GPT-4.1 Mini
OpenAI's efficiency model. Long-context specialist with 1.0M window.
Scores by test
Methodology →What you need to know
GPT-4.1 Mini is optimized for high-volume, multilingual tasks requiring massive context windows. With a 1.0M token limit and a perfect 5/5 internal score for long context and multilingual capabilities, it is designed for processing expansive datasets across various languages without losing persona consistency.
The model offers a strong value proposition for developers prioritizing utility over strict safety guardrails. While its overall rank is #49 of 71, it performs reliably in technical execution, scoring 4/5 in agentic planning, tool calling, and structured output. However, it struggles with safety calibration (2/5) and basic classification (3/5), suggesting it is less suited for moderated user-facing interfaces or simple labeling tasks.
Mathematically, the model is highly capable, evidenced by an 87.3% score on MATH Level 5. At a blended cost of $1.30/MTok, it provides high-tier reasoning and long-context memory at a price point suitable for scaling complex agentic workflows.
Use this model if you need a cost-effective solution for multilingual processing, long-document analysis, or complex mathematical reasoning. Skip this model if your application requires strict safety filtering or high precision in simple classification tasks.
Strengths — Top 3
Relative weaknesses — Bottom 3
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