Multimodal · Mistral AI

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

Codestral Instruct scores 70.0 on MMMU, placing it #302 of 600 models in the index. The benchmark average is 67.1 and the leader is GPT-5.2 Max at 86.9.

Score
70.0
0–100 scale
Rank
#302
of 600
Percentile
50th
within the index
Benchmark avg
67.1
all models
Leader
86.9
GPT-5.2 Max
Gap to leader
16.9
points

Score distribution

The chart shows the top 10 models on MMMU, with Codestral Instruct 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 Instruct sits at #302 of 600. The leader, GPT-5.2 Max, scores 86.9, so the gap to the top is 16.9 points. The median model in this index scores 70.0, which puts Codestral Instruct below the midpoint.

Mistral AI models average 64.8 on this benchmark. Codestral Instruct is 5.2 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 Instruct is 4.3 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 70.0 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 $4.01 per million input tokens, Codestral Instruct is above 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
Llama 3.1 Cerebras Cerebras 128000 $4.01 76.0
MiniMax-Text Base MiniMax 128000 $3.66 74.9
Sonar Perplexity 200000 $10.57 76.0
Auto Turbo OpenRouter 65536 $7.96 70.9
Codestral Instruct Mistral AI 65536 $4.01 75.1
Llama 3 Lambda Instruct Lambda 32768 $3.92 75.8
QwQ Base Alibaba Cloud 65536 $3.68 74.1

Other benchmarks for Codestral Instruct

BenchmarkScoreRankPercentileRelative
MMLU-Pro 78.4 228 62th
GPQA Diamond 69.2 287 52th
SWE-bench Verified 64.3 225 63th
HumanEval 82.5 171 72th
MATH-500 78.1 256 57th
AIME 2025 68.0 295 51th
LiveCodeBench 66.1 272 55th
IFEval 84.2 209 65th
TruthfulQA 78.2 248 59th
HellaSwag 87.3 161 73th
Arena Elo 1394.4 231 62th

FAQ

What score does Codestral Instruct get on MMMU?

Codestral Instruct scores 70.0 on MMMU in this index. The benchmark average is 67.1 and the top score is 86.9.

How does Codestral Instruct rank on MMMU?

It ranks #302 of 600 models, in the 50th percentile. The leader is GPT-5.2 Max at 86.9.

Is Codestral Instruct good at MMMU?

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 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 Instruct cost?

Codestral Instruct costs $4.01 per million input tokens and $16.29 per million output tokens.

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