I Tested DeepSeek, Qwen, Kimi, and GLM: Here's What I Found
When I graduated from my coding bootcamp six months ago, I thought I knew the AI landscape pretty well. GPT-4o, Claude, Gemini — those were the names floating around our cohort's Slack channel. Then one weekend, while doomscrolling through some Reddit threads, I stumbled into a whole universe of Chinese AI models I had no idea existed.
Honestly? It blew my mind.
I kept seeing mentions of DeepSeek, Qwen, Kimi, and GLM, but every thread treated them like they were either magic or completely unusable. I couldn't find a single source that just compared them side by side with real numbers. So, being the stubborn bootcamp grad I am, I decided to test them all myself.
What happened over the next few weeks changed how I think about AI pricing forever.
Why I Even Bothered With This
Here's the thing nobody tells you at bootcamp: API costs add up FAST. When I built my first "real" project — a small customer support chatbot for a friend's e-commerce store — I nearly cried when I saw my OpenAI bill at the end of the month. I was spending more on tokens than I was making from freelance gigs.
So when I started seeing whispers about models that cost pennies compared to the Western giants, I had to investigate. And since I had a few weeks between contract jobs, I went full mad scientist mode.
The problem was that each Chinese AI company has its own documentation, its own pricing page, its own SDK quirks. Testing them all individually felt like trying to learn four different programming languages in a weekend. Then someone in a Discord server mentioned Global API, which lets you hit all of these models through a single endpoint. That was the unlock I needed.
The Lay of the Land
Before I dove deep, let me give you the quick rundown. There are four major Chinese AI model families worth knowing about:
- DeepSeek — made by a company called DeepSeek (幻方)
- Qwen — built by Alibaba (阿里), yes, the e-commerce giant
- Kimi — from Moonshot AI (月之暗面)
- GLM — made by Zhipu AI (智谱)
All four offer OpenAI-compatible APIs, which is huge for someone like me who's used the OpenAI Python library to death. I didn't have to learn a new SDK for any of them.
Here's the pricing landscape that genuinely shocked me:
| Family | Price Range |
|---|---|
| DeepSeek | $0.25-$2.50/M tokens |
| Qwen | $0.01-$3.20/M tokens |
| Kimi | $3.00-$3.50/M tokens |
| GLM | $0.01-$1.92/M tokens |
I was shocked to see some of these models priced at literally one cent per million output tokens. That's not a typo. A penny. Per million tokens. I had no idea this existed.
DeepSeek Became My Daily Driver
I started with DeepSeek because, frankly, everyone online was raving about it. And I get it now. Their V4 Flash model at $0.25 per million output tokens is, in my opinion, the single best deal in AI right now.
I ran it through my usual battery of tests. First, I asked it to explain quantum computing in exactly 100 words. Then I gave it some LeetCode-style problems. Then I made it write a basic CRUD API in FastAPI.
The results? Honestly, indistinguishable from GPT-4o for my use cases. And at a fraction of the price.
Here's the full DeepSeek lineup I tested:
| Model | Output $/M | What I Used It For |
|---|---|---|
| V4 Flash | $0.25 | Daily coding, content writing, brainstorming |
| V3.2 | $0.38 | When I wanted the latest architecture |
| V4 Pro | $0.78 | Production work I couldn't afford to mess up |
| R1 (Reasoner) | $2.50 | Hard math problems, logical puzzles |
| Coder | $0.25 | Pure code generation tasks |
The speed also blew my mind. V4 Flash was pushing around 60 tokens per second in my tests, which is among the fastest I've ever used. When I'm building chat interfaces, latency matters, and DeepSeek delivered.
The one thing that frustrated me? Vision capabilities. DeepSeek has limited support for image understanding compared to the others. If you need to throw images at your model, you'll want to look elsewhere.
And on Chinese language tasks, DeepSeek is good, but GLM and Kimi edge it out. I tested this by feeding it some Chinese product descriptions and comparing outputs side by side.
Let me show you the basic DeepSeek setup, which is honestly one of the simplest things I learned during this whole project:
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. That's the whole thing. The same OpenAI library I already knew. I felt silly for being nervous about it.
Qwen Is the Swiss Army Knife I Didn't Know I Needed
If DeepSeek is a sharp, focused tool, then Qwen is that drawer in your kitchen with 47 gadgets in it. Some you use weekly, some you forgot existed, but you're glad they're all there.
Alibaba makes Qwen, and they've gone absolutely wild with the model variants. Let me list what I personally tested:
| Model | Output $/M | What I Used It For |
|---|---|---|
| Qwen3-8B | $0.01 | Tiny classification tasks, keyword extraction |
| Qwen3-32B | $0.28 | My new general-purpose workhorse |
| Qwen3-Coder-30B | $0.35 | Code generation when I needed alternatives |
| Qwen3-VL-32B | $0.52 | Reading screenshots and diagrams |
| Qwen3-Omni-30B | $0.52 | Audio + image + text in one go |
| Qwen3.5-397B | $2.34 | When I needed the big guns for reasoning |
The Qwen3-8B at $0.01 per million output tokens is genuinely absurd. I built a spam classifier with it that costs me basically nothing to run. Like, I checked my dashboard after a month and thought there was a bug because the bill showed $0.00.
