I Tested DeepSeek vs Qwen vs Kimi vs GLM — Here’s the Winner

我测试了 DeepSeek、Qwen、Kimi 和 GLM — 最终赢家是谁？

So here’s what happened: I’ve been on this absolute rabbit hole for the past few weeks, and I have to share what I’ve found. You know how everyone’s been talking about GPT-4o and Claude, but there’s this whole other universe of Chinese AI models that are honestly punching way above their weight? Yeah, I went deep into it. Let me walk you through what I learned. 事情是这样的：过去几周我一直在深入研究这个领域，必须和大家分享我的发现。大家都知道 GPT-4o 和 Claude 很火，但其实还有一个中国 AI 模型的世界，它们的表现远超预期。没错，我深入研究了它们。让我带你了解一下我的心得。

If you’ve ever stared at a pricing page wondering which model to actually use for your side project, your startup’s chatbot, or that one client who’s been asking about cheaper alternatives — this is for you. I spent hours testing DeepSeek, Qwen, Kimi, and GLM through Global API’s unified endpoint, and I’m going to break it all down for you. No fluff, no marketing speak, just what actually works. 如果你曾盯着定价页面，纠结该为你的副业项目、初创公司的聊天机器人，或者那位询问更便宜替代方案的客户选择哪种模型——这篇文章就是为你准备的。我花了数小时通过 Global API 的统一端点测试了 DeepSeek、Qwen、Kimi 和 GLM，现在我将为你详细拆解。没有废话，没有营销术语，只有真正好用的东西。

Why I Even Started Looking at Chinese Models

为什么我开始关注中国模型？

Let me be honest with you — I was skeptical at first. My mental model was “Western models = good, Chinese models = questionable.” Then a friend who runs a SaaS startup told me he cut his API bill by 80% by switching to DeepSeek for non-critical workloads. Eighty percent! I had to see for myself. 老实说，起初我是持怀疑态度的。我原本的认知是“西方模型 = 好，中国模型 = 有待商榷”。后来，一位经营 SaaS 初创公司的朋友告诉我，他通过将非关键工作负载切换到 DeepSeek，将 API 账单削减了 80%。80%！我必须亲自验证一下。

The thing is, China’s AI scene has exploded in the last couple of years. You’ve got four major players — DeepSeek from High-Flyer (幻方), Qwen from Alibaba (阿里), Kimi from Moonshot AI (月之暗面), and GLM from Zhipu AI (智谱) — and each one has its own personality, if you will. Some are great at coding, some are reasoning beasts, and some just refuse to break the bank. I figured the best way to compare them was to actually run the same prompts through all of them and see what happens. That’s exactly what I did, and here’s how it went. 事实上，中国的 AI 领域在过去几年里爆发式增长。目前有四大巨头——来自幻方（High-Flyer）的 DeepSeek、来自阿里（Alibaba）的 Qwen、来自月之暗面（Moonshot AI）的 Kimi，以及来自智谱（Zhipu AI）的 GLM。它们各有千秋：有的擅长编程，有的推理能力极强，有的则主打高性价比。我认为比较它们的最好方法就是用相同的提示词（Prompts）进行测试。我正是这样做的，以下是测试结果。

The TL;DR (For the Impatient Folks)

总结（给没耐心的读者）

I’ll give you the punchline upfront because I know some of you are skimming: 我知道有些人喜欢直接看结论，所以先给出核心观点：

• DeepSeek V4 Flash — absolute champion of price-to-performance at $0.25/M output. DeepSeek V4 Flash — 性价比之王，输出价格仅为 $0.25/百万 token。
• Qwen — widest range of models, from $0.01/M all the way up to $3.20/M. Qwen — 模型选择最丰富，价格从 $0.01/百万 token 到 $3.20/百万 token 不等。
• Kimi — the reasoning specialist, leads benchmarks, but you’ll pay $3.00-$3.50/M. Kimi — 推理专家，基准测试领先，但价格在 $3.00-$3.50/百万 token。
• GLM — Chinese-language tasks are its superpower, with GLM-5 at $1.92/M. GLM — 中文任务是其强项，GLM-5 价格为 $1.92/百万 token。

If you want one model that does everything well without emptying your wallet? DeepSeek V4 Flash. If you want a reasoning powerhouse and don’t mind spending? Kimi K2.5. If you need every size under the sun? Qwen. And if you’re building for Chinese users? You already know — GLM. 如果你想要一个全能且不掏空钱包的模型？选 DeepSeek V4 Flash。如果你需要强大的推理能力且不介意成本？选 Kimi K2.5。如果你需要各种尺寸的模型？选 Qwen。如果你是为中国用户开发产品？你应该知道选谁——GLM。

