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⚖️ Comparison 横向对比 🏆 两大开源阵营对决 comparison llm qwen deepseek chinese

通义千问 vs DeepSeek – 全面对比

Qwen vs DeepSeek head-to-head: reasoning, coding, multimodality, ecosystem and API pricing — with a buying guide深度推理、代码能力、多模态、生态整合与 API 价格全面对比，附选型建议

🗓 Updated更新:
Qwen Official Site ↗ 通义官网 ↗ DeepSeek Official Site ↗ DeepSeek 官网 ↗ ⚖️ Compare
Category分类
Comparison · LLM对比 · 大语言模型
comparison
Players对阵双方
通义千问 vs DeepSeek
Qwen (Alibaba) vs DeepSeek (Hangzhou)阿里 vs 深度求索
Open weights开源状态
Both open都开源
Qwen: Apache 2.0 · DeepSeek: MIT通义 Apache 2.0 · DeepSeek MIT
Tags标签
comparison, llm, qwen, deepseek, chinese
5 tags total个标签

One-line verdict: DeepSeek wins on raw reasoning-per-yuan and its ultra-cheap API; Qwen wins on breadth — more model sizes, native vision, and the Alibaba cloud ecosystem behind it. For a cheap production API, start with DeepSeek; for private or edge deployment across hardware tiers, Qwen's size ladder is unmatched. Most serious teams end up testing both.

一句话结论：DeepSeek 赢在"推理性价比"和超便宜的 API；通义千问赢在"广度"——更多尺寸、原生视觉、背靠阿里云生态。做低成本线上 API 先看 DeepSeek；做私有化或多硬件部署，通义的尺寸梯队无人能及。认真的团队最后都会两边实测。

— 中国吧 AI Tools Hub Editorial Team

Why Compare Qwen and DeepSeek?为什么要对比通义千问和 DeepSeek？

Qwen （通义千问）, from Alibaba, is the most prolific open model family in the world: dozens of sizes from under 1B to 235B parameters, covering chat, coding, vision, audio, and image generation — with new generations (Qwen3, Qwen3.5, Qwen3.8) shipping through 2026.通义千问出自阿里，是全球最多产的开源模型家族：从 1B 以下到 235B 参数的几十个尺寸，覆盖对话、代码、视觉、音频、图像生成——2026 年 Qwen3、Qwen3.5、Qwen3.8 代代相传。

DeepSeek (Hangzhou, 深度求索) took a narrower, sharper path: fewer models, but the V3/R1 line reset industry expectations on reasoning quality per dollar, and its 2026 V3.2 iteration sits in the top tier on public benchmarks.DeepSeek（杭州深度求索）走的是少而精的路线：模型不多，但 V3/R1 系列把"单位成本的推理质量"拉到新高度，2026 年的 V3.2 在公开跑分中位列第一梯队。

Both release open weights and both are free to chat. The real differences show up in model breadth, multimodal strength, and deployment flexibility.两者都开源权重、对话都免费。真正的差异体现在模型广度、多模态能力和部署灵活性上。

Head-to-Head Spec Table核心参数对比表

Dimension对比维度 通义千问 DeepSeek
Latest models最新模型 Qwen3.8-27B, Qwen3.5-27B (vision), Qwen3.8-Flash-NextQwen3.8-27B、Qwen3.5-27B（视觉）、Qwen3.8-Flash-Next V3.2 (chat) / R1 (reasoning)V3.2（对话）/ R1（推理）
Deep reasoning / math深度推理 / 数学 ⭐⭐⭐⭐ strong, hybrid thinking modes强，混合思考模式 ⭐⭐⭐⭐⭐ top-tier (R1)顶尖（R1）
Code ability代码能力 ⭐⭐⭐⭐⭐ Qwen-Coder line, strong at agentic codingQwen-Coder 系列，Agent 编程强 ⭐⭐⭐⭐⭐ excellent at debugging and reasoning-heavy code调试和推理型代码任务优秀
Multimodal多模态 ⭐⭐⭐⭐⭐ native vision (VL), audio, image-gen models in family原生视觉（VL）、音频，家族含生图模型 weak — text-focused弱，以文本为主
Model size ladder尺寸梯队 ⭐⭐⭐⭐⭐ ~0.6B to 235B, runs on phones to clusters约 0.6B 到 235B，手机到集群全覆盖 few sizes, full models need datacenter GPUs尺寸少，完整版需数据中心级显卡
Long-context window长文本上限 256K native, up to ~1M with scaling原生 256K，可扩展至约 1M ~128K约 128K
Open license开源协议 Apache 2.0 (largest models: research license)Apache 2.0（最大尺寸为研究许可） MITMIT
Local deployment本地部署 ⭐⭐⭐⭐⭐ Ollama / vLLM / GGUF, 8GB VRAM minimum for small sizesOllama / vLLM / GGUF，小尺寸 8GB 显存可跑 distilled versions via Ollama; full model heavy蒸馏版可走 Ollama，完整版很重
API pricingAPI 价格 low (Alibaba Cloud), see official site较低（阿里云），以官网为准 ⭐ very low, cache-hit tiers极低，缓存命中分档
Ecosystem生态整合 ⭐⭐⭐⭐⭐ Alibaba Cloud, Tongyi App, ModelScope阿里云、通义 App、魔搭社区 standalone, huge community fine-tunes独立产品，社区微调版本丰富
💡 Model lineups and licenses change fast — check the official model pages before deploying. Figures above reflect public sources as of 2026-10.模型阵容和协议变化快，部署前请以官方模型页为准。以上为截至 2026-10 的公开信息。

