I Pitted China's Best Open AI Models Against Each Other
Last month I did something that probably annoyed a few of my colleagues. I ripped out every OpenAI and Anthropic call from my side projects and replaced them with Chinese open-weight models running through Global API. Not because I have some chip on my shoulder about Silicon Valley — although, honestly, the vendor lock-in stuff does grind my gears — but because the math finally made sense.
I've been burned too many times by API price hikes and sudden "deprecations" of models I depended on. When your entire production stack runs on someone else's proprietary, closed source, walled garden, you're one pricing email away from disaster. The Chinese labs — DeepSeek, Qwen, Kimi, and GLM — are doing something fundamentally different. They're publishing weights, releasing under Apache and MIT licenses in many cases, and competing hard on price. So I decided to actually test them all, head to head, with real workloads.
Here's what I found after weeks of running them through code, reasoning, and language benchmarks.
Let me get this off my chest before diving in. I have nothing against commercial AI. I use plenty of proprietary tools in my daily work. But there's a specific kind of frustration that builds up when you're three months into a project and a vendor decides to bump prices 40% overnight, retire a model you depend on, or throttle your rate limits because you got popular.
That's the world of the walled garden. You get convenience, sure. But you give up control.
The Chinese labs are playing a different game. DeepSeek publishes papers alongside their models. Qwen releases under Apache 2.0 in many cases. GLM is genuinely committed to open research. Kimi — well, Kimi is a bit more closed, but the pricing is aggressive enough that I gave them a fair shot. When I can download a model, inspect its weights, fine-tune it, and deploy it but I want, that's freedom. That's the kind of freedom Apache and MIT licenses were designed to protect.
So when I found out Global API offered unified access to all four of these families through one OpenAI-compatible endpoint, I cleared my calendar.
Before the results, let me share how I actually evaluated these models. I didn't just eyeball outputs and go "feels good." I built a small benchmark suite:
I routed everything through https://global-apis.com/v1
because honestly, juggling four different SDKs and authentication schemes sounded miserable. The OpenAI-compatible format meant I could swap model names in my existing code with maybe a five-minute migration.
Here's the kind of snippet I was running dozens of times:
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": "Write a Python function that flattens a nested dictionary. Include type hints and handle edge cases."
}]
)
print(response.choices[0].message.content)
print(f"Tokens used: {response.usage.total_tokens}")
That same script, with a one-word change to the model name, let me test all four families. Beautiful.
Here's the overview before I dig into each family. I've ranked them on the dimensions that actually mattered to my work:
| Dimension | DeepSeek | Qwen | Kimi | GLM |
|---|---|---|---|---|
| Price Range (per 1M output tokens) | ||||
| $0.25–$2.50 | $0.01–$3.20 | $3.00–$3.50 | $0.01–$1.92 | |
| Best Budget Pick | ||||
| V4 Flash at $0.25 | Qwen3-8B at $0.01 | None — premium only | GLM-4-9B at $0.01 | |
| Best Overall | ||||
| V4 Flash at $0.25 | Qwen3-32B at $0.28 | K2.5 at $3.00 | GLM-5 at $1.92 | |
| Code Generation | ||||
| ★★★★★ | ★★★★ | ★★★★ | ★★★ | |
| Chinese Language | ||||
| ★★★★ | ★★★★ | ★★★★★ | ★★★★★ | |
| English Language | ||||
| ★★★★★ | ★★★★ | ★★★★ | ★★★★ | |
| Reasoning | ||||
| ★★★★ | ★★★★ | ★★★★★ | ★★★★ | |
| Speed | ||||
| ★★★★★ | ★★★★ | ★★★ | ★★★★ | |
| Vision/Multimodal | ||||
| Limited | ✅ (VL, Omni) | ❌ | ✅ (GLM-4.6V) | |
| Context Window | ||||
| Up to 128K | Up to 128K | Up to 128K | Up to 128K | |
| API Compatibility | ||||
| OpenAI ✅ | OpenAI ✅ | OpenAI ✅ | OpenAI ✅ | |
| Open Weights / License | ||||
| Yes (custom permissive) | Yes (Apache 2.0 for many) | Partial | Yes (mostly permissive) |
A few things jump out immediately. First, every single one of these models is OpenAI-compatible at the API level. Second, every single one supports a 128K context window, which is wild — that's GPT-4o territory at a fraction of the cost. Third, the pricing spread is enormous. Qwen3-8B at $0.01 per million output tokens is basically free, and Kimi's top model at $3.50 is genuinely premium-priced.
Let me start with DeepSeek because it became my daily driver and probably will surprise you most. DeepSeek is built by High-Flyer (幻方), a Chinese quantitative hedge fund. They have this refreshing habit of publishing their research alongside their model releases — proper papers, proper benchmarks, proper methodology. It's the kind of transparency you almost never see from closed source shops.
| Model | Output $/M | What I Used It For |
|---|---|---|
| V4 Flash | ||
| $0.25 | Daily coding, content, quick questions | |
| V3.2 | $0.38 | Latest architecture experiments |
| V4 Pro | $0.78 | When I needed higher quality |
| R1 (Reasoner) | $2.50 | Hard math, complex logic chains |
| Coder | $0.25 | Pure code generation |
The headline number is $0.25 per million output tokens for V4 Flash. To put that in perspective, GPT-4o is roughly 40x more expensive for comparable quality on English tasks. That's not a typo. Forty times. When you're running a chatbot that processes a million tokens a day, you're suddenly talking about the difference between a $7.50 monthly bill and a $300 monthly bill.
