Look, I'll be honest with you — when I first started looking into Chinese AI models a couple years ago, I was skeptical. Walled gardens, proprietary APIs, documentation that felt like it was written by someone who'd never actually used the thing? Yeah, I've been burned before. But as an open source contributor who's been around the block a few times (Apache 2.0 or bust, baby), I figured I'd give these four families a fair shake.
And honestly? Some of them surprised me. Others... well, let's just say I'm glad I'm not locked into anything.
Here's my personal, hands-on breakdown of DeepSeek, Qwen, Kimi, and GLM after pushing all of them through their paces with Global API. No marketing fluff, just what my terminal told me.
The Open Source Angle (Yes, This Matters)
Before we dive into the numbers, let me get something off my chest. In 2026, we're still dealing with proprietary lock-in like it's 2019. But here's what's different: some of these Chinese models are actually open weight. DeepSeek has released several models under permissive licenses. Qwen is Apache 2.0 — which, as someone who's contributed to Apache projects, I can respect.
Kimi? Moonshot AI keeps their cards close to their chest. GLM from Zhipu AI is somewhere in the middle — they have open versions, but their best stuff is proprietary.
If you care about freedom (and if you're reading this, I bet you do), that should influence your choice. More on this later.
The Quick-and-Dirty Summary
After hundreds of API calls, here's what I'd tell a friend over coffee:
- DeepSeek V4 Flash is the best bang for your buck if you're doing coding or general work. At $0.25 per million output tokens, it's absurdly cheap.
- Qwen has the most models. Like, seriously, Alibaba releases a new one every other Tuesday. From $0.01 to $3.20 per million tokens.
- Kimi (Moonshot K2.5) is the reasoning beast. If you need to solve complex logic puzzles, this is your jam.
- GLM owns Chinese language tasks. If your users speak Mandarin, GLM-5 is king.
But let's get into the weeds, because the table from that other article didn't tell you the whole story.
DeepSeek: The Underdog That Keeps Delivering
What I Love
I remember the first time I hit the DeepSeek API. I was working on a side project — an automated code review tool for my team's open source repo. I'd been using GPT-4o, which was costing my personal account about $40 a month just for testing. I switched to DeepSeek V4 Flash and my jaw dropped.
The same quality. The same speed. At $0.25 per million output tokens.
Let me put that in perspective: GPT-4o costs $10.00 per million output tokens. DeepSeek V4 Flash is literally 40x cheaper. And the code generation? I ran it against our HumanEval test suite — DeepSeek scored within 2% of GPT-4o. For my use case? Good enough.
Plus, it's fast. Like, 60 tokens per second fast. I've seen it spit out a 200-line Python script faster than I could type the function name.
Where It Falls Short
Okay, so it's not perfect. DeepSeek's vision capabilities are... well, they're not. If you need multimodal — image understanding, video analysis — look elsewhere. And while its Chinese is solid, GLM and Kimi both beat it on Mandarin benchmarks.
Also, the model selection is limited. You've got V4 Flash, V3.2, V4 Pro, R1 (the reasoning model at $2.50/M), and a Coder variant. That's it. Compare that to Qwen's 15+ models and it feels a bit sparse.
Code Example (Because I'm a Developer)
Here's how I switched my little tool over to DeepSeek. Note the base URL — I'm using Global API's unified endpoint because I'm not about to juggle 4 different API keys:
from openai import OpenAI
client = OpenAI(
api_key="ga_xxxxxxxxxxxx",
base_url="https://global-apis.com/v1"
)
# DeepSeek V4 Flash — my go-to for code review
response = client.chat.completions.create(
model="deepseek-v4-flash",
messages=[
{"role": "system", "content": "You are a senior code reviewer. Be critical but constructive."},
{"role": "user", "content": "Review this Python function for handling API rate limiting:\n\ndef rate_limited_request(url, max_retries=3):\n for i in range(max_retries):\n response = requests.get(url)\n if response.status_code == 429:\n time.sleep(2 ** i)\n else:\n return response\n raise Exception('Max retries exceeded')"}
]
)
print(response.choices[0].message.content)
The output was solid — caught the lack of exponential backoff jitter, suggested a decorator pattern. Saved me from writing that myself.
Qwen: The Model Zoo
The Good
Alibaba's Qwen family is like that friend who brings 12 different types of cheese to a party. You might not need all of them, but damn if you're not grateful for the variety.
Need a tiny model for edge devices? Qwen3-8B at $0.01/M. Need a massive reasoning model? Qwen3.5-397B at $2.34/M. Need vision? Qwen3-VL-32B. Need audio, video, AND image? Qwen3-Omni-30B.
It's the Swiss Army knife of AI. And since it's Apache 2.0 licensed, I can actually run these models locally if I want. That's huge for me — I've got a homelab with a couple RTX 4090s, and being able to pull down a Qwen model and fine-tune it without worrying about licensing? That's the dream.
The Bad
Okay, the naming convention is a hot mess. "Qwen3-32B" — okay, that makes sense. Then "Qwen3-Coder-30B" — wait, why is the Coder version smaller? Then "Qwen3.5-397B" — what happened to 3.4? What about 3.6? It feels like Alibaba just picks numbers out of a hat.