Pros & Cons各自优缺点

通义千问

✓ Pros优点

  • Widest size ladder (~0.6B–235B): deploy on phones, PCs, or clusters尺寸梯队最全（约 0.6B–235B），手机、PC、集群都能部署
  • Native vision, audio, and image-gen models — true multimodal family原生视觉、音频、生图模型，是真正的多模态家族
  • Backed by Alibaba Cloud: one-stop API, fine-tuning, and enterprise support背靠阿里云：API、微调、企业支持一站式

✕ Cons缺点

  • Pure reasoning benchmarks slightly trail DeepSeek-R1纯推理跑分略逊于 DeepSeek-R1
  • Largest models carry a research license, not full Apache 2.0最大尺寸模型用研究许可，非完整 Apache 2.0

DeepSeek

✓ Pros优点

  • Top-tier reasoning (R1) — math, debugging, logic at very low cost顶尖推理能力（R1），数学、调试、逻辑极强且成本极低
  • Cheapest capable API on the market with cache-hit tiers市面上最便宜的可用 API，带缓存命中分档
  • MIT license — the most permissive of the major open labsMIT 协议，主流开源大模型中最宽松

✕ Cons缺点

  • Weak multimodal — no native vision or image generation多模态弱，无原生视觉和图像生成
  • Full models need datacenter GPUs; fewer size options完整版需数据中心级显卡，尺寸选择少

Who Should Pick Which?选型建议：分人群看

🎓 Students学生

  • DeepSeek for math/code homework with free step-by-step reasoningDeepSeek 做数理化和代码作业，免费分步推理
  • Qwen for multimodal coursework: reading diagrams, images, and mixed documents通义千问 做多模态课程任务：看图、读图表、处理图文混排文档

💻 Developers开发者

  • DeepSeek API for high-volume batch jobs at the lowest costDeepSeek API 做高频批量任务，成本最低
  • Qwen for local/edge deployment: pick the size that fits your GPU通义千问 做本地/边缘部署：按显卡选尺寸

🏢 Enterprises企业

  • DeepSeek for cost-sensitive production APIs; MIT license simplifies complianceDeepSeek 做成本敏感的线上 API，MIT 协议合规简单
  • Qwen for private cloud deployment on Alibaba Cloud with fine-tuning and support通义千问 上阿里云做私有化部署，带微调和企业支持

Related AI Tools相关 AI 工具

Other tools mentioned in this comparison:本对比中提到的其他工具：

Qwen / 通义千问
Alibaba open LLM family阿里开源大模型家族
DeepSeek
Open-weight LLM (V3/R1), strong reasoning开源大模型（V3/R1），推理能力强
Doubao / 豆包
ByteDance AI assistant字节跳动 AI 助手
Kimi
Moonshot AI's long-context assistant月之暗面长文本智能助手

Frequently Asked Questions常见问题

Which is better for local deployment, Qwen or DeepSeek?本地部署选通义千问还是 DeepSeek？ ▼
Qwen is better for local deployment: it ships in many sizes (from under 1B to 235B parameters) with quantized versions that run on consumer GPUs, while DeepSeek offers fewer size options and its full models need datacenter-grade hardware. Both run via Ollama, vLLM, and Transformers.选通义千问：尺寸从 1B 以下到 235B 全覆盖，量化版可在消费级显卡上运行；DeepSeek 尺寸选择少，完整版需要数据中心级显卡。两者都支持 Ollama、vLLM 和 Transformers。
Which is better at coding, Qwen or DeepSeek?写代码谁更强，通义千问还是 DeepSeek？ ▼
Both are strong. DeepSeek-R1/V3.2 leads on reasoning-heavy coding tasks and debugging, while Qwen's code-specialized models (Qwen-Coder line) are excellent for code generation and agentic coding workflows. Test both on your own codebase before committing.都很强。DeepSeek-R1/V3.2 在推理型代码任务和调试上领先，通义的 Qwen-Coder 系列在代码生成和 Agent 编程流程上出色。建议在自己的代码库上实测后再定。
Are Qwen and DeepSeek both open source?通义千问和 DeepSeek 都开源吗？ ▼
Yes, both release open weights. DeepSeek uses the MIT license, while Qwen models are released under Apache 2.0 (and the Qwen research license for the largest models). Both allow commercial use within their license terms.都开源权重。DeepSeek 用 MIT 协议，通义用 Apache 2.0（最大尺寸为研究许可）。在协议条款内都允许商用。
Which has better image understanding, Qwen or DeepSeek?图像理解谁更强，通义千问还是 DeepSeek？ ▼
Qwen is clearly stronger: its Qwen-VL line has native vision encoders, and the Qwen family extends to image generation models. DeepSeek's chat product has historically been text-focused with weak multimodal support.通义千问明显更强：Qwen-VL 系列有原生视觉编码器，家族还延伸出生图模型。DeepSeek 的对话产品历史上以文本为主，多模态支持较弱。
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