Instruction following · Mistral AI

Mistral Large 3 Instruct on IFEval: score, rank and what it means

Mistral Large 3 Instruct scores 87.6 on IFEval, placing it #113 of 600 models in the index. The benchmark average is 78.6 and the leader is Claude Opus 4.5 Pro at 96.1.

Score
87.6
0–100 scale
Rank
#113
of 600
Percentile
81th
within the index
Benchmark avg
78.6
all models
Leader
96.1
Claude Opus 4.5 Pro
Gap to leader
8.5
points

Score distribution

The chart shows the top 10 models on IFEval, with Mistral Large 3 Instruct highlighted. Longer bars mean higher scores.

Claude Opus 4.5 Pro 96.1
Claude Opus 4.5 Reasoning 95.9
GPT-5.2 Pro 95.6
GPT-5.2 Max 95.2
GPT-5.2 Reasoning 95.0
GPT-5.1 Pro 94.8
Gemini 3 Pro Max 94.7
GPT-5.1 Reasoning 94.6
Claude Opus 4.5 Max 94.5
o4 Reasoning 94.5

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 Claude Opus 4.5 Pro Anthropic 200000 $20.98 89.1
2 Claude Opus 4.5 Reasoning Anthropic 2000000 $20.91 88.2
3 GPT-5.2 Pro OpenAI 2000000 $21.32 89.6
4 GPT-5.2 Max OpenAI 1000000 $21.39 89.2
5 GPT-5.2 Reasoning OpenAI 1000000 $20.98 87.5
6 GPT-5.1 Pro OpenAI 200000 $19.77 87.8
7 Gemini 3 Pro Max Google DeepMind 2000000 $20.57 87.9
8 GPT-5.1 Reasoning OpenAI 400000 $20.27 88.3
9 Claude Opus 4.5 Max Anthropic 1000000 $20.90 87.8
10 o4 Reasoning OpenAI 1000000 $19.69 87.5

What this score means

On IFEval, Mistral Large 3 Instruct sits at #113 of 600. The leader, Claude Opus 4.5 Pro, scores 96.1, so the gap to the top is 8.5 points. The median model in this index scores 81.2, which puts Mistral Large 3 Instruct above the midpoint.

Mistral AI models average 76.5 on this benchmark. Mistral Large 3 Instruct is 11.1 points above that provider line, so it is a stronger result than the average Mistral AI model.

Among multimodal models, the average is 85.2. Mistral Large 3 Instruct is 2.4 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 87.6 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 IFEval. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Nova Pro Amazon 32768 $12.75 79.4
QwQ Instruct Alibaba Cloud 65536 $4.62 79.0
Gemini 2.5 Pro Google DeepMind 200000 $15.20 81.6
MiniMax-Text Instruct MiniMax 128000 $4.59 77.7
Mistral Large 3 Instruct Mistral AI 400000 $5.31 81.1
Nova Pro Pro Amazon 200000 $13.03 79.5
Reka Core Max Reka AI 128000 $13.41 79.3

Other benchmarks for Mistral Large 3 Instruct

BenchmarkScoreRankPercentileRelative
MMLU-Pro 82.3 116 81th
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
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 IFEval?

Mistral Large 3 Instruct scores 87.6 on IFEval in this index. The benchmark average is 78.6 and the top score is 96.1.

How does Mistral Large 3 Instruct rank on IFEval?

It ranks #113 of 600 models, in the 81st percentile. The leader is Claude Opus 4.5 Pro at 96.1.

Is Mistral Large 3 Instruct good at IFEval?

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 IFEval score calculated?

IFEval is a instruction following 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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