Coding · Mistral AI

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

Mixtral Base scores 62.0 on LiveCodeBench, placing it #349 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
62.0
0–100 scale
Rank
#349
of 600
Percentile
42th
within the index
Benchmark avg
61.0
all models
Leader
85.1
Claude Sonnet 4.5 Reasoning
Gap to leader
23.1
points

Score distribution

The chart shows the top 10 models on LiveCodeBench, with Mixtral Base 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 Base sits at #349 of 600. The leader, Claude Sonnet 4.5 Reasoning, scores 85.1, so the gap to the top is 23.1 points. The median model in this index scores 64.7, which puts Mixtral Base below the midpoint.

Mistral AI models average 58.1 on this benchmark. Mixtral Base is 3.9 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 Base is 2.9 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 62.0 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 $2.95 per million input tokens, Mixtral Base 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
Gemma 3 Base Google DeepMind 65536 $3.09 71.1
Claude Haiku 4.5 Mini Anthropic 16384 $8.76 71.6
Mixtral Turbo Base Together AI 32768 $2.53 69.7
Firesearch Turbo Fireworks AI 16384 $7.58 69.3
Mixtral Base Mistral AI 65536 $2.95 71.1
Reka Core Mini Reka AI 8192 $7.48 70.1
OLMo 2 Chat Allen Institute for AI 16384 $2.94 70.6

Other benchmarks for Mixtral Base

BenchmarkScoreRankPercentileRelative
MMLU-Pro 75.7 313 48th
GPQA Diamond 64.3 376 37th
SWE-bench Verified 61.4 294 51th
HumanEval 73.7 363 40th
MATH-500 76.8 285 53th
AIME 2025 66.7 319 47th
IFEval 81.2 299 50th
TruthfulQA 72.8 372 38th
HellaSwag 81.0 371 38th
MMMU 66.6 364 39th
Arena Elo 1345.3 372 38th

FAQ

What score does Mixtral Base get on LiveCodeBench?

Mixtral Base scores 62.0 on LiveCodeBench in this index. The benchmark average is 61.0 and the top score is 85.1.

How does Mixtral Base rank on LiveCodeBench?

It ranks #349 of 600 models, in the 42nd percentile. The leader is Claude Sonnet 4.5 Reasoning at 85.1.

Is Mixtral Base 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 Base cost?

Mixtral Base costs $2.95 per million input tokens and $12.22 per million output tokens.

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