Knowledge · Mistral AI

Codestral 7B on MMLU-Pro: score, rank and what it means

Codestral 7B scores 60.2 on MMLU-Pro, placing it #526 of 600 models in the index. The benchmark average is 73.5 and the leader is GPT-5.2 Pro at 91.6.

Score
60.2
0–100 scale
Rank
#526
of 600
Percentile
12th
within the index
Benchmark avg
73.5
all models
Leader
91.6
GPT-5.2 Pro
Gap to leader
31.4
points

Score distribution

The chart shows the top 10 models on MMLU-Pro, with Codestral 7B highlighted. Longer bars mean higher scores.

GPT-5.2 Pro 91.6
GPT-5.1 Max 91.3
Claude Opus 4.5 Pro 91.2
Claude Opus 4.5 Reasoning 90.7
Gemini 3 Pro Reasoning 90.1
GPT-5.1 Reasoning 90.0
GPT-5.2 89.9
Claude Opus 4.5 Max 89.5
GPT-5.2 Vision 89.5
Gemini 3 Pro Max 89.5

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 GPT-5.2 Pro OpenAI 2000000 $21.32 89.6
2 GPT-5.1 Max OpenAI 2000000 $20.50 87.9
3 Claude Opus 4.5 Pro Anthropic 200000 $20.98 89.1
4 Claude Opus 4.5 Reasoning Anthropic 2000000 $20.91 88.2
5 Gemini 3 Pro Reasoning Google DeepMind 200000 $19.69 87.5
6 GPT-5.1 Reasoning OpenAI 400000 $20.27 88.3
7 GPT-5.2 OpenAI 2000000 $19.71 88.0
8 Claude Opus 4.5 Max Anthropic 1000000 $20.90 87.8
9 GPT-5.2 Vision OpenAI 1000000 $19.03 87.4
10 Gemini 3 Pro Max Google DeepMind 2000000 $20.57 87.9

What this score means

On MMLU-Pro, Codestral 7B sits at #526 of 600. The leader, GPT-5.2 Pro, scores 91.6, so the gap to the top is 31.4 points. The median model in this index scores 76.1, which puts Codestral 7B below the midpoint.

Mistral AI models average 71.4 on this benchmark. Codestral 7B is 11.2 points below that provider line, so it is a weaker result than the average Mistral AI model.

Among small open-weight models, the average is 59.8. Codestral 7B is 0.4 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 60.2 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 $1.05 per million input tokens, Codestral 7B is below the median input price of $4.00 across the 600 models scored on MMLU-Pro. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Llama 3 Replicate 8B Replicate 32768 $1.00 57.0
Nemotron 4 7B NVIDIA 16384 $1.05 56.9
Pythia Base EleutherAI 16384 $0.99 55.9
Mixtral Groq 8B Groq 32768 $0.77 54.8
Codestral 7B Mistral AI 32768 $1.05 57.0
Mixtral Turbo 7B Together AI 8192 $0.68 53.2
Llama 3 Novita 7B Novita AI 8192 $0.92 56.3

Other benchmarks for Codestral 7B

BenchmarkScoreRankPercentileRelative
GPQA Diamond 54.0 480 20th
SWE-bench Verified 44.0 478 20th
HumanEval 62.3 465 23th
MATH-500 57.0 497 17th
AIME 2025 42.8 525 13th
LiveCodeBench 48.0 465 23th
IFEval 69.0 473 21th
TruthfulQA 61.0 519 14th
HellaSwag 73.9 457 24th
MMMU 55.1 495 18th
Arena Elo 1243.9 500 17th

FAQ

What score does Codestral 7B get on MMLU-Pro?

Codestral 7B scores 60.2 on MMLU-Pro in this index. The benchmark average is 73.5 and the top score is 91.6.

How does Codestral 7B rank on MMLU-Pro?

It ranks #526 of 600 models, in the 12th percentile. The leader is GPT-5.2 Pro at 91.6.

Is Codestral 7B good at MMLU-Pro?

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 MMLU-Pro score calculated?

MMLU-Pro is a knowledge 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 7B cost?

Codestral 7B costs $1.05 per million input tokens and $5.23 per million output tokens.

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