Quick Reference Table (Bookmark This)

快速参考表（建议收藏）

Feature	DeepSeek	Qwen	Kimi	GLM
Developer	DeepSeek (幻方)	Alibaba (阿里)	Moonshot AI (月之暗面)	Zhipu AI (智谱)
Price Range	$0.25-$2.50/M	$0.01-$3.20/M	$3.00-$3.50/M	$0.01-$1.92/M
Best Budget	V4 Flash @ $0.25/M	Qwen3-8B @ $0.01/M	N/A	GLM-4-9B @ $0.01/M
Best Overall	V4 Flash @ $0.25/M	Qwen3-32B @ $0.28/M	K2.5 @ $3.00/M	GLM-5 @ $1.92/M
Code Gen	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐
Chinese	⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐
English	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐
Reasoning	⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐
Speed	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐⭐
Vision	Limited	✅ (VL, Omni)	❌	✅ (GLM-4.6V)
Context	Up to 128K	Up to 128K	Up to 128K	Up to 128K

API Compatibility

API 兼容性

OpenAI ✅ All four are OpenAI-compatible, which is huge. It means you don’t have to learn a new SDK or rewrite your entire codebase to switch between them. Just change the model name and maybe the base URL. Speaking of which… 这四家都兼容 OpenAI 接口，这非常重要。这意味着你无需学习新的 SDK 或重写整个代码库即可在它们之间切换。只需更改模型名称，可能还需要更改基础 URL。说到这个……

Let Me Show You How the Setup Works

让我展示一下如何配置

Before I dive into each model family, here’s a quick code snippet so you can follow along. I tested everything through Global API’s unified endpoint, which makes life a million times easier. 在深入介绍每个模型系列之前，这里有一个简单的代码片段供你参考。我通过 Global API 的统一端点进行了所有测试，这让工作变得极其简单。

from openai import OpenAI

client = OpenAI(
    api_key="ga_xxxxxxxxxxxx",
    base_url="https://global-apis.com/v1"
)

response = client.chat.completions.create(
    model="deepseek-v4-flash",
    messages=[{"role": "user", "content": "Explain quantum computing in 100 words"}]
)

print(response.choices[0].message.content)

See how clean that is? Same OpenAI SDK you’re already used to, just a different base URL. The model parameter is where you swap things out. I love this because I can A/B test models in seconds without juggling multiple API keys or SDKs. 看，这多简洁？使用你已经熟悉的 OpenAI SDK，只是换了个基础 URL。模型参数就是你切换模型的地方。我非常喜欢这一点，因为我可以在几秒钟内进行 A/B 测试，而无需处理多个 API 密钥或 SDK。

DeepSeek: My New Go-To for Most Things

DeepSeek：我目前的首选

Alright, let’s start with my favorite. DeepSeek came out of nowhere and just started dominating benchmarks, and honestly, I’ve been reaching for it constantly. 好了，先从我最喜欢的开始。DeepSeek 横空出世并开始在基准测试中占据主导地位，老实说，我现在经常使用它。

The thing that blew my mind was V4 Flash at $0.25 per million output tokens. That’s incredibly cheap for the quality you’re getting. I ran it through some of my usual tests and it held its own against models that cost 4-5x more. 最让我震惊的是 V4 Flash 的价格：每百万输出 token 仅需 $0.25。考虑到它的质量，这简直便宜得不可思议。我用常规测试跑了一下，它的表现完全不输给那些价格高出 4-5 倍的模型。

What I Love About DeepSeek: 我喜欢 DeepSeek 的地方：

• Insane price-to-performance — V4 Flash at $0.25/M genuinely rivals GPT-4o quality for most tasks. 极致性价比 — V4 Flash 在大多数任务中确实能媲美 GPT-4o 的质量。
• Coding champion — Consistently nails HumanEval and MBPP benchmarks. 编程冠军 — 在 HumanEval 和 MBPP 基准测试中表现稳定。
• Blazing fast — V4 Flash pushes around 60 tokens/sec. 速度极快 — V4 Flash 每秒可输出约 60 个 token。
• English is excellent — You wouldn’t know it’s a Chinese model. 英语水平出色 — 你根本看不出这是一个中国模型。

Where It Falls Short: 不足之处：

• No vision capabilities — You’re stuck with text-only. 没有视觉能力 — 仅限于文本。
• Chinese is good but not the best — GLM and Kimi edge it out slightly. 中文能力尚可但非顶尖 — GLM 和 Kimi 在中文基准测试上略胜一筹。