Coding · Mistral AI

Mistral Large 3 Chat on LiveCodeBench: score, rank and what it means

Mistral Large 3 Chat scores 73.0 on LiveCodeBench, placing it #128 of 600 models in the index. The benchmark average is 61.0 and the leader is Claude Sonnet 4.5 Reasoning at 85.1.

Score
73.0
0–100 scale
Rank
#128
of 600
Percentile
79th
within the index
Benchmark avg
61.0
all models
Leader
85.1
Claude Sonnet 4.5 Reasoning
Gap to leader
12.1
points

Score distribution

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

Claude Sonnet 4.5 Reasoning 85.1
Grok 4 Reasoning 85.1
Claude Opus 4.5 85.0
GPT-5.2 Max 84.8
Grok 4 Turbo 84.7
GPT-5.1 Turbo 84.6
GPT-5.2 Pro 84.6
GPT-5.2 Reasoning 84.6
Claude Opus 4.5 Pro 84.1
Gemini 3 Pro Max 83.6

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 Claude Sonnet 4.5 Reasoning Anthropic 400000 $19.48 87.7
2 Grok 4 Reasoning xAI 2000000 $19.67 87.8
3 Claude Opus 4.5 Anthropic 400000 $19.42 87.7
4 GPT-5.2 Max OpenAI 1000000 $21.39 89.2
5 Grok 4 Turbo xAI 1000000 $19.21 86.7
6 GPT-5.1 Turbo OpenAI 2000000 $19.19 86.1
7 GPT-5.2 Pro OpenAI 2000000 $21.32 89.6
8 GPT-5.2 Reasoning OpenAI 1000000 $20.98 87.5
9 Claude Opus 4.5 Pro Anthropic 200000 $20.98 89.1
10 Gemini 3 Pro Max Google DeepMind 2000000 $20.57 87.9

What this score means

On LiveCodeBench, Mistral Large 3 Chat sits at #128 of 600. The leader, Claude Sonnet 4.5 Reasoning, scores 85.1, so the gap to the top is 12.1 points. The median model in this index scores 64.7, which puts Mistral Large 3 Chat above the midpoint.

Mistral AI models average 58.1 on this benchmark. Mistral Large 3 Chat is 14.9 points above that provider line, so it is a stronger result than the average Mistral AI model.

Among multimodal models, the average is 70.3. Mistral Large 3 Chat is 2.7 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 73.0 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 $4.90 per million input tokens, Mistral Large 3 Chat is above the median input price of $4.00 across the 600 models scored on LiveCodeBench. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Command A Max Cohere 400000 $14.28 79.9
DeepSeek-V3.2 Base DeepSeek 128000 $5.05 80.2
ERNIE 4.5 Max Baidu 128000 $14.41 80.6
GLM-4.5 Zhipu AI 200000 $4.55 78.2
Mistral Large 3 Chat Mistral AI 1000000 $4.90 79.1
Reka Core Reasoning Reka AI 32768 $13.11 78.5
Command A Turbo Cohere 32768 $13.53 79.8

Other benchmarks for Mistral Large 3 Chat

BenchmarkScoreRankPercentileRelative
MMLU-Pro 83.3 101 83th
GPQA Diamond 75.9 123 80th
SWE-bench Verified 67.1 167 72th
HumanEval 85.0 119 80th
MATH-500 81.6 175 71th
AIME 2025 74.1 178 70th
IFEval 86.6 143 76th
TruthfulQA 80.2 173 71th
HellaSwag 87.1 174 71th
MMMU 76.6 121 80th
Arena Elo 1414.2 172 71th

FAQ

What score does Mistral Large 3 Chat get on LiveCodeBench?

Mistral Large 3 Chat scores 73.0 on LiveCodeBench in this index. The benchmark average is 61.0 and the top score is 85.1.

How does Mistral Large 3 Chat rank on LiveCodeBench?

It ranks #128 of 600 models, in the 79th percentile. The leader is Claude Sonnet 4.5 Reasoning at 85.1.

Is Mistral Large 3 Chat good at LiveCodeBench?

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 LiveCodeBench score calculated?

LiveCodeBench is a coding 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 Chat cost?

Mistral Large 3 Chat costs $4.90 per million input tokens and $20.04 per million output tokens.

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