Knowledge · Mistral AI

Mistral Large 3 Instruct on MMLU-Pro: score, rank and what it means

Mistral Large 3 Instruct scores 82.3 on MMLU-Pro, placing it #116 of 600 models in the index. The benchmark average is 73.5 and the leader is GPT-5.2 Pro at 91.6.

Score
82.3
0–100 scale
Rank
#116
of 600
Percentile
81th
within the index
Benchmark avg
73.5
all models
Leader
91.6
GPT-5.2 Pro
Gap to leader
9.3
points

Score distribution

The chart shows the top 10 models on MMLU-Pro, with Mistral Large 3 Instruct 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, Mistral Large 3 Instruct sits at #116 of 600. The leader, GPT-5.2 Pro, scores 91.6, so the gap to the top is 9.3 points. The median model in this index scores 76.1, which puts Mistral Large 3 Instruct above the midpoint.

Mistral AI models average 71.4 on this benchmark. Mistral Large 3 Instruct is 10.9 points above that provider line, so it is a stronger result than the average Mistral AI model.

Among multimodal models, the average is 80.0. Mistral Large 3 Instruct is 2.3 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 82.3 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 $5.31 per million input tokens, Mistral Large 3 Instruct is above 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
Sonar Pro Reasoning Perplexity 128000 $13.94 80.5
Claude 3.7 Sonnet Vision Anthropic 400000 $13.96 81.0
DeepSeek-V3.2 Chat DeepSeek 1000000 $5.80 82.5
Llama 3.3 Meta AI 128000 $4.56 78.2
Mistral Large 3 Instruct Mistral AI 400000 $5.31 81.1
GLM-4.6 Chat Zhipu AI 400000 $4.89 79.8
Claude 3.7 Sonnet Turbo Anthropic 200000 $15.56 82.3

Other benchmarks for Mistral Large 3 Instruct

BenchmarkScoreRankPercentileRelative
GPQA Diamond 79.8 60 90th
SWE-bench Verified 71.9 76 87th
HumanEval 84.2 129 79th
MATH-500 83.4 140 77th
AIME 2025 76.2 137 77th
LiveCodeBench 75.1 94 84th
IFEval 87.6 113 81th
TruthfulQA 85.5 60 90th
HellaSwag 89.1 110 82th
MMMU 76.5 125 79th
Arena Elo 1446.2 88 85th

FAQ

What score does Mistral Large 3 Instruct get on MMLU-Pro?

Mistral Large 3 Instruct scores 82.3 on MMLU-Pro in this index. The benchmark average is 73.5 and the top score is 91.6.

How does Mistral Large 3 Instruct rank on MMLU-Pro?

It ranks #116 of 600 models, in the 81st percentile. The leader is GPT-5.2 Pro at 91.6.

Is Mistral Large 3 Instruct good at MMLU-Pro?

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 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 Mistral Large 3 Instruct cost?

Mistral Large 3 Instruct costs $5.31 per million input tokens and $22.47 per million output tokens.

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