Coding · Mistral AI

Mixtral 7B on LiveCodeBench: score, rank and what it means

Mixtral 7B scores 41.7 on LiveCodeBench, placing it #528 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
41.7
0–100 scale
Rank
#528
of 600
Percentile
12th
within the index
Benchmark avg
61.0
all models
Leader
85.1
Claude Sonnet 4.5 Reasoning
Gap to leader
43.4
points

Score distribution

The chart shows the top 10 models on LiveCodeBench, with Mixtral 7B 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 7B sits at #528 of 600. The leader, Claude Sonnet 4.5 Reasoning, scores 85.1, so the gap to the top is 43.4 points. The median model in this index scores 64.7, which puts Mixtral 7B below the midpoint.

Mistral AI models average 58.1 on this benchmark. Mixtral 7B is 16.4 points below that provider line, so it is a weaker result than the average Mistral AI model.

Among small open-weight models, the average is 41.2. Mixtral 7B is 0.5 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 41.7 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 $0.70 per million input tokens, Mixtral 7B 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
Kimi K2 1.5B Moonshot AI 4096 $0.99 56.8
Llama 4 Maverick 1.5B Meta AI 8192 $0.93 55.2
Mixtral 8B Mistral AI 8192 $0.91 55.4
Phi-4 8B Microsoft 32768 $0.96 54.9
Mixtral 7B Mistral AI 16384 $0.70 54.1
Firesearch Nano Fireworks AI 8192 $2.49 55.6
MiniMax-M2 1.5B MiniMax 16384 $0.84 54.6

Other benchmarks for Mixtral 7B

BenchmarkScoreRankPercentileRelative
MMLU-Pro 61.8 505 16th
GPQA Diamond 50.8 513 15th
SWE-bench Verified 38.7 532 11th
HumanEval 56.8 532 11th
MATH-500 51.6 545 9th
AIME 2025 42.3 530 12th
IFEval 66.5 512 15th
TruthfulQA 59.9 531 12th
HellaSwag 70.3 531 12th
MMMU 54.6 502 16th
Arena Elo 1216.6 537 11th

FAQ

What score does Mixtral 7B get on LiveCodeBench?

Mixtral 7B scores 41.7 on LiveCodeBench in this index. The benchmark average is 61.0 and the top score is 85.1.

How does Mixtral 7B rank on LiveCodeBench?

It ranks #528 of 600 models, in the 12th percentile. The leader is Claude Sonnet 4.5 Reasoning at 85.1.

Is Mixtral 7B 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 7B cost?

Mixtral 7B costs $0.70 per million input tokens and $3.13 per million output tokens.

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