Microsoft's small models trained mainly on curated textbook-quality data.
| 维度 | Phi | DeepSeek |
|---|---|---|
| Context window | 128k tokens | 128k tokens |
| License | MIT | MIT for code, DeepSeek License for weights |
| Open weights | Yes | Yes |
| Parameters | 3.8B, 14B | 7B to 67B |
| Provider | Microsoft | DeepSeek |
灰底行表示两者相同；有差异的行才是下方推荐结论的依据。
由上述差异按固定规则生成，不是模型生成的。
model · Models
Phi is a family of small language models trained on a deliberately narrow, high-quality corpus of textbooks and exercises rather than on a web-scale crawl. It solves the assumption that capability requires scale, showing that data selection can substitute for a large part of it. It exists because it reframes the question from how much data to what kind of data, and the answer is useful when the budget is a single GPU.
model · Models
DeepSeek is an open-weight model family whose published training methods attracted attention for reaching competitive quality at lower reported cost. It solves the cost question in training rather than in inference, which is where most optimisation effort goes. It exists as evidence that architecture and training-schedule choices matter enough to change what a given budget can produce, and its weights and methods are published.
