Mathematics · Mistral AI

Mixtral Chat on MATH-500: score, rank and what it means

Mixtral Chat scores 75.8 on MATH-500, placing it #306 of 600 models in the index. The benchmark average is 72.5 and the leader is GPT-5.2 Max at 96.7.

Score
75.8
0–100 scale
Rank
#306
of 600
Percentile
49th
within the index
Benchmark avg
72.5
all models
Leader
96.7
GPT-5.2 Max
Gap to leader
20.9
points

Score distribution

The chart shows the top 10 models on MATH-500, with Mixtral Chat highlighted. Longer bars mean higher scores.

GPT-5.2 Max 96.7
GPT-5.1 Reasoning 95.9
GPT-5.1 Pro 95.1
o4 Reasoning 94.8
Grok 4 Max 94.6
Grok 4 Reasoning 94.4
Claude Opus 4.5 Max 94.2
Claude Opus 4.5 93.7
Gemini 3 Pro Reasoning 93.3
Claude Opus 4.5 Turbo 93.2

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg 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, Mixtral Chat sits at #306 of 600. The leader, GPT-5.2 Max, scores 96.7, so the gap to the top is 20.9 points. The median model in this index scores 76.0, which puts Mixtral Chat below the midpoint.

Mistral AI models average 69.1 on this benchmark. Mixtral Chat is 6.7 points above that provider line, so it is a stronger result than the average Mistral AI model.

Among open-weight models, the average is 70.8. Mixtral Chat is 5.0 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 75.8 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 $3.30 per million input tokens, Mixtral Chat is below 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.

ModelProviderContextInput / 1MAvg score
Llama 3 DeepInfra Base DeepInfra 16384 $2.95 71.8
Mixtral Turbo Instruct Together AI 32768 $3.16 71.9
Llama 3 DeepInfra DeepInfra 128000 $3.67 74.0
Llama 3 Modal Base Modal 16384 $3.21 72.2
Mixtral Chat Mistral AI 65536 $3.30 72.6
Nova Pro Mini Amazon 16384 $7.74 70.6
DBRX Instruct Databricks 8192 $3.21 72.4

Other benchmarks for Mixtral Chat

BenchmarkScoreRankPercentileRelative
MMLU-Pro 76.1 303 50th
GPQA Diamond 70.6 247 59th
SWE-bench Verified 59.5 334 44th
HumanEval 78.6 276 54th
AIME 2025 64.4 350 42th
LiveCodeBench 64.8 298 50th
IFEval 80.5 321 47th
TruthfulQA 73.8 360 40th
HellaSwag 83.7 295 51th
MMMU 70.4 286 52th
Arena Elo 1377.6 282 53th

FAQ

What score does Mixtral Chat get on MATH-500?

Mixtral Chat scores 75.8 on MATH-500 in this index. The benchmark average is 72.5 and the top score is 96.7.

How does Mixtral Chat rank on MATH-500?

It ranks #306 of 600 models, in the 49th percentile. The leader is GPT-5.2 Max at 96.7.

Is Mixtral Chat good at MATH-500?

It is below the median for this benchmark, so test it carefully before relying on it for this capability. 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 Mixtral Chat cost?

Mixtral Chat costs $3.30 per million input tokens and $13.04 per million output tokens.

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