Knowledge · OpenAI

o3 Nano on MMLU-Pro: score, rank and what it means

o3 Nano scores 75.3 on MMLU-Pro, placing it #325 of 600 models in the index. The benchmark average is 73.5 and the leader is GPT-5.2 Pro at 91.6.

Score
75.3
0–100 scale
Rank
#325
of 600
Percentile
46th
within the index
Benchmark avg
73.5
all models
Leader
91.6
GPT-5.2 Pro
Gap to leader
16.3
points

Score distribution

The chart shows the top 10 models on MMLU-Pro, with o3 Nano highlighted. Longer bars mean higher scores.

GPT-5.2 Pro 91.6
GPT-5.1 Max 91.3
Claude Opus 4.5 Pro 91.2
Claude Opus 4.5 Reasoning 90.7
Gemini 3 Pro Reasoning 90.1
GPT-5.1 Reasoning 90.0
GPT-5.2 89.9
Claude Opus 4.5 Max 89.5
GPT-5.2 Vision 89.5
Gemini 3 Pro Max 89.5

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 GPT-5.2 Pro OpenAI 2000000 $21.32 89.6
2 GPT-5.1 Max OpenAI 2000000 $20.50 87.9
3 Claude Opus 4.5 Pro Anthropic 200000 $20.98 89.1
4 Claude Opus 4.5 Reasoning Anthropic 2000000 $20.91 88.2
5 Gemini 3 Pro Reasoning Google DeepMind 200000 $19.69 87.5
6 GPT-5.1 Reasoning OpenAI 400000 $20.27 88.3
7 GPT-5.2 OpenAI 2000000 $19.71 88.0
8 Claude Opus 4.5 Max Anthropic 1000000 $20.90 87.8
9 GPT-5.2 Vision OpenAI 1000000 $19.03 87.4
10 Gemini 3 Pro Max Google DeepMind 2000000 $20.57 87.9

What this score means

On MMLU-Pro, o3 Nano sits at #325 of 600. The leader, GPT-5.2 Pro, scores 91.6, so the gap to the top is 16.3 points. The median model in this index scores 76.1, which puts o3 Nano below the midpoint.

OpenAI models average 84.4 on this benchmark. o3 Nano is 9.1 points below that provider line, so it is a weaker result than the average OpenAI model.

Among mid-range models, the average is 71.5. o3 Nano is 3.8 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 75.3 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 $7.78 per million input tokens, o3 Nano is above the median input price of $4.00 across the 600 models scored on MMLU-Pro. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Gemma 3 Instruct Google DeepMind 128000 $3.72 74.1
Yi-Large Base 01.AI 16384 $2.78 70.3
Firefunction Max Fireworks AI 32768 $9.12 73.2
Llama 3.3 Groq Base Groq 16384 $3.24 73.5
o3 Nano OpenAI 32768 $7.78 70.6
DBRX Chat Databricks 65536 $3.07 71.7
Gemma 3 Base Google DeepMind 65536 $3.09 71.1

Other benchmarks for o3 Nano

BenchmarkScoreRankPercentileRelative
GPQA Diamond 64.8 364 39th
SWE-bench Verified 57.9 358 40th
HumanEval 72.5 377 37th
MATH-500 74.5 339 44th
AIME 2025 65.6 334 44th
LiveCodeBench 60.3 365 39th
IFEval 78.2 365 39th
TruthfulQA 75.4 324 46th
HellaSwag 81.4 360 40th
MMMU 70.2 295 51th
Arena Elo 1354.5 355 41th

FAQ

What score does o3 Nano get on MMLU-Pro?

o3 Nano scores 75.3 on MMLU-Pro in this index. The benchmark average is 73.5 and the top score is 91.6.

How does o3 Nano rank on MMLU-Pro?

It ranks #325 of 600 models, in the 46th percentile. The leader is GPT-5.2 Pro at 91.6.

Is o3 Nano good at MMLU-Pro?

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 MMLU-Pro score calculated?

MMLU-Pro is a knowledge 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 o3 Nano cost?

o3 Nano costs $7.78 per million input tokens and $29.81 per million output tokens.

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