StepFun: Step 3.7 Flash
stepfun's efficiency model. Context window: 256K tokens.
Scores by test
Methodology →What you need to know
Step 3.7 Flash is optimized for high-precision technical tasks, specifically excelling in structured output, tool calling, and strategic analysis. With perfect scores in these areas, it is built for reliability in programmatic workflows and data extraction. Its ability to handle tabular data and multilingual inputs further strengthens its utility for complex, data-driven automation.
The model offers a competitive price point with a blended cost of $0.912 per million tokens and a substantial 256K context window. This makes it an economical choice for processing large datasets or maintaining long conversation histories without significant cost overhead.
Despite its technical strengths, the model has critical failures in safety calibration and classification. A score of 1/5 in safety indicates a lack of robust guardrails, while its poor classification performance suggests it struggles with simple labeling or categorization tasks.
Use this model if you need a low-cost, high-context engine for agentic planning, API tool integration, or structured data generation. Skip this model if your application requires strict safety filtering or high accuracy in text classification.
Strengths — Top 3
Relative weaknesses — Bottom 3
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