Seed 2.1 Turbo
ByteDance's efficiency model. Context window: 262K tokens.
Scores by test
Methodology →What you need to know
Seed 2.1 Turbo is engineered for high-precision technical tasks, specifically excelling in structured output, tool calling, and faithfulness. With perfect 5/5 scores across these domains, it is highly reliable for developers building agentic workflows or applications requiring strict adherence to schemas and data formats. Its ability to handle tabular data and multilingual inputs further strengthens its utility for complex data processing.
The model offers a substantial 262K context window and maintains a 5/5 score for long-context performance, making it a viable choice for analyzing large documents. At a blended cost of $2.00/MTok, it is competitively priced for a model with these specific technical capabilities, providing high utility for automation without the premium cost of top-tier frontier models.
Performance is inconsistent outside of technical execution. It struggles significantly with classification (2/5) and safety calibration (1/5), the latter of which indicates a tendency to over-refuse benign requests. These weaknesses, combined with an overall rank of 86 out of 136 models, suggest that while it is a specialist in structure and retrieval, it is not a general-purpose powerhouse.
Use this model if you need a cost-effective engine for tool calling, structured data extraction, or processing long-form multilingual documents. Skip this model if your use case relies heavily on nuanced text classification or if your application requires a high tolerance for diverse prompts without triggering false-positive safety refusals.
Strengths — Top 3
Relative weaknesses — Bottom 3
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