Unverified50% confidenceFactExact time
The differences in how models respond to prompts mainly come from three levels: training data composition, architecture and context window, and coverage of the continued-training corpus.
1
Sources
50%
Confidence
Long-term
Relevance
10/12/2026
First Seen
Sources
Related Entities
Related Claims
Unverified不同模型系列的工具调用输出格式差异根源于指令微调阶段使用的训练数据差异80% similarUnverified模型之间的差异源于训练数据规模和质量、参数数量、对齐策略（如RLHF）以及上下文窗口长度等核心设计选择77% similarUnverifiedThere is a gap between benchmark results and real-world experience, as a model's actual performance varies depending on the project's code style, technology stack, and task type.74% similarUnverified参数量相似的情况下，训练数据质量分布的差异往往比架构差异更能决定特定场景的表现上限74% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1008111API
curl https://kongchang.com/api/v1/knowledge/claims/1008111MCP
get_claim(id=1008111)