Mathematics · Mistral AI

Mixtral Base on MATH-500: score, rank and what it means

Mixtral Base scores 76.8 on MATH-500, placing it #285 of 600 models in the index. The benchmark average is 72.5 and the leader is GPT-5.2 Max at 96.7.

Score
76.8
0–100 scale
Rank
#285
of 600
Percentile
53th
within the index
Benchmark avg
72.5
all models
Leader
96.7
GPT-5.2 Max
Gap to leader
19.9
points

Score distribution

The chart shows the top 10 models on MATH-500, with Mixtral Base highlighted. Longer bars mean higher scores.

GPT-5.2 Max 96.7
GPT-5.1 Reasoning 95.9
GPT-5.1 Pro 95.1
o4 Reasoning 94.8
Grok 4 Max 94.6
Grok 4 Reasoning 94.4
Claude Opus 4.5 Max 94.2
Claude Opus 4.5 93.7
Gemini 3 Pro Reasoning 93.3
Claude Opus 4.5 Turbo 93.2

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 GPT-5.2 Max OpenAI 1000000 $21.39 89.2
2 GPT-5.1 Reasoning OpenAI 400000 $20.27 88.3
3 GPT-5.1 Pro OpenAI 200000 $19.77 87.8
4 o4 Reasoning OpenAI 1000000 $19.69 87.5
5 Grok 4 Max xAI 2000000 $19.70 86.6
6 Grok 4 Reasoning xAI 2000000 $19.67 87.8
7 Claude Opus 4.5 Max Anthropic 1000000 $20.90 87.8
8 Claude Opus 4.5 Anthropic 400000 $19.42 87.7
9 Gemini 3 Pro Reasoning Google DeepMind 200000 $19.69 87.5
10 Claude Opus 4.5 Turbo Anthropic 2000000 $19.57 85.7

What this score means

On MATH-500, Mixtral Base sits at #285 of 600. The leader, GPT-5.2 Max, scores 96.7, so the gap to the top is 19.9 points. The median model in this index scores 76.0, which puts Mixtral Base above the midpoint.

Mistral AI models average 69.1 on this benchmark. Mixtral Base is 7.7 points above that provider line, so it is a stronger result than the average Mistral AI model.

Among open-weight models, the average is 70.8. Mixtral Base is 6.0 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 76.8 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 $2.95 per million input tokens, Mixtral Base is below the median input price of $4.00 across the 600 models scored on MATH-500. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Reka Flash Reasoning Reka AI 32768 $10.70 75.6
Yi-Lightning Base 01.AI 16384 $3.31 72.7
Codestral Chat Mistral AI 32768 $3.89 75.8
Llama 3.1 Turbo Base Together AI 16384 $3.30 73.0
Mixtral Base Mistral AI 65536 $2.95 71.1
Nemotron 4 Base NVIDIA 8192 $3.29 73.0
Command R+ Chat Cohere 32768 $4.02 76.0

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
AIME 2025 66.7 319 47th
LiveCodeBench 62.0 349 42th
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 MATH-500?

Mixtral Base scores 76.8 on MATH-500 in this index. The benchmark average is 72.5 and the top score is 96.7.

How does Mixtral Base rank on MATH-500?

It ranks #285 of 600 models, in the 53rd percentile. The leader is GPT-5.2 Max at 96.7.

Is Mixtral Base good at MATH-500?

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 MATH-500 score calculated?

MATH-500 is a mathematics 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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