Mathematics · OpenAI

GPT-4.1 Nano on MATH-500: score, rank and what it means

GPT-4.1 Nano scores 75.3 on MATH-500, placing it #319 of 600 models in the index. The benchmark average is 72.5 and the leader is GPT-5.2 Max at 96.7.

Score
75.3
0–100 scale
Rank
#319
of 600
Percentile
47th
within the index
Benchmark avg
72.5
all models
Leader
96.7
GPT-5.2 Max
Gap to leader
21.4
points

Score distribution

The chart shows the top 10 models on MATH-500, with GPT-4.1 Nano 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, GPT-4.1 Nano sits at #319 of 600. The leader, GPT-5.2 Max, scores 96.7, so the gap to the top is 21.4 points. The median model in this index scores 76.0, which puts GPT-4.1 Nano below the midpoint.

OpenAI models average 87.5 on this benchmark. GPT-4.1 Nano is 12.2 points below that provider line, so it is a weaker result than the average OpenAI model.

Among mid-range models, the average is 70.0. GPT-4.1 Nano is 5.3 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 $7.42 per million input tokens, GPT-4.1 Nano is above 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
Mixtral Turbo Together AI 65536 $3.20 72.4
Phi-3.5 Microsoft 16384 $2.81 70.9
Command A Mini Cohere 8192 $8.12 71.0
ERNIE 4.5 Mini Baidu 32768 $8.62 71.0
GPT-4.1 Nano OpenAI 32768 $7.42 70.5
Grok 3 Mini xAI 200000 $9.67 73.9
Reka Flash Turbo Reka AI 128000 $10.19 75.1

Other benchmarks for GPT-4.1 Nano

BenchmarkScoreRankPercentileRelative
MMLU-Pro 74.0 352 41th
GPQA Diamond 65.8 345 43th
SWE-bench Verified 59.7 332 45th
HumanEval 72.0 379 37th
AIME 2025 59.5 390 35th
LiveCodeBench 64.3 310 48th
IFEval 80.3 328 45th
TruthfulQA 75.3 325 46th
HellaSwag 82.5 334 44th
MMMU 66.6 363 40th
Arena Elo 1352.6 360 40th

FAQ

What score does GPT-4.1 Nano get on MATH-500?

GPT-4.1 Nano scores 75.3 on MATH-500 in this index. The benchmark average is 72.5 and the top score is 96.7.

How does GPT-4.1 Nano rank on MATH-500?

It ranks #319 of 600 models, in the 47th percentile. The leader is GPT-5.2 Max at 96.7.

Is GPT-4.1 Nano good at MATH-500?

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 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 GPT-4.1 Nano cost?

GPT-4.1 Nano costs $7.42 per million input tokens and $32.36 per million output tokens.

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