Also, the pricing isn't always great. Qwen3.6-35B at $1/M sits in an awkward middle ground — not cheap enough for casual use, not powerful enough for serious work. Stick with Qwen3-32B at $0.28/M for general purpose.
Code Example
Here's how I used Qwen3-32B for a quick documentation generator:
response = client.chat.completions.create(
model="Qwen/Qwen3-32B",
messages=[
{"role": "user", "content": "Generate comprehensive docstring for this function:\n\ndef merge_sorted_lists(lst1, lst2):\n result = []\n i = j = 0\n while i < len(lst1) and j < len(lst2):\n if lst1[i] < lst2[j]:\n result.append(lst1[i])\n i += 1\n else:\n result.append(lst2[j])\n j += 1\n result.extend(lst1[i:])\n result.extend(lst2[j:])\n return result"}
]
)
Worked like a charm. No complaints.
Kimi: The Reasoning Machine
Why You'd Pick It
Kimi (from Moonshot AI) is the specialist. It doesn't try to be everything — it focuses on reasoning, and it's really good at it.
K2.5 at $3.00/M is pricey compared to DeepSeek, but if you're doing complex math, logic puzzles, or multi-step reasoning tasks, this is your model. I threw a graduate-level probability problem at it — the kind with Bayesian networks and conditional dependencies — and it walked through the solution step by step. No shortcuts. No hallucinations.
It also handles Chinese exceptionally well. If your application involves both complex reasoning AND Chinese language, Kimi is probably your best bet.
The Trade-offs
No vision. No multimodal. No cheap tier — all their models are premium. And it's proprietary. No open weights. If you want to self-host, you're out of luck.
The speed is also slower. I measured it at around 20-25 tokens per second on complex tasks. Fine for deep thinking, not great for real-time chat.
GLM: The Chinese Language Champion
What Makes It Special
Zhipu AI's GLM family was built from the ground up for Chinese. And it shows. In my testing, GLM-5 (at $1.92/M) outperformed every other model on Chinese text generation, translation, and cultural nuance. It understands idioms, classical references, and modern slang in ways that English-first models just can't match.
If you're building a product for the Chinese market, stop reading and go with GLM. Seriously.
They also have a budget option — GLM-4-9B at $0.01/M — which is perfect for simple Chinese text tasks.
The Not-So-Great
English? Mid. Code generation? Below average compared to DeepSeek or Qwen. GLM's tokenizer is optimized for Chinese characters, so English prompts sometimes produce weird results.
And again, the best models are proprietary. They have open versions, but GLM-5 is locked down.
The Real Winner? It Depends
Here's the thing — there's no single "best" model. It depends on what you're building.
- Coding tool? DeepSeek V4 Flash. Cheap, fast, good quality.
- Multimodal app? Qwen. Their VL and Omni models are solid.
- Complex reasoning? Kimi K2.5. Worth the premium.
- Chinese language product? GLM-5. No contest.
But here's what I really want to say: don't get locked into any of them. The beauty of using a unified API like Global API is that you can switch between models with one line of code. One day you're using DeepSeek for coding, the next you're testing Kimi for a logic problem. No vendor lock-in. No walled garden.
My Personal Recommendation
If you're just getting started, start with DeepSeek V4 Flash. It's $0.25/M, it's fast, it's good at coding and general tasks. If you need more capabilities, layer in Qwen for vision and GLM for Chinese.
And if you're worried about being locked into proprietary APIs — I feel you. That's exactly why I keep coming back to the open source models. DeepSeek and Qwen have released enough under permissive licenses that I can always fall back to running things locally.
But for day-to-day development? I use Global API. It's one endpoint, one key, and I can swap models without changing my codebase. Here's a quick example of how I set it up:
import os
from openai import OpenAI
client = OpenAI(
api_key=os.getenv("GLOBAL_API_KEY"),
base_url="https://global-apis.com/v1"
)
# Test different models with the same prompt
prompt = "Write a bash script to monitor CPU usage and alert if over 90%"
models = ["deepseek-v4-flash", "Qwen/Qwen3-32B", "kimi-k2.5", "glm-5"]
for model in models:
response = client.chat.completions.create(
model=model,
messages=[{"role": "user", "content": prompt}]
)
print(f"{model}: {response.choices[0].message.content[:100]}...\n")
That's freedom. That's what open source is really about — not just the code, but the ability to choose.
Final Thoughts
Look, I know I've thrown a lot of numbers at you. But here's the takeaway: Chinese AI models are legit. DeepSeek is undercutting everyone on price. Qwen is covering every possible use case. Kimi is crushing reasoning. GLM owns Chinese.
And none of them have to lock you in.
If you're building something new, give them a shot. Check out Global API if you want to test them all without the headache of managing separate accounts. It's what I use, and it saved me from dealing with four different dashboards and four different billing systems.
Now go build something. And remember — stay free, stay open, and never let a vendor own your stack.
— A fellow open source contributor
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