Coding · Mistral AI

Mixtral Chat on LiveCodeBench: score, rank and what it means

Mixtral Chat scores 64.8 on LiveCodeBench, placing it #298 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
64.8
0–100 scale
Rank
#298
of 600
Percentile
50th
within the index
Benchmark avg
61.0
all models
Leader
85.1
Claude Sonnet 4.5 Reasoning
Gap to leader
20.3
points

Score distribution

The chart shows the top 10 models on LiveCodeBench, with Mixtral 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, Mixtral Chat sits at #298 of 600. The leader, Claude Sonnet 4.5 Reasoning, scores 85.1, so the gap to the top is 20.3 points. The median model in this index scores 64.7, which puts Mixtral Chat above the midpoint.

Mistral AI models average 58.1 on this benchmark. Mixtral Chat is 6.7 points above that provider line, so it is a stronger result than the average Mistral AI model.

Among open-weight models, the average is 59.1. Mixtral Chat is 5.7 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 64.8 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 $3.30 per million input tokens, Mixtral Chat 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
DBRX Instruct Databricks 8192 $3.21 72.4
Llama 3 DeepInfra DeepInfra 128000 $3.67 74.0
Hunyuan-Turbo Tencent 128000 $10.55 75.8
Llama 3.3 Base Meta AI 32768 $3.63 74.0
Mixtral Chat Mistral AI 65536 $3.30 72.6
Nova Lite Amazon 65536 $9.20 73.4
Firefunction Max Fireworks AI 32768 $9.12 73.2

Other benchmarks for Mixtral Chat

BenchmarkScoreRankPercentileRelative
MMLU-Pro 76.1 303 50th
GPQA Diamond 70.6 247 59th
SWE-bench Verified 59.5 334 44th
HumanEval 78.6 276 54th
MATH-500 75.8 306 49th
AIME 2025 64.4 350 42th
IFEval 80.5 321 47th
TruthfulQA 73.8 360 40th
HellaSwag 83.7 295 51th
MMMU 70.4 286 52th
Arena Elo 1377.6 282 53th

FAQ

What score does Mixtral Chat get on LiveCodeBench?

Mixtral Chat scores 64.8 on LiveCodeBench in this index. The benchmark average is 61.0 and the top score is 85.1.

How does Mixtral Chat rank on LiveCodeBench?

It ranks #298 of 600 models, in the 50th percentile. The leader is Claude Sonnet 4.5 Reasoning at 85.1.

Is Mixtral 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 Mixtral Chat cost?

Mixtral Chat costs $3.30 per million input tokens and $13.04 per million output tokens.

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