Coding · Mistral AI

Codestral Instruct on LiveCodeBench: score, rank and what it means

Codestral Instruct scores 66.1 on LiveCodeBench, placing it #272 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
66.1
0–100 scale
Rank
#272
of 600
Percentile
55th
within the index
Benchmark avg
61.0
all models
Leader
85.1
Claude Sonnet 4.5 Reasoning
Gap to leader
19.0
points

Score distribution

The chart shows the top 10 models on LiveCodeBench, with Codestral Instruct 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, Codestral Instruct sits at #272 of 600. The leader, Claude Sonnet 4.5 Reasoning, scores 85.1, so the gap to the top is 19.0 points. The median model in this index scores 64.7, which puts Codestral Instruct above the midpoint.

Mistral AI models average 58.1 on this benchmark. Codestral Instruct is 8.0 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. Codestral Instruct is 4.2 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 66.1 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 $4.01 per million input tokens, Codestral Instruct 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
Llama 3 Anyscale Chat Anyscale 65536 $3.67 74.6
MiniMax-M2 Base MiniMax 65536 $4.20 76.4
Mixtral Groq Instruct Groq 16384 $3.25 72.5
Mixtral Instruct Mistral AI 65536 $3.35 72.9
Codestral Instruct Mistral AI 65536 $4.01 75.1
Llama 3.1 Cerebras Cerebras 128000 $4.01 76.0
Firefunction Pro Fireworks AI 16384 $9.05 72.9

Other benchmarks for Codestral Instruct

BenchmarkScoreRankPercentileRelative
MMLU-Pro 78.4 228 62th
GPQA Diamond 69.2 287 52th
SWE-bench Verified 64.3 225 63th
HumanEval 82.5 171 72th
MATH-500 78.1 256 57th
AIME 2025 68.0 295 51th
IFEval 84.2 209 65th
TruthfulQA 78.2 248 59th
HellaSwag 87.3 161 73th
MMMU 70.0 302 50th
Arena Elo 1394.4 231 62th

FAQ

What score does Codestral Instruct get on LiveCodeBench?

Codestral Instruct scores 66.1 on LiveCodeBench in this index. The benchmark average is 61.0 and the top score is 85.1.

How does Codestral Instruct rank on LiveCodeBench?

It ranks #272 of 600 models, in the 55th percentile. The leader is Claude Sonnet 4.5 Reasoning at 85.1.

Is Codestral Instruct 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 Codestral Instruct cost?

Codestral Instruct costs $4.01 per million input tokens and $16.29 per million output tokens.

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