Llama vs DeepSeek

Open-weight language model family from Meta, released for local and self-hosted use.

功能对比

Llama 与 DeepSeek 功能对比
维度	Llama	DeepSeek
Context window	128k tokens	128k tokens
License	Llama Community License	MIT for code, DeepSeek License for weights
Open weights	Yes	Yes
Parameters	8B, 70B, 405B	7B to 67B
Provider	Meta	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。

该选哪个？

由上述差异按固定规则生成，不是模型生成的。

Choose Llama if…

已记录的属性中，没有任何一项显示它更有优势。

Choose DeepSeek if…

已记录的属性中，没有任何一项显示它更有优势。

Llama

model · Models

Llama is a family of language models whose weights are published, so they can be run on your own hardware instead of only through an API. It solves the problem that most capable models are reachable only as a hosted service, which rules them out when data cannot leave your network or when per-token cost dominates. It exists because releasing weights turned out to build an ecosystem of fine-tunes and tooling around one architecture, and it is the model most local inference tools target first.

DeepSeek

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.