o3
OpenAI's mid-tier model. Context window: 200K tokens.
Scores by test
Methodology →What you need to know
o3 is engineered for high-reasoning tasks, specifically excelling in strategic analysis, agentic planning, and tool calling. Its performance in complex mathematical and coding environments is a primary differentiator, evidenced by a 97.8% score on MATH Level 5 and a 62.3% success rate on SWE-bench Verified. These metrics indicate a model capable of handling deep logic and autonomous software engineering tasks that typically defeat standard LLMs.
The pricing is high, with a blended cost of $6.50/MTok, positioning it as a premium tool. While expensive, the cost is justified for developers requiring high faithfulness and structured output, both of which score 5/5. However, the model is poorly suited for moderated environments, as its safety calibration is its lowest internal metric at 1/5.
The 200K context window is sufficient for most long-form documents, though its 3/5 classification score suggests it may struggle with simple labeling tasks compared to its strength in complex reasoning. It is a specialized instrument for logic rather than a general-purpose classifier.
Use this model if your application requires autonomous agentic workflows, complex mathematical derivation, or rigorous structured data output. Skip this model if you are budget-constrained, require strict safety guardrails, or only need a model for basic text classification.
Strengths — Top 3
Relative weaknesses — Bottom 3
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