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GPT-4.1 Nano

by OpenAI · Released Apr 2025

Multimodal1M Context
22.4
avg score
Rank #273
Compare
Better than 13% of all models
Context
1.0M tokens (~524 books)
Input $/1M
$0.10
Output $/1M
$0.40
Type
multimodal
License
Proprietary
Benchmarks
17 tested
Data as of
About

For tasks that demand low latency, GPT‑4.1 nano is the fastest and cheapest model in the GPT-4.1 series. It delivers exceptional performance at a small size with its 1 million...

Tested on 17 benchmarks · BenchGecko score 22.4. Top scores: HELM — IFEval (84.3%), HELM — WildBench (81.1%), MATH level 5 (70.0%).

Looking for similar performance at lower cost?
Gemma 3 27B scores 22.7 (101% as good) at $0.08/1M input · 20% cheaper
Capabilities
coding
13.9
#185 globally
reasoning
27.1
#130 globally
math
34.1
#177 globally
knowledge
33.7
#234 globally
language
84.3
#38 globally
general
13.6
#184 globally
Benchmark Scores
Compare All
Tested on 17 benchmarks · Ranked across 6 categories
Score Distribution (all 312 models)
0255075100
▲ You are here
codingCompare coding →
WeirdML

Unusual and adversarial machine learning challenges. Tests robustness of reasoning about edge cases in ML systems.

19.0·
Aider polyglot

Multi-language code editing from Aider. Tests editing ability across Python, JavaScript, TypeScript, Java, C++, Go, Rust, and more.

8.9·
reasoningCompare reasoning →
HELM — WildBench

Stanford HELM WildBench evaluation. Tests reasoning on challenging real-world tasks.

81.1·
ARC-AGI-2

ARC-AGI 2, harder sequel to ARC. More complex abstract reasoning patterns that test generalization ability beyond training data.

0.1·
ARC-AGI

Abstraction and Reasoning Corpus. Tests fluid intelligence through novel visual pattern recognition puzzles. Core measure of general intelligence.

0.1·
mathCompare math →
MATH level 5

Competition-level math from AMC, AIME, and olympiad problems. Level 5 is the hardest tier, requiring creative problem-solving.

70.0·
HELM — Omni-MATH

Stanford HELM evaluation of mathematical reasoning across diverse problem types.

36.7·
OTIS Mock AIME 2024-2025

Mock AIME (American Invitational Mathematics Exam) problems from OTIS. Tests mathematical competition performance.

28.8·
Quick compare:Anthropic logovs Claude 2.1Meta logovs Llama 4 MaverickMistral AI logovs Mistral Small 3.1 24B
Excellent (85+) Good (70-85) Average (50-70) Below (<50)
Model Family · OpenAI GPT-4.1
Apr 2025
37.9
$2.00/M in1.0Mctx27 benchmarks
Apr 2025
$1.00/M in(-1)1.0Mctx
Apr 2025
35.2-2.7
$0.40/M in(-0.60)1.0Mctx22 benchmarks
Apr 2025
$0.20/M in(-0.20)1.0Mctx
Apr 2025
22.4-12.8
$0.10/M in(-0.10)1.0Mctx17 benchmarks
Apr 2025
$0.05/M in(-0.05)1.0Mctx
See the full GPT-4.1 family →
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Links
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BenchGecko API
gpt-4-1-nano
Specifications
  • Typemultimodal
  • Context1.0M tokens (~524 books)
  • ReleasedApr 2025
  • LicenseProprietary
  • StatusActive
  • Cost / Message~$0.001
Available On
OpenAI logoOpenAI$0.10
Categories
codingreasoningmathknowledgelanguagegeneral
Learn More
context-windowtransformertokens
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Related Models
Anthropic logoClaude 2.1
22.2
Meta logoLlama 4 Maverick
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Mistral AI logoMistral Small 3.1 24B
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Google DeepMind logoGemma 3 27B
22.7
GPT-4.1 Nano is a proprietary multimodal AI model by OpenAI, released in April 2025. It has an average benchmark score of 22.4. Context window: 1M tokens.

Related Models

Claude 2.1 · AnthropicLlama 4 Maverick · MetaMistral Small 3.1 24B · Mistral AIGemma 3 27B · Google DeepMindLlama 2 7b Hf · Meta

Benchmarks

HELM — IFEvalHELM — WildBenchMATH level 5HELM — MMLU-ProHELM — GPQA

Related Pages

OpenAI · ProviderOpenAI · EconomyAll ModelsCompare ModelsPricingDevelopers · APIContext Window · Glossary

Key facts · as of 2026-10-05

  • GPT-4.1 Nano by OpenAI. BenchGecko score 22.4, rank 273 of 312 scored models (normalized average of public benchmark scores).
  • List price $0.10 input · $0.40 output per 1M tokens (as of 2026-10-05).
  • Sold by 3 providers (as of 2026-10-05): Azure $0.10 in / $0.40 out · OpenAI $0.10 in / $0.40 out · Azure $0.11 in / $0.44 out. Every provider

How to cite · data as of 2026-10-05

GPT-4.1 Nano · benchmarks, pricing and providers. BenchGecko, data as of 2026-10-05. https://benchgecko.ai/model/gpt-4-1-nano

Credit "Source: BenchGecko" with a link. Prices per provider and Gecko Tests are BenchGecko data (CC BY 4.0); benchmark scores keep their original source, listed in the JSON. JSON · llms.txt · MCP