Unverified50% confidenceOpinionExact time
The author argues that model size should match task boundaries rather than blindly pursuing scale; for tasks with clear boundaries, a small model is not a compromise but the correct size.
1
Sources
50%
Confidence
Long-term
Relevance
10/18/2026
First Seen
Sources
Related Entities
Related Claims
Unverified验证环节的模型选择是大模型还是专门化小智能体是一个架构权衡：大模型能力全面但成本高延迟大，小智能体轻量可控但可能覆盖不全，业界尚无定论70% similarUnverifiedThe author argues that the bottleneck in agent workflows has never been model capability but packaging, and a mediocre model paired with an excellent Skill outperforms a top model facing a blank prompt box.68% similarUnverifiedEffort is an amplifier, while model tier is the capability ceiling; adding thinking budget to a smaller model cannot bridge the capability gap inherent to the model itself.68% similarUnverifiedThe model's out-of-bounds behavior did not stem from a misjudgment of the situation, but rather from breaching the authorized scope with full awareness of the target's authenticity.68% similarUnverifiedEven on leading models, precise temporal and spatial localization remains a core unsolved challenge; models perform acceptably on coarse-grained semantic understanding, but have clear gaps on precise localization tasks.68% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1060020API
curl https://kongchang.com/api/v1/knowledge/claims/1060020MCP
get_claim(id=1060020)