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 optimized for high-precision data extraction and automation rather than cognitive reasoning. Its primary strengths are structured output and faithfulness, both scoring 5/5, making it highly reliable for tasks requiring strict adherence to schemas and factual grounding. These capabilities, paired with a 4/5 in tool calling and agentic planning, position the model as a dependable utility for pipeline integration.
The model struggles with complex intellectual tasks, scoring 2/5 in strategic analysis, creative problem solving, and safety calibration. This deficit is reflected in its external benchmarks, where it achieves only 28.9% on AIME 2025. Consequently, it is not suitable for autonomous decision-making or high-level mathematical reasoning.
At a blended cost of $0.325/MTok, the model is priced for high-volume utility. While it ranks #60 of 71 models overall, its 1.0M context window provides significant value for processing large datasets provided the goal is extraction rather than synthesis.
Use this model if you need a low-cost, high-context tool for structured data extraction, tool calling, or constrained rewriting. Skip this model if your use case requires complex strategic planning, creative problem solving, or rigorous safety guardrails.
Strengths — Top 3
Relative weaknesses — Bottom 3
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