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GPT-4.1 nano benchmarks, pricing and specs

GPT-4.1 nano is an OpenAI model released on April 14, 2025 that takes text and image input and produces text. It costs $0.10 per 1M input tokens and $0.40 per 1M output tokens and accepts up to 1,047,576 tokens of context. It scores 1322 Elo on Arena Text, #39 of 39 current models (Arena, measured October 8, 2026).

Per 1M input tokens
$0.10
Per 1M output tokens
$0.40
Context window
1.05M
Arena Text
1322 Elo · #39/39

Data verified October 12, 2026 · Dataset 2026-10-12

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Key facts

Provider
OpenAI
Model family
GPT-4.1
Release date
April 14, 2025
Status
Current
Access
API only
API model id
Not reported
Context window
1,047,576 tokens
Max output
32,768 tokens
Input
text, image
Output
text
Task families
text, multimodal
Declared capabilities
reasoning

GPT-4.1 nano pricing

It is tied for the cheapest of 37 current text models priced per 1M input tokens.

UnitPrice (USD)TierAppliesSource
1M input tokens$0.10standardCurrent
1M output tokens$0.40standardCurrent
1M input tokens$0.025cachedCurrent

Independent benchmark results

Scores come from independent evaluators that ran the model themselves, pinned to a dated snapshot. Rank is among current models in the same snapshot. OpenAI's own launch figures are listed under strengths with the publisher named, because they are not measured the same way.

BenchmarkScoreRankMeasuredSource
Arena Text1322 Elo#39 of 39October 8, 2026Arena · 2026-10-12
GPQA Diamond48.9%#37 of 37October 12, 2026Epoch AI · 2026-10-12
SimpleQA Verified6%#34 of 34October 12, 2026Epoch AI · 2026-10-12

Strengths, cautions and best uses

Strengths

  • GPT-4.1 nano records API access for text and image inputs and a published 1,047,576-token context window

Cautions

  • Not in the pinned LiveBench snapshot; its independent scores here come from Epoch AI's own GPQA Diamond, FrontierMath and SimpleQA Verified runs

Best for

  • High-volume API screening at low published token prices

Alternatives to GPT-4.1 nano

The closest current textmodels by price. The cheapest model that scores as well or better is listed first; failing that, the cheapest one within 3% of the benchmark's scale.

Compare GPT-4.1 nano with

Other GPT-4.1 models

GPT-4.1 nano FAQ

How much does GPT-4.1 nano cost?

GPT-4.1 nano costs $0.10 per 1M input tokens and $0.40 per 1M output tokens at the standard tier. It is tied for the cheapest of 37 current text models priced per 1M input tokens. Source: OpenAI, verified August 6, 2026.

What is the context window of GPT-4.1 nano?

GPT-4.1 nano accepts up to 1,047,576 tokens of context. It can return up to 32,768 tokens in one response. That ranks #21 of 44 current text models by context length.

How does GPT-4.1 nano score on independent benchmarks?

GPT-4.1 nano scores 1322 Elo on Arena Text, #39 of 39 current models (Arena, measured October 8, 2026), 48.9% on GPQA Diamond, #37 of 37 current models (Epoch AI, measured October 12, 2026) and 6% on SimpleQA Verified, #34 of 34 current models (Epoch AI, measured October 12, 2026). These are independent evaluations, not OpenAI's own launch figures.

Is GPT-4.1 nano open source or API only?

GPT-4.1 nano is available through OpenAI's API only; its weights are not published.

What are the best alternatives to GPT-4.1 nano?

The closest current alternatives by price and measured performance are Claude Haiku 5.5 (same price per 1M input tokens, $0.10), GPT-6 Luna (same price per 1M input tokens, $0.10), GPT-5.6 Luna (2.0x the price per 1M input tokens, $0.20 vs $0.10) and Gemini 3.1 Flash-Lite (2.5x the price per 1M input tokens, $0.25 vs $0.10).

Sources

Cite this page

Van Data Team. "GPT-4.1 nano Benchmarks, Pricing and Specs." AI Model Benchmark Dataset 2026-10-12. https://vandatateam.com/ai-model-benchmarks/gpt-4-1-nano

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Production evaluation

Deciding whether GPT-4.1 nano fits your workload?

Public benchmarks narrow the shortlist. We test the finalists on your own tasks, data and cost limits, then build the routing and review controls to run the winner in production.

  • Evaluation cases and acceptance thresholds tied to real work
  • Model routing, fallbacks and cost controls