2026 年本地大模型横评:Llama 3.3 vs Qwen 2.5 vs Mistral vs DeepSeek

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2026 年本地大模型横评:Llama 3.3 vs Qwen 2.5 vs Mistral vs DeepSeek

用真实任务测试,告诉你该下载哪个模型

Local LLM 横评 2026:Llama vs Qwen vs Mistral——Llama 生态/工具最全、Qwen 多语言(中文)与编程范围广、Mistral/Mixtral 主打效率(MoE)。含部署(Ollama/vLLM)与量化链路、模型库对照。

Local LLM Comparison 2026: Llama vs Qwen vs Mistral

Short answer: all three are top open-weight families you can run locally, with different sweet spots. Llama (Meta) is the ecosystem default — the widest tooling and fine-tune support. Qwen (Alibaba) is a strong all-rounder, especially good at multilingual and Chinese plus coding, with many sizes. Mistral/Mixtral (Mistral AI) is known for efficient, high-quality models including mixture-of-experts. Pick Llama for ecosystem, Qwen for multilingual/coding breadth, Mistral for efficiency.

At a glance

Llama (Meta)Qwen (Alibaba)Mistral / Mixtral

StrengthEcosystem, toolingMultilingual, coding, many sizesEfficiency, MoE SizesSmall → largeVery wide rangeCompact + MoE LicenseOpen (Llama community)Open (Apache for many)Open (Apache for several) Best forDefault local + fine-tuningChinese/multilingual, codingEfficient quality per param

How they differ

Llama is the gravitational center of the open-weights world — almost every tool, quantization format, and fine-tuning recipe targets it first. If you want the smoothest local experience and the most community resources, start here.

Qwen offers an unusually wide range of sizes and is strong on multilingual (notably Chinese) and coding tasks, with permissive licensing on many checkpoints — a great all-rounder.

Mistral/Mixtral focuses on efficiency: high quality per parameter, including mixture-of-experts (Mixtral) that activate only part of the network per token for speed.

To actually serve these, see Ollama vs vLLM and the GUI options in Ollama vs LM Studio vs Jan. To fit bigger models on your hardware, see 模型量化 GPTQ/AWQ 指南.

How to choose

  • Want the safest default with the most tooling? Llama.
  • Multilingual / Chinese / coding breadth? Qwen.
  • Best efficiency per parameter? Mistral/Mixtral.
  • Running on modest hardware? Pick a smaller size + quantization.
  • FAQ

    Which runs on a laptop? Smaller variants of all three, especially quantized (GGUF) via Ollama. Which is best for Chinese? Qwen, generally. Are they free for commercial use? Many checkpoints are (Apache); always check the specific model's license.

    Verdict

    There's no single best local LLM — there's the best for your constraint. Llama for ecosystem and fine-tuning, Qwen for multilingual and coding range, Mistral/Mixtral for efficiency. Browse the lineup and sizes in our 模型库, then serve with Ollama (dev) or vLLM (production).


    *Last updated: June 2026. Open-weight models update often; verify current versions and licenses in our 模型库.*

    相关工具

    OllamaLM Studiollama.cpp