Unverified50% confidenceFactExact time
The output space of a decision model is fixed to a finite set of options, allowing all candidates to be scored in parallel rather than decoded sequentially, which greatly reduces latency.
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50%
Confidence
Long-term
Relevance
10/7/2026
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Unverified函数调用能力更多依赖于针对性的指令微调，而非单纯堆叠模型规模，因为其输出空间有限且模式固定78% similarUnverifiedCompared to having an LLM perform the same classification task, a dedicated decision model typically has lower latency, more controllable costs, and more well-formed output formats, but at the cost of open-ended expressive capability75% similarUnverifiedThe Decisions API can improve speed by nearly tenfold by constraining the output space so the model selects only from a small number of preset options74% similarUnverifiedDecision models with a single forward pass and a constrained output space are well-suited to the low-latency requirements of edge computing, making them appropriate for high-throughput, low-latency scenarios such as network request processing and security policy determination.72% similarUnverifiedThe prerequisite for choosing a specialized small model is that the business scenario is sufficiently stable and the label space is relatively closed; when decision logic changes frequently or a large number of long-tail anomalies need to be handled, a general-purpose large model is more appropriate.72% similar
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