Reasoning · Mistral AI

Mixtral 1.5B on GPQA Diamond: score, rank and what it means

Mixtral 1.5B scores 45.5 on GPQA Diamond, placing it #561 of 600 models in the index. The benchmark average is 65.6 and the leader is GPT-5.2 Reasoning at 87.0.

Score
45.5
0–100 scale
Rank
#561
of 600
Percentile
7th
within the index
Benchmark avg
65.6
all models
Leader
87.0
GPT-5.2 Reasoning
Gap to leader
41.5
points

Score distribution

The chart shows the top 10 models on GPQA Diamond, with Mixtral 1.5B highlighted. Longer bars mean higher scores.

GPT-5.2 Reasoning 87.0
GPT-5.1 Max 86.4
Claude Opus 4.5 Pro 86.0
GPT-5.1 Reasoning 85.6
Claude Sonnet 4.5 Reasoning 85.2
GPT-5.2 84.9
o4 Turbo 84.8
Claude Opus 4.5 Max 84.6
Grok 4 Turbo 84.5
Claude Sonnet 4.5 Max 84.4

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 GPT-5.2 Reasoning OpenAI 1000000 $20.98 87.5
2 GPT-5.1 Max OpenAI 2000000 $20.50 87.9
3 Claude Opus 4.5 Pro Anthropic 200000 $20.98 89.1
4 GPT-5.1 Reasoning OpenAI 400000 $20.27 88.3
5 Claude Sonnet 4.5 Reasoning Anthropic 400000 $19.48 87.7
6 GPT-5.2 OpenAI 2000000 $19.71 88.0
7 o4 Turbo OpenAI 2000000 $19.34 86.6
8 Claude Opus 4.5 Max Anthropic 1000000 $20.90 87.8
9 Grok 4 Turbo xAI 1000000 $19.21 86.7
10 Claude Sonnet 4.5 Max Anthropic 1000000 $19.90 87.2

What this score means

On GPQA Diamond, Mixtral 1.5B sits at #561 of 600. The leader, GPT-5.2 Reasoning, scores 87.0, so the gap to the top is 41.5 points. The median model in this index scores 68.8, which puts Mixtral 1.5B below the midpoint.

Mistral AI models average 63.3 on this benchmark. Mixtral 1.5B is 17.8 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 49.3. Mixtral 1.5B is 3.8 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 45.5 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 $0.36 per million input tokens, Mixtral 1.5B is below the median input price of $4.00 across the 600 models scored on GPQA Diamond. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Llama 3.3 1.5B Meta AI 4096 $0.61 52.2
Llama 3.1 Cerebras 1.5B Cerebras 4096 $0.48 50.2
Nemotron 4 1.5B NVIDIA 32768 $0.49 50.8
Embed 4 Nano Cohere 8192 $1.48 49.7
Mixtral 1.5B Mistral AI 16384 $0.36 47.7
Llama 3 Replicate 1.5B Replicate 4096 $0.51 49.3
DeepSeek-Coder 1.5B DeepSeek 16384 $0.58 51.0

Other benchmarks for Mixtral 1.5B

BenchmarkScoreRankPercentileRelative
MMLU-Pro 52.3 584 3th
SWE-bench Verified 30.5 580 3th
HumanEval 51.1 566 6th
MATH-500 47.3 573 5th
AIME 2025 33.8 574 4th
LiveCodeBench 35.9 561 7th
IFEval 61.6 559 7th
TruthfulQA 54.1 577 4th
HellaSwag 64.9 585 3th
MMMU 47.6 567 6th
Arena Elo 1188.2 566 6th

FAQ

What score does Mixtral 1.5B get on GPQA Diamond?

Mixtral 1.5B scores 45.5 on GPQA Diamond in this index. The benchmark average is 65.6 and the top score is 87.0.

How does Mixtral 1.5B rank on GPQA Diamond?

It ranks #561 of 600 models, in the 7th percentile. The leader is GPT-5.2 Reasoning at 87.0.

Is Mixtral 1.5B good at GPQA Diamond?

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 GPQA Diamond score calculated?

GPQA Diamond is a reasoning 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 1.5B cost?

Mixtral 1.5B costs $0.36 per million input tokens and $1.50 per million output tokens.

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