Unverified50% confidenceOpinionExact time
Compared with closed-source models that can only be guided through prompts and a few examples, fine-tuning of open-source models can directly change parameters, so they often perform more stably on tasks with strict formats and dense terminology.
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50%
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Long-term
Relevance
11/2/2026
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VerifiedOpen-source models still lag behind top closed-source models on some complex reasoning and long-context tasks.79% similarUnverified端侧小模型在指令遵循上往往不如云端大模型稳定，通过约束解码强制模型按预定义语法输出可在不牺牲推理速度的前提下保证工具调用的结构化可靠性78% similarUnverifiedWhen constraining output format through prompts, the model may forget these constraints in long conversations or complex reasoning scenarios.74% similarUnverifiedThis formula governs structure, not the upper limit of content quality; the verification and hands-on practice in the tutorial are based on single-generation results from Doubao, and performance will differ across models and prompt versions—the formula is a starting point, not a guarantee.74% similarUnverifiedOpen-weight models (such as the Llama series and Mistral) allow users to modify system prompts, fine-tune, or even remove safety alignment layers, so the built-in refusal constraints of commercial models are easily bypassed on open-source models.74% similar
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