Coding · OpenAI

o4 Nano on LiveCodeBench: score, rank and what it means

o4 Nano scores 65.9 on LiveCodeBench, placing it #278 of 600 models in the index. The benchmark average is 61.0 and the leader is Claude Sonnet 4.5 Reasoning at 85.1.

Score
65.9
0–100 scale
Rank
#278
of 600
Percentile
54th
within the index
Benchmark avg
61.0
all models
Leader
85.1
Claude Sonnet 4.5 Reasoning
Gap to leader
19.2
points

Score distribution

The chart shows the top 10 models on LiveCodeBench, with o4 Nano highlighted. Longer bars mean higher scores.

Claude Sonnet 4.5 Reasoning 85.1
Grok 4 Reasoning 85.1
Claude Opus 4.5 85.0
GPT-5.2 Max 84.8
Grok 4 Turbo 84.7
GPT-5.1 Turbo 84.6
GPT-5.2 Pro 84.6
GPT-5.2 Reasoning 84.6
Claude Opus 4.5 Pro 84.1
Gemini 3 Pro Max 83.6

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 Claude Sonnet 4.5 Reasoning Anthropic 400000 $19.48 87.7
2 Grok 4 Reasoning xAI 2000000 $19.67 87.8
3 Claude Opus 4.5 Anthropic 400000 $19.42 87.7
4 GPT-5.2 Max OpenAI 1000000 $21.39 89.2
5 Grok 4 Turbo xAI 1000000 $19.21 86.7
6 GPT-5.1 Turbo OpenAI 2000000 $19.19 86.1
7 GPT-5.2 Pro OpenAI 2000000 $21.32 89.6
8 GPT-5.2 Reasoning OpenAI 1000000 $20.98 87.5
9 Claude Opus 4.5 Pro Anthropic 200000 $20.98 89.1
10 Gemini 3 Pro Max Google DeepMind 2000000 $20.57 87.9

What this score means

On LiveCodeBench, o4 Nano sits at #278 of 600. The leader, Claude Sonnet 4.5 Reasoning, scores 85.1, so the gap to the top is 19.2 points. The median model in this index scores 64.7, which puts o4 Nano above the midpoint.

OpenAI models average 77.2 on this benchmark. o4 Nano is 11.3 points below that provider line, so it is a weaker result than the average OpenAI model.

Among mid-range models, the average is 58.3. o4 Nano is 7.6 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 65.9 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 LiveCodeBench. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Firefunction Pro Fireworks AI 16384 $9.05 72.9
Llama 3 Replicate Instruct Replicate 128000 $3.71 75.1
Nemotron 4 Chat NVIDIA 65536 $3.99 75.0
Yi-Large Instruct 01.AI 32768 $3.48 73.4
o4 Nano OpenAI 65536 $8.61 72.0
Codestral Base Mistral AI 8192 $3.31 73.1
Llama 3 Lambda Base Lambda 8192 $3.19 72.9

Other benchmarks for o4 Nano

BenchmarkScoreRankPercentileRelative
MMLU-Pro 74.9 339 44th
GPQA Diamond 67.3 327 46th
SWE-bench Verified 62.3 272 55th
HumanEval 74.7 350 42th
MATH-500 74.0 349 42th
AIME 2025 64.2 354 41th
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 LiveCodeBench?

o4 Nano scores 65.9 on LiveCodeBench in this index. The benchmark average is 61.0 and the top score is 85.1.

How does o4 Nano rank on LiveCodeBench?

It ranks #278 of 600 models, in the 54th percentile. The leader is Claude Sonnet 4.5 Reasoning at 85.1.

Is o4 Nano good at LiveCodeBench?

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 LiveCodeBench score calculated?

LiveCodeBench 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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