Coding · Mistral AI

Mixtral on LiveCodeBench: score, rank and what it means

Mixtral scores 62.5 on LiveCodeBench, placing it #342 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
62.5
0–100 scale
Rank
#342
of 600
Percentile
43th
within the index
Benchmark avg
61.0
all models
Leader
85.1
Claude Sonnet 4.5 Reasoning
Gap to leader
22.6
points

Score distribution

The chart shows the top 10 models on LiveCodeBench, with Mixtral 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, Mixtral sits at #342 of 600. The leader, Claude Sonnet 4.5 Reasoning, scores 85.1, so the gap to the top is 22.6 points. The median model in this index scores 64.7, which puts Mixtral below the midpoint.

Mistral AI models average 58.1 on this benchmark. Mixtral is 4.4 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. Mixtral is 7.8 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 62.5 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 $3.48 per million input tokens, Mixtral is below 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
Yi-Large 01.AI 32768 $3.40 73.5
Yi-Large Chat 01.AI 65536 $3.49 73.5
Phi-3.5 Chat Microsoft 16384 $2.64 69.0
Llama 3 Hyperbolic Base Hyperbolic 32768 $3.02 71.1
Mixtral Mistral AI 65536 $3.48 73.7
Qwen3-Max Nano Alibaba Cloud 65536 $8.31 71.7
Command R+ Base Cohere 65536 $3.24 71.6

Other benchmarks for Mixtral

BenchmarkScoreRankPercentileRelative
MMLU-Pro 77.3 269 55th
GPQA Diamond 71.3 234 61th
SWE-bench Verified 60.3 321 47th
HumanEval 75.9 330 45th
MATH-500 78.5 249 59th
AIME 2025 72.2 215 64th
IFEval 81.2 298 50th
TruthfulQA 76.4 297 51th
HellaSwag 82.8 326 46th
MMMU 72.7 228 62th
Arena Elo 1363.2 337 44th

FAQ

What score does Mixtral get on LiveCodeBench?

Mixtral scores 62.5 on LiveCodeBench in this index. The benchmark average is 61.0 and the top score is 85.1.

How does Mixtral rank on LiveCodeBench?

It ranks #342 of 600 models, in the 43rd percentile. The leader is Claude Sonnet 4.5 Reasoning at 85.1.

Is Mixtral good at LiveCodeBench?

It is below the median for this benchmark, so test it carefully before relying on it for this capability. 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 Mixtral cost?

Mixtral costs $3.48 per million input tokens and $12.73 per million output tokens.

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