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OGPT-4.1 Nano (batch)API

For tasks that demand low latency, GPT‑4.1 nano is the fastest and cheapest model in the GPT-4.1 series.

1MContext window · tokens
$0.05Input price · per M tokens
$0.2Output price · per M tokens
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Prices update automatically — checked daily against provider list prices.

See open-source alternatives → Compare all model prices

Benchmarks & performance

Independent benchmark scores for GPT-4.1 Nano (batch), measured by Artificial Analysis. Higher is better.

Intelligence index9.6
Coding index11.1
Math index24
GPQA51.2%
MMLU-Pro65.7%
Humanity's Last Exam3.8%
Long Context Reasoning19.3%
LiveCodeBench32.6%
SciCode25.9%
MATH-50084.8%
AIME23.7%
AIME 202524%
IFBench32%
τ²-Bench17.3%
τ-Bench Banking3.5%
Terminal-Bench3.7%
Terminal-Bench Hard3.8%
💰 Blended price$0.175 / 1M tokens
📈 Value54.9 intelligence points per $
vs. models measured heretop 84%
Scores higher than 16% of the 273 models measured by Artificial Analysis and tracked here.
Models at this level cost $0.22 per 1M tokens (median of 25) — this one costs $0.09.
Cheaper and better on this index: DeepSeek V4 Flash 0731 · Solar Pro 4 · Ling-3.0-flash and 3 more
Benchmark data by Artificial Analysis

About this model

GPT-4.1 Nano (batch) is a commercial AI model by OpenAI. The specifications below are tracked automatically: pricing is refreshed daily from public list prices, so the numbers on this page reflect the current cost of using the model through its API.

Frequently asked questions

What is GPT-4.1 Nano (batch)?

GPT-4.1 Nano (batch) is an AI language model from OpenAI. It is a proprietary model, available through an API. It scores 9.6 on the Artificial Analysis intelligence index.

Is GPT-4.1 Nano (batch) free?

GPT-4.1 Nano (batch) is not free: it costs $0.05 per million input tokens and $0.2 per million output tokens. Open-weight alternatives can be self-hosted at no per-token cost.

What is GPT-4.1 Nano (batch) good at?

Independent benchmarks from Artificial Analysis give it GPQA 51.2%, MMLU-Pro 65.7%, Humanity's Last Exam 3.8%, Long Context Reasoning 19.3%, LiveCodeBench 32.6%, SciCode 25.9%, MATH-500 84.8%, AIME 23.7%, AIME 2025 24%, IFBench 32%, τ²-Bench 17.3%, τ-Bench Banking 3.5%, Terminal-Bench 3.7%, Terminal-Bench Hard 3.8%. It is particularly used for code generation.

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