Coding · OpenAI

o4 Nano on SWE-bench Verified: score, rank and what it means

o4 Nano scores 62.3 on SWE-bench Verified, placing it #272 of 600 models in the index. The benchmark average is 57.5 and the leader is GPT-5.2 Pro at 81.7.

Score
62.3
0–100 scale
Rank
#272
of 600
Percentile
55th
within the index
Benchmark avg
57.5
all models
Leader
81.7
GPT-5.2 Pro
Gap to leader
19.4
points

Score distribution

The chart shows the top 10 models on SWE-bench Verified, with o4 Nano highlighted. Longer bars mean higher scores.

GPT-5.2 Pro 81.7
Claude Opus 4.5 Pro 81.1
GPT-5.2 Max 80.5
Claude Sonnet 4.5 Max 80.2
GPT-5.2 80.2
GPT-5.1 Pro 80.1
o4 Max 79.8
Claude Sonnet 4.5 Reasoning 79.6
Claude Opus 4.5 Max 79.5
Claude Opus 4.5 Reasoning 79.5

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 GPT-5.2 Pro OpenAI 2000000 $21.32 89.6
2 Claude Opus 4.5 Pro Anthropic 200000 $20.98 89.1
3 GPT-5.2 Max OpenAI 1000000 $21.39 89.2
4 Claude Sonnet 4.5 Max Anthropic 1000000 $19.90 87.2
5 GPT-5.2 OpenAI 2000000 $19.71 88.0
6 GPT-5.1 Pro OpenAI 200000 $19.77 87.8
7 o4 Max OpenAI 2000000 $19.51 86.2
8 Claude Sonnet 4.5 Reasoning Anthropic 400000 $19.48 87.7
9 Claude Opus 4.5 Max Anthropic 1000000 $20.90 87.8
10 Claude Opus 4.5 Reasoning Anthropic 2000000 $20.91 88.2

What this score means

On SWE-bench Verified, o4 Nano sits at #272 of 600. The leader, GPT-5.2 Pro, scores 81.7, so the gap to the top is 19.4 points. The median model in this index scores 61.0, which puts o4 Nano above the midpoint.

OpenAI models average 72.7 on this benchmark. o4 Nano is 10.4 points below that provider line, so it is a weaker result than the average OpenAI model.

Among mid-range models, the average is 54.9. o4 Nano is 7.4 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 62.3 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 $8.61 per million input tokens, o4 Nano is above the median input price of $4.00 across the 600 models scored on SWE-bench Verified. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
GPT-5.2 Nano OpenAI 128000 $9.73 73.5
Llama 3 Replicate Instruct Replicate 128000 $3.71 75.1
Nova Lite Reasoning Amazon 32768 $9.92 74.3
Nemotron 4 Chat NVIDIA 65536 $3.99 75.0
o4 Nano OpenAI 65536 $8.61 72.0
Claude Opus 4.5 Nano Anthropic 32768 $9.38 73.3
Llama 3 Replicate Base Replicate 65536 $3.15 72.1

Other benchmarks for o4 Nano

BenchmarkScoreRankPercentileRelative
MMLU-Pro 74.9 339 44th
GPQA Diamond 67.3 327 46th
HumanEval 74.7 350 42th
MATH-500 74.0 349 42th
AIME 2025 64.2 354 41th
LiveCodeBench 65.9 278 54th
IFEval 81.6 286 52th
TruthfulQA 75.2 327 46th
HellaSwag 81.2 363 40th
MMMU 70.2 296 51th
Arena Elo 1374.8 294 51th

FAQ

What score does o4 Nano get on SWE-bench Verified?

o4 Nano scores 62.3 on SWE-bench Verified in this index. The benchmark average is 57.5 and the top score is 81.7.

How does o4 Nano rank on SWE-bench Verified?

It ranks #272 of 600 models, in the 55th percentile. The leader is GPT-5.2 Pro at 81.7.

Is o4 Nano good at SWE-bench Verified?

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 SWE-bench Verified score calculated?

SWE-bench Verified 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 o4 Nano cost?

o4 Nano costs $8.61 per million input tokens and $41.97 per million output tokens.

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