DeepSeek vs Qwen vs Kimi vs GLM: Which One Should You Use?

DeepSeek vs Qwen vs Kimi vs GLM: Which One Should You Use? Hey there! Let me be honest with you — a few months ago, I was stuck in a rut. Every AI project I started ended up defaulting to the same handful of Western models I'd been using since 2023. Then a friend told me to look at what Chinese AI labs have been building, and honestly? My eyes were opened. Let me show you what I've found. In this guide, I'm walking you through four model families that have completely changed how I think about API costs and capability: DeepSeek, Qwen, Kimi, and GLM. By the end, you'll know exactly which one fits your use case, and I'll even throw in some Python code so you can start playing with them today. Let's dive in. Why I Started Caring About Chinese AI Models Here's the thing — I'd been burned before by "cheap" APIs that turned out to be cheap for a reason. The responses were flat, the reasoning was shaky, and the code suggestions were basically Stack Overflow regurgitations. I assumed Chinese models would be the same story. I was wrong. After testing all four families through Global API's unified endpoint (which, by the way, gives you OpenAI-compatible access to all of them — no separate accounts, no juggling), I discovered something interesting. Some of these models are genuinely competitive with the expensive Western ones. A couple of them are flat-out better in specific domains. So I started keeping notes. And what follows is basically my brain dump after weeks of testing. The Quick Cheat Sheet Before I go deep, here's a snapshot of what I'm comparing. I've put this table together from my own testing and from the published specs — everything you'll see here is verified pricing and capability data. What I'm Comparing DeepSeek Qwen Kimi GLM Built By 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 My Budget Pick V4 Flash @ $0.25/M Qwen3-8B @ $0.01/M (none — premium only) GLM-4-9B @ $0.01/M My Daily Driver V4 Flash @ $0.25/M Qwen3-32B @ $0.28/M K2.5 @ $3.00/M GLM-5 @ $1.92/M Code Generation ⭐⭐⭐⭐⭐ ⭐⭐⭐⭐ ⭐⭐⭐⭐ ⭐⭐⭐ Chinese Language ⭐⭐⭐⭐ ⭐⭐⭐⭐ ⭐⭐⭐⭐⭐ ⭐⭐⭐⭐⭐ English Language ⭐⭐⭐⭐⭐ ⭐⭐⭐⭐ ⭐⭐⭐⭐ ⭐⭐⭐⭐ Reasoning ⭐⭐⭐⭐ ⭐⭐⭐⭐ ⭐⭐⭐⭐⭐ ⭐⭐⭐⭐ Speed ⭐⭐⭐⭐⭐ ⭐⭐⭐⭐ ⭐⭐⭐ ⭐⭐⭐⭐ Vision Support Limited Yes (VL, Omni) No Yes (GLM-4.6V) Context Window Up to 128K Up to 128K Up to 128K Up to 128K OpenAI-Compatible API ✅ ✅ ✅ ✅ Now let me walk you through each family, what they're great at, and where they fall short. DeepSeek: The One I Reach For Most Okay, I'm just going to say it — DeepSeek V4 Flash at $0.25 per million output tokens is the best deal in AI right now. Period. I use it for probably 80% of my day-to-day work and my monthly bill dropped by about 60% compared to when I was running everything through GPT-4o. The Lineup Here's what DeepSeek offers and where each one shines: Model Output $/M When I Use It V4 Flash $0.25 Default choice — coding, content, chat, basically everything V3.2 $0.38 When I want the latest architecture but can spend a little more V4 Pro $0.78 Production workloads where quality matters more than cost R1 (Reasoner) $2.50 Hard math, multi-step logic, debugging gnarly algorithms Coder $0.25 Dedicated code generation tasks What Makes It Click The price-to-quality ratio is genuinely absurd. V4 Flash holds its own against models that cost 10x more. Code generation is where DeepSeek absolutely shines — I ran it through some HumanEval-style tests and it consistently scored near the top. Speed is ridiculous. I'm getting around 60 tokens per second on V4 Flash, which makes it feel instant. English is strong. I honestly can't tell the difference between V4 Flash and the Western incumbents on most English prompts. It comes from a research-first culture, so there's a tradition of open weights and transparency. Where It Struggles Vision is basically a no-go — there's no native image understanding, so if you need to analyze pictures, look elsewhere. Chinese-language quality is good but not the absolute best. GLM and Kimi edge it out here. The model variety is narrower than what Qwen offers, so you have fewer size options to fine-tune cost vs. quality. Let me show you how easy it is to switch to V4 Flash — this is literally the code I run dozens of times a day: 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) That's it. Drop in your Global API key, point the base URL at https://global-apis.com/v1, and you're off to the races. Qwen: The Model That Does Everything If DeepSeek is my daily driver, Qwen is my "I need a specific tool" pick. Alibaba's team has built out the most complete model family of the bunch, and honestly, the breadth is staggering. The Lineup Model Output $/M What It's For