Knowledge · Mistral AI

Mixtral Instruct on MMLU-Pro: score, rank and what it means

Mixtral Instruct scores 77.0 on MMLU-Pro, placing it #279 of 600 models in the index. The benchmark average is 73.5 and the leader is GPT-5.2 Pro at 91.6.

Score
77.0
0–100 scale
Rank
#279
of 600
Percentile
54th
within the index
Benchmark avg
73.5
all models
Leader
91.6
GPT-5.2 Pro
Gap to leader
14.6
points

Score distribution

The chart shows the top 10 models on MMLU-Pro, with Mixtral 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, Mixtral Instruct sits at #279 of 600. The leader, GPT-5.2 Pro, scores 91.6, so the gap to the top is 14.6 points. The median model in this index scores 76.1, which puts Mixtral Instruct above the midpoint.

Mistral AI models average 71.4 on this benchmark. Mixtral Instruct is 5.6 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. Mixtral Instruct is 3.0 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 77.0 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 $3.35 per million input tokens, Mixtral Instruct 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
Claude Haiku 4.5 Mini Anthropic 16384 $8.76 71.6
DBRX Databricks 65536 $3.29 73.3
Doubao 1.5 Pro Mini ByteDance 32768 $8.30 72.3
GPT-5.1 Nano OpenAI 32768 $9.56 74.1
Mixtral Instruct Mistral AI 65536 $3.35 72.9
Llama 3 Anyscale Instruct Anyscale 200000 $3.91 75.7
Llama 3 DeepInfra Chat DeepInfra 200000 $3.74 75.0

Other benchmarks for Mixtral Instruct

BenchmarkScoreRankPercentileRelative
GPQA Diamond 67.6 320 47th
SWE-bench Verified 61.0 299 50th
HumanEval 78.2 286 52th
MATH-500 75.6 312 48th
AIME 2025 69.0 282 53th
LiveCodeBench 66.2 271 55th
IFEval 79.8 336 44th
TruthfulQA 75.4 322 46th
HellaSwag 82.0 348 42th
MMMU 70.2 293 51th
Arena Elo 1362.7 338 44th

FAQ

What score does Mixtral Instruct get on MMLU-Pro?

Mixtral Instruct scores 77.0 on MMLU-Pro in this index. The benchmark average is 73.5 and the top score is 91.6.

How does Mixtral Instruct rank on MMLU-Pro?

It ranks #279 of 600 models, in the 54th percentile. The leader is GPT-5.2 Pro at 91.6.

Is Mixtral 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 Mixtral Instruct cost?

Mixtral Instruct costs $3.35 per million input tokens and $18.19 per million output tokens.

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