que cette page ne fera pas
Il ne vous dira pas quelle IA utiliser. Le leader des benchmarks n'est souvent pas le bon outil pour votre travail ; le prix, la disponibilité, les intégrations, la longueur du contexte et la façon dont il suit VOS instructions comptent généralement plus qu'un point ou deux d'écart. Quel AI devrais-je utiliser ? →