OpenAI: gpt-oss-20b
OpenAI's efficiency model. Context window: 131K tokens.
Scores by test
Methodology →What you need to know
The primary value of gpt-oss-20b is its high reliability in structured data generation and long-context processing. With perfect 5/5 internal scores in both Structured Output and Long Context, this model is optimized for extracting data from large documents into precise formats. These capabilities are paired with a 131K context window, making it a specialized tool for high-volume data parsing.
Despite these strengths, the model ranks low overall (#115 of 130) due to significant deficits in reasoning and safety. A 34.4% score on SciCode and a 2/5 in Safety Calibration indicate that it struggles with complex scientific logic and strict guardrail adherence. Its performance in classification and constrained rewriting is mediocre, suggesting it is not suitable for nuanced editorial tasks or high-precision labeling.
At a blended cost of $0.105/MTok, the model is priced as a budget-tier option. While the cost is low, the trade-off is a lack of general-purpose intelligence and poor safety tuning. You are paying for a narrow set of capabilities—specifically structured output and context handling—rather than a balanced assistant.
Use this model if you need a low-cost engine for transforming large volumes of unstructured text into JSON or other structured formats. Skip this model if your application requires complex strategic reasoning, high safety standards, or precise classification.
Strengths — Top 3
Relative weaknesses — Bottom 3
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