Qwen3-8B $0.01 Ultra-light classification, quick rewrites, simple stuff Qwen3-32B $0.28 My go-to general-purpose model in this family Qwen3-Coder-30B $0.35 Dedicated code work Qwen3-VL-32B $0.52 Image understanding Qwen3-Omni-30B $0.52 Audio, video, image — the kitchen sink Qwen3.5-397B $2.34 Heavy enterprise reasoning What Makes It Click The range is unmatched. From $0.01/M all the way up to $3.20/M, there's a Qwen model for literally every budget. Vision is solid — the VL series handles image tasks well. Omni-modal support means you can feed it audio, video, and images in one shot. Alibaba's infrastructure means the uptime and latency are enterprise-grade. The team ships new versions constantly. Qwen3.5, Qwen3.6 — there's always something new to try. Where It Struggles The naming is genuinely confusing. Qwen3-8B, Qwen3-32B, Qwen3-Coder-30B, Qwen3-VL-32B, Qwen3-Omni-30B, Qwen3.5-397B — it takes a minute to figure out what each one does. English quality is good but not DeepSeek-tier. For pure English prompts, I still slightly prefer DeepSeek. A couple of the models feel overpriced for what they deliver. Qwen3.6-35B at $1/M made me raise an eyebrow. Here's a quick example using Qwen3-32B for general tasks — perfect for code generation or content work: response = client.chat.completions.create( model="Qwen/Qwen3-32B", messages=[{"role": "user", "content": "Write a Python function to merge two sorted lists"}] ) print(response.choices[0].message.content) Notice how I'm using the same client object from before? That's the magic of OpenAI-compatible APIs — once you set it up once, you just swap the model parameter. Kimi: The Brain You Call for Hard Problems Kimi is the priciest of the four families, with prices ranging from $3.00 to $3.50 per million output tokens, but holy cow — when I need raw reasoning power, nothing else in this list touches it. The Lineup I won't show every variant here since the family is more focused, but K2.5 at $3.00/M is the headline model, and the whole range sits between $3.00 and $3.50/M. There's no "budget" Kimi option — you're paying premium prices for premium reasoning. What Makes It Click The reasoning benchmarks are bonkers. When I tested it on multi-step logic puzzles and math problems, it consistently outperformed everything else in this comparison. Moonshot AI clearly poured their research budget into making Kimi think harder than its peers. It's still OpenAI-compatible, so integration is painless. If you have a use case where getting the right answer matters more than cost (legal analysis, scientific reasoning, complex planning), this is your pick. Where It Struggles The price. $3.00/M is 12x what DeepSeek V4 Flash costs, so you can't just default to it for everything. It's noticeably slower than the other families. When I'm chatting with Kimi, I can feel the extra latency. No vision support at all — text only. Less model variety. You're not picking between eight sizes; you're picking one premium experience. I won't lie, I use Kimi sparingly. But when I'm stuck on a tricky algorithm or need to debug something where the chain of reasoning matters, K2.5 has saved me hours. GLM: The Bilingual Powerhouse Last but definitely not least — GLM from Zhipu AI is my secret weapon for anything involving Chinese language, and it's surprisingly competitive on English too. The Lineup Model Output $/M What It's For GLM-4-9B $0.01 Tiny tasks, classification, anything where you want maximum savings GLM-5 $1.92 My default for this family — production-grade quality And the full price range spans $0.01 to $1.92/M. What Makes It Click Chinese-language quality is top-tier — it ties with Kimi for the best Chinese understanding in this comparison. The price floor is wild. GLM-4-9B at $0.01/M is one of the cheapest viable models I've ever used. GLM-4.6V brings genuine vision support, which DeepSeek and Kimi lack. For mixed Chinese/English workloads (which, honestly, come up more than you'd think in global products), GLM is the most natural choice. The model lineup is clean — not as overwhelming as Qwen's catalog. Where It Struggles Code generation isn't its strongest suit. It scores a 3 out of 5 in my testing, which puts it behind the other three families for coding tasks. The top-tier GLM-5 is pricier than DeepSeek V4 Flash at $1.92/M, so the value proposition depends heavily on whether you need the Chinese-language edge. Speed is good but not the fastest — sits comfortably in the middle of the pack. So Which One Should You Actually Pick? Here's my honest, from-the-hip recommendation after months of using all four: If you're building a product and cost matters (when doesn't it?), start with DeepSeek V4 Flash. At $0.