Coding · OpenAI

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

GPT-4.1 Nano scores 64.3 on LiveCodeBench, placing it #310 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
64.3
0–100 scale
Rank
#310
of 600
Percentile
48th
within the index
Benchmark avg
61.0
all models
Leader
85.1
Claude Sonnet 4.5 Reasoning
Gap to leader
20.8
points

Score distribution

The chart shows the top 10 models on LiveCodeBench, with GPT-4.1 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, GPT-4.1 Nano sits at #310 of 600. The leader, Claude Sonnet 4.5 Reasoning, scores 85.1, so the gap to the top is 20.8 points. The median model in this index scores 64.7, which puts GPT-4.1 Nano below the midpoint.

OpenAI models average 77.2 on this benchmark. GPT-4.1 Nano is 12.9 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. GPT-4.1 Nano is 6.0 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 64.3 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 LiveCodeBench. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Llama 3 Lambda Chat Lambda 32768 $3.73 74.7
DBRX Chat Databricks 65536 $3.07 71.7
Gemma 3 Instruct Google DeepMind 128000 $3.72 74.1
Fusion OpenRouter 16384 $8.41 71.1
GPT-4.1 Nano OpenAI 32768 $7.42 70.5
GPT-5.2 Nano OpenAI 128000 $9.73 73.5
Grok 3 Mini xAI 200000 $9.67 73.9

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
MATH-500 75.3 319 47th
AIME 2025 59.5 390 35th
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 LiveCodeBench?

GPT-4.1 Nano scores 64.3 on LiveCodeBench in this index. The benchmark average is 61.0 and the top score is 85.1.

How does GPT-4.1 Nano rank on LiveCodeBench?

It ranks #310 of 600 models, in the 48th percentile. The leader is Claude Sonnet 4.5 Reasoning at 85.1.

Is GPT-4.1 Nano good at LiveCodeBench?

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 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 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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