But here's what really impressed me: the multimodal stuff. The Qwen3-Omni model handles audio, video, AND images in a single API call. I built a small tool that transcribes voice notes and summarizes them, and it just... worked. When I was at bootcamp, this kind of thing required stitching together three different services.
The downsides? The naming convention is genuinely confusing. Qwen3, Qwen3.5, Qwen3.6 — I lost track of which version was which at 2 AM more times than I'd like to admit. And some of the mid-tier English performance wasn't quite at the level of DeepSeek or the top Western models. Still excellent, just not quite the same.
Also, I want to flag something: Qwen3.6-35B at around $1 per million output tokens felt steep to me. The smaller Qwen3 models give you much better value unless you specifically need that tier.
Here's how I use Qwen3-32B for general-purpose coding tasks:
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 the "Qwen/" prefix? That's how Global API namespaces models from different providers. Once you know that pattern, switching between models is as easy as changing a string.
Kimi Made Me Feel Things About Math Again
Okay, real talk — I hated math in school. I barely survived calculus. But Kimi's reasoning capabilities made me feel like maybe my high school math teacher just wasn't using the right tools.
The Kimi K2.5 model at $3.00 per million output tokens is the priciest in my regular rotation, but holy cow does it earn its keep on hard problems. I gave it a logic puzzle that I genuinely couldn't solve, and it walked through the reasoning step by step until I understood. I had no idea an AI could explain Gödel's incompleteness theorem in a way that actually clicked for me.
Kimi sits in the $3.00-$3.50 per million token range, which puts it firmly in "premium" territory. The company behind it is Moonshot AI (月之暗面), which is a way cooler name than I'll ever come up with for a startup.
Here's what I noticed during testing:
- It absolutely dominated reasoning benchmarks. If you need a model to think deeply about something, this is your pick.
- Chinese language performance is elite. Probably tied with GLM for the top spot.
- Speed was the trade-off. It's noticeably slower than DeepSeek or Qwen's smaller models. For real-time chat, that matters.
- No vision support, which is a bummer for multimodal workflows.
For my bootcamp-style projects, I don't reach for Kimi often because of the price. But when I do, it's because the task genuinely requires that level of reasoning depth. And every time, it's delivered.
GLM Is the Quiet Champion You Might Be Sleeping On
Zhipu AI's GLM lineup was honestly the biggest surprise of my entire testing journey. I went in expecting it to be a solid-but-boring option. I came out completely rethinking my whole stack.
The price range is wild: $0.01 to $1.92 per million output tokens. The GLM-4-9B at $0.01 is in that absurd-cheap tier alongside Qwen3-8B. I built a content moderation helper with it that runs 24/7 and costs me literal pocket change.
Their flagship, GLM-5 at $1.92 per million output tokens, competes directly with much pricier Western models. And their GLM-4.6V vision model handled every image-related task I threw at it.
Here's my honest breakdown:
| Model | Output $/M | Sweet Spot |
|---|---|---|
| GLM-4-9B | $0.01 | Lightweight tasks, high volume |
| GLM-5 | $1.92 | Production-quality general work |
What genuinely impressed me:
- Chinese language understanding is genuinely best-in-class. If you're building anything for the Chinese market, this is your pick.
- Vision support through GLM-4.6V was solid. It correctly read a hand-drawn diagram I uploaded, which GPT-4o had struggled with.
- The context window goes to 128K tokens, same as the others.
- API is OpenAI-compatible, so no migration headaches.
The downsides? It's not as fast as DeepSeek. And the English language performance, while good, doesn't quite hit the heights of DeepSeek for me.
What I Actually Use Now
After all this testing, here's my honest stack:
For daily coding and content work, I default to DeepSeek V4 Flash. The $0.25/M price is unbeatable for what I get.
For when I need vision or multimodal capabilities, I reach for Qwen. The Qwen3-VL and Qwen3-Omni models cover basically every use case I have.
For deep reasoning tasks — math, logic puzzles, anything where I need the model to actually think — I pay the premium for Kimi K2.5. It's worth every penny when I need it.
For Chinese-language projects and high-volume lightweight tasks, GLM is my pick. The $0.01/M GLM-4-9B is almost too good to be true.
The Stuff Nobody Talks About
A few things I learned that surprised me:
All four providers use OpenAI-compatible APIs. This means switching between them is just changing the model name string. That's HUGE for anyone worried about vendor lock-in.
The context windows are all 128K. So no compromises there.
Speed varies a lot. DeepSeek is fast, Kimi is slow. Plan accordingly.
Pricing changes frequently. The numbers I listed above were accurate when I tested, but always check the current pricing before committing.
Global API made all of this possible. Instead of signing up for four different platforms, managing four different API keys, and dealing with four different billing systems, I use one endpoint at global-apis.com/v1 and access all of them. If you're a solo dev or small team, this is a game-changer.
Final Thoughts From a Grumpy Bootcamp Grad
Six months ago, I thought I had the AI landscape figured out. I was wrong, and I'm glad I was.
The Chinese AI ecosystem isn't just "cheap alternatives" — it's a genuinely competitive landscape with models that excel in different areas. DeepSeek on price, Qwen on versatility, Kimi on reasoning, GLM on Chinese language tasks. Each one has carved out a space.
If you're a developer trying to save money without sacrificing quality, you owe it to yourself to test these out. I dropped my monthly API bill by about 70% just by switching my default model, and I haven't looked back.
If
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