Mistral Large 3 Chat on MATH-500: score, rank and what it means
Mistral Large 3 Chat scores 81.6 on MATH-500, placing it #175 of 600 models in the index. The benchmark average is 72.5 and the leader is GPT-5.2 Max at 96.7.
Score distribution
The chart shows the top 10 models on MATH-500, with Mistral Large 3 Chat highlighted. Longer bars mean higher scores.
Top 10 leaderboard
| # | Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|---|
| 1 | GPT-5.2 Max | OpenAI | 1000000 | $21.39 | 89.2 |
| 2 | GPT-5.1 Reasoning | OpenAI | 400000 | $20.27 | 88.3 |
| 3 | GPT-5.1 Pro | OpenAI | 200000 | $19.77 | 87.8 |
| 4 | o4 Reasoning | OpenAI | 1000000 | $19.69 | 87.5 |
| 5 | Grok 4 Max | xAI | 2000000 | $19.70 | 86.6 |
| 6 | Grok 4 Reasoning | xAI | 2000000 | $19.67 | 87.8 |
| 7 | Claude Opus 4.5 Max | Anthropic | 1000000 | $20.90 | 87.8 |
| 8 | Claude Opus 4.5 | Anthropic | 400000 | $19.42 | 87.7 |
| 9 | Gemini 3 Pro Reasoning | Google DeepMind | 200000 | $19.69 | 87.5 |
| 10 | Claude Opus 4.5 Turbo | Anthropic | 2000000 | $19.57 | 85.7 |
What this score means
On MATH-500, Mistral Large 3 Chat sits at #175 of 600. The leader, GPT-5.2 Max, scores 96.7, so the gap to the top is 15.1 points. The median model in this index scores 76.0, which puts Mistral Large 3 Chat above the midpoint.
Mistral AI models average 69.1 on this benchmark. Mistral Large 3 Chat is 12.5 points above that provider line, so it is a stronger result than the average Mistral AI model.
Among multimodal models, the average is 81.3. Mistral Large 3 Chat is 0.3 points above that group. This is the more useful comparison if you are shortlisting within the same tier rather than across the whole index.
Read a 81.6 as a relative signal, not an absolute guarantee. Benchmark scores depend on prompt format, evaluation harness and contamination controls. Use this page to narrow the shortlist, then test the model on your own data.
Price context
At $4.90 per million input tokens, Mistral Large 3 Chat is above the median input price of $4.00 across the 600 models scored on MATH-500. The models immediately around it in the ranking are shown below so you can compare score and price together.
| Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|
| Kimi K1.5 Chat | Moonshot AI | 128000 | $5.11 | 80.2 |
| Magistral Instruct | Mistral AI | 65536 | $4.48 | 78.4 |
| Reka Flash Max | Reka AI | 32768 | $11.17 | 75.9 |
| DeepSeek-Coder Chat | DeepSeek | 200000 | $4.20 | 77.3 |
| Mistral Large 3 Chat | Mistral AI | 1000000 | $4.90 | 79.1 |
| Llama 3.1 Turbo Chat | Together AI | 200000 | $3.95 | 76.1 |
| Reka Core Reasoning | Reka AI | 32768 | $13.11 | 78.5 |
Other benchmarks for Mistral Large 3 Chat
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 83.3 | 101 | 83th | |
| GPQA Diamond | 75.9 | 123 | 80th | |
| SWE-bench Verified | 67.1 | 167 | 72th | |
| HumanEval | 85.0 | 119 | 80th | |
| AIME 2025 | 74.1 | 178 | 70th | |
| LiveCodeBench | 73.0 | 128 | 79th | |
| IFEval | 86.6 | 143 | 76th | |
| TruthfulQA | 80.2 | 173 | 71th | |
| HellaSwag | 87.1 | 174 | 71th | |
| MMMU | 76.6 | 121 | 80th | |
| Arena Elo | 1414.2 | 172 | 71th |
FAQ
What score does Mistral Large 3 Chat get on MATH-500?
Mistral Large 3 Chat scores 81.6 on MATH-500 in this index. The benchmark average is 72.5 and the top score is 96.7.
How does Mistral Large 3 Chat rank on MATH-500?
It ranks #175 of 600 models, in the 71st percentile. The leader is GPT-5.2 Max at 96.7.
Is Mistral Large 3 Chat good at MATH-500?
Yes, relative to the index: it is above the median and inside the top half of scores. The result should be read alongside its price and context window.
How is the MATH-500 score calculated?
MATH-500 is a mathematics benchmark. Scores in this index are modelled estimates on a 0–100 scale (or Elo for Arena), derived from capability tier and benchmark difficulty. They are not official leaderboard results.
What does Mistral Large 3 Chat cost?
Mistral Large 3 Chat costs $4.90 per million input tokens and $20.04 per million output tokens.