Speed was the second shock. V4 Flash was hitting around 60 tokens per second in my tests, which made it the fastest of the four families. For interactive applications — chatbots, autocomplete, IDE plugins — that latency matters.
Code generation is where DeepSeek genuinely impressed me. I ran the HumanEval and MBPP suites and DeepSeek's V4 Flash was consistently in the top tier. The Coder variant is even better but honestly, for most tasks V4 Flash was good enough.
Vision is the obvious gap. DeepSeek doesn't ship a strong multimodal model in the same way Qwen and GLM do. If you need to analyze images, you'll want to look elsewhere.
Chinese language performance is also slightly behind Kimi and GLM, though "slightly" is doing a lot of work here. DeepSeek is still excellent at Chinese — it's just not the best.
Finally, the model variety is narrower than Qwen. Qwen has a model for literally every niche. DeepSeek keeps things tighter, which some people will appreciate and others will find limiting.
Here's the actual code I run daily for coding tasks:
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": "system", "content": "You are a senior Python developer."},
{"role": "user", "content": "Refactor this function to use asyncio."}
]
)
That's it. That's the whole migration story from OpenAI. The base URL change and the model name. Everything else stays the same.
If DeepSeek is a precision tool, Qwen is a Swiss Army knife. Alibaba's Qwen team has built the widest range of models in this space, period. They have models for almost every use case you can imagine, from tiny 8B parameter models all the way up to massive 397B flagship models.
| Model | Output $/M | Sweet Spot |
|---|---|---|
| Qwen3-8B | $0.01 | Ultra-lightweight tasks |
| Qwen3-32B | $0.28 | General purpose workhorse |
| Qwen3-Coder-30B | $0.35 | Code-focused work |
| Qwen3-VL-32B | $0.52 | Image understanding |
| Qwen3-Omni-30B | $0.52 | Audio, video, image, text |
| Qwen3.5-397B | $2.34 | Enterprise reasoning |
The pricing floor is the headline. Qwen3-8B at $0.01 per million output tokens is essentially free. I tested it for simple classification and extraction tasks, and for those lightweight jobs, it's totally sufficient. If you're processing thousands of support tickets or doing bulk data labeling, this is your model.
Then there's the multimodal story. Qwen3-VL handles vision tasks well, and Qwen3-Omni goes further — it handles audio, video, and images alongside text in a single model. For anyone building agents that need to see and hear, Qwen is the obvious pick.
Alibaba's enterprise infrastructure backing shows too. The latency was consistent, the rate limits were generous, and the uptime was solid throughout my testing. They also release frequently — Qwen3.5, Qwen3.6 — which means the models keep getting better.
The naming is genuinely confusing. Qwen3, Qwen3.5, Qwen3-Coder, Qwen3-VL, Qwen3-Omni. I had to keep a spreadsheet to track which model did what. It feels like every Qwen release spawns three sub-models and a decoder ring.
English performance is good but not DeepSeek-level. For pure English tasks, DeepSeek's V4 Flash edged it out in my tests. And some models feel overpriced relative to the competition — Qwen3.6-35B at $1 per million output is steep when GLM-5 at $1.92 covers a wider range.
Whenever I need multimodal capabilities, basically. Here's a typical call:
response = client.chat.completions.create(
model="Qwen/Qwen3-VL-32B",
messages=[{
"role": "user",
"content": [
{"type": "text", "text": "What's in this image?"},
{"type": "image_url", "image_url": {"url": "https://example.com/photo.jpg"}}
]
}]
)
Same OpenAI format, same Global API base URL. The migration story is identical.
Kimi comes from Moonshot AI (月之暗面), and their positioning is clear: they want to win on reasoning. If you give Kimi a complex multi-step problem — math, logic, planning — it tends to outperform the others. The trade-off is price and speed.
| Model | Output $/M | Sweet Spot |
|---|---|---|
| K2.5 | $3.00 | Top-tier reasoning |
| K2 Max | $3.50 | The flagship |
Reasoning benchmarks. Full stop. When I ran logic puzzles and multi-step math problems, Kimi was consistently the most accurate. If you're building an agent that needs to plan, decompose problems, or do chain-of-thought work, Kimi is genuinely the best of the four families.
Chinese language handling is also exceptional. Kimi treats Chinese as a first-class citizen, not an afterthought. For Chinese-language production workloads, it's tied with GLM at the top.
The price is the obvious issue. At $3.00 to $3.50 per million output tokens, Kimi is premium-priced. For most workloads, you're paying for reasoning capability you don't actually need. Kimi also lacks vision and multimodal support entirely, which limits its use cases.
Speed is the other concern. Kimi was the slowest of the four families in my testing. For interactive applications, that latency can be a dealbreaker.
Kimi is also the most closed of the bunch. While they've published some technical details, the weights aren't as freely available as DeepSeek or Qwen. For an open source purist, that's a meaningful gap.
GLM comes from Zhipu AI (智谱), and I think they're the most underrated player in this space. They don't get the headlines that DeepSeek and Qwen do, but their models are excellent, especially for Chinese-language work.
| Model | Output $/M | Sweet Spot |
|---|---|---|
| GLM-4-9B | $0.01 | Budget tasks |
| GLM-5 | $1.92 | Flagship |
Chinese language is GLM's crown jewel. For pure Chinese-language tasks, GLM-5 is tied with Kimi for the top spot. If your user base is Chinese-speaking, GLM deserves a serious look.
GLM also has vision capabilities through GLM-4.6V, which closes the gap with Qwen's multimodal offerings. And the pricing — $0.01 for the budget model and $1.