GPT-4.1 Nano
OpenAI's efficiency model. Long-context specialist with 1.0M window.
Scores by test
Methodology →What you need to know
GPT-4.1 Nano is built for high-reliability data extraction and formatting rather than cognitive reasoning. Its primary technical advantage is a perfect score in structured output and faithfulness, meaning it adheres strictly to schemas and avoids hallucinations. This reliability extends to tool calling and agentic planning, making it a stable engine for automated workflows.
The model's reasoning capabilities are limited. It scores poorly in strategic analysis and creative problem solving, and its performance on complex logic benchmarks is low, specifically scoring 28.9% on AIME 2025 and 25.9% on SciCode. It is not designed for deep analytical tasks or complex mathematical derivation.
At a blended cost of $0.325 per million tokens, this is a low-cost utility model. It provides a massive 1.0M token context window, which, combined with its high faithfulness score, makes it an efficient tool for processing large volumes of text without losing detail.
Use this model if you need a cheap, reliable worker for structured data extraction, API orchestration, or long-context document processing. Skip this model if your use case requires complex reasoning, strategic planning, or high-level mathematical problem solving.
Strengths — Top 3
Relative weaknesses — Bottom 3
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