Multimodal · Mistral AI

Codestral Chat on MMMU: score, rank and what it means

Codestral Chat scores 73.1 on MMMU, placing it #215 of 600 models in the index. The benchmark average is 67.1 and the leader is GPT-5.2 Max at 86.9.

Score
73.1
0–100 scale
Rank
#215
of 600
Percentile
64th
within the index
Benchmark avg
67.1
all models
Leader
86.9
GPT-5.2 Max
Gap to leader
13.8
points

Score distribution

The chart shows the top 10 models on MMMU, with Codestral Chat highlighted. Longer bars mean higher scores.

GPT-5.2 Max 86.9
GPT-5.1 Reasoning 86.2
GPT-5.2 85.3
Claude Sonnet 4.5 Reasoning 85.2
GPT-5.1 Max 85.1
Grok 4 Pro 84.9
Claude Opus 4.5 Reasoning 84.6
Claude Sonnet 4.5 Pro 84.5
GPT-5.2 Pro 84.4
GPT-5.2 Vision 84.4

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-5.2 OpenAI 2000000 $19.71 88.0
4 Claude Sonnet 4.5 Reasoning Anthropic 400000 $19.48 87.7
5 GPT-5.1 Max OpenAI 2000000 $20.50 87.9
6 Grok 4 Pro xAI 1000000 $19.00 85.4
7 Claude Opus 4.5 Reasoning Anthropic 2000000 $20.91 88.2
8 Claude Sonnet 4.5 Pro Anthropic 1000000 $18.73 86.7
9 GPT-5.2 Pro OpenAI 2000000 $21.32 89.6
10 GPT-5.2 Vision OpenAI 1000000 $19.03 87.4

What this score means

On MMMU, Codestral Chat sits at #215 of 600. The leader, GPT-5.2 Max, scores 86.9, so the gap to the top is 13.8 points. The median model in this index scores 70.0, which puts Codestral Chat above the midpoint.

Mistral AI models average 64.8 on this benchmark. Codestral Chat is 8.3 points above that provider line, so it is a stronger result than the average Mistral AI model.

Among multimodal models, the average is 74.3. Codestral Chat is 1.2 points below that group. This is the more useful comparison if you are shortlisting within the same tier rather than across the whole index.

Read a 73.1 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 $3.89 per million input tokens, Codestral Chat is below the median input price of $4.00 across the 600 models scored on MMMU. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
GLM-4.6 Base Zhipu AI 128000 $4.53 78.5
Hunyuan-Turbo Pro Tencent 65536 $12.00 77.6
Nova Lite Pro Amazon 200000 $9.69 74.1
Yi-Large Instruct 01.AI 32768 $3.48 73.4
Codestral Chat Mistral AI 32768 $3.89 75.8
Grok 3 Mini xAI 200000 $9.67 73.9
Hunyuan-Large Base Tencent 200000 $3.64 74.0

Other benchmarks for Codestral Chat

BenchmarkScoreRankPercentileRelative
MMLU-Pro 80.1 180 70th
GPQA Diamond 70.0 260 57th
SWE-bench Verified 62.8 256 57th
HumanEval 82.3 178 70th
MATH-500 76.8 283 53th
AIME 2025 73.9 184 69th
LiveCodeBench 67.0 248 59th
IFEval 82.0 276 54th
TruthfulQA 79.2 209 65th
HellaSwag 86.3 204 66th
Arena Elo 1389.9 250 58th

FAQ

What score does Codestral Chat get on MMMU?

Codestral Chat scores 73.1 on MMMU in this index. The benchmark average is 67.1 and the top score is 86.9.

How does Codestral Chat rank on MMMU?

It ranks #215 of 600 models, in the 64th percentile. The leader is GPT-5.2 Max at 86.9.

Is Codestral Chat good at MMMU?

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 MMMU score calculated?

MMMU is a multimodal 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 Codestral Chat cost?

Codestral Chat costs $3.89 per million input tokens and $18.19 per million output tokens.

Related