I Tested 4 Chinese LLMs for Client Work: A Freelance Dev's Breakdown
Last month I shipped a chatbot MVP for a local e-commerce client. Invoice came in at $2,400. The OpenAI bill for the month? $187. That's the moment I realized the model choice, not the labor, was eating my margin. So I went on a hunt for cheaper alternatives, and four Chinese model families kept surfacing: DeepSeek, Qwen, Kimi, and GLM.
I didn't just read the marketing pages. I ran real prompts through real APIs for two straight weeks, tracked every token, and timed how long each model took to come back. This is what I found, with receipts.
The Short Version (For Fellow Impatient Freelancers)
If you want my one-liner recommendation: DeepSeek V4 Flash wins on the dollars-to-quality ratio for English work. Qwen is the safe bet if you want every size tier under one roof. Kimi crushes anything involving logic, math, or chain-of-thought. GLM is your pick when the client throws Chinese-language data at you.
Now let me show you the work behind that.
Side-by-Side Snapshot
| Metric | DeepSeek | Qwen | Kimi | GLM |
|---|---|---|---|---|
| Maker | DeepSeek (幻方) | Alibaba (阿里) | Moonshot AI (月之暗面) | Zhipu AI (智谱) |
| Price Band | $0.25–$2.50/M | $0.01–$3.20/M | $3.00–$3.50/M | $0.01–$1.92/M |
| Sweet Spot Model | V4 Flash @ $0.25/M | Qwen3-32B @ $0.28/M | K2.5 @ $3.00/M | GLM-5 @ $1.92/M |
| Code Generation | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐ |
| Chinese | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ |
| English | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ |
| Reasoning | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ |
| Speed | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐⭐ |
| Vision | Limited | ✅ (VL, Omni) | ❌ | ✅ (GLM-4.6V) |
| Context Window | Up to 128K | Up to 128K | Up to 128K | Up to 128K |
| OpenAI-Compatible | ✅ | ✅ | ✅ | ✅ |
A few things jumped out at me right away. Every single one of these is OpenAI-compatible, which means I didn't have to rewrite a single line of my existing client code. I just swapped the base URL and the model name. More on that later.
The price range is wild. $0.01 per million tokens on the low end, all the way up to $3.50. That's a 350x spread. For a freelancer, that's the difference between a profitable project and eating hours for free.
DeepSeek: My Daily Driver for English Work
I've been running DeepSeek on probably 80% of my client engagements since I started testing. Here's the lineup:
| Model | Output $/M | What I Use It For |
|---|---|---|
| V4 Flash | $0.25 | Default daily work, coding, content drafting |
| V3.2 | $0.38 | When I want the newest architecture |
| V4 Pro | $0.78 | Client deliverables where quality is non-negotiable |
| R1 (Reasoner) | $2.50 | The hard math and logic puzzles |
| Coder | $0.25 | Pure code-completion tasks |
Where it shines
The V4 Flash at $0.25 per million output tokens is genuinely a problem for OpenAI's pricing. I'm getting GPT-4o-class responses for roughly 1/40th of the cost. On the e-commerce chatbot project, I migrated from GPT-4o to V4 Flash mid-build and my monthly inference cost dropped from $187 to about $9. That's a real line item I can hand to my client as a cost-savings win, which by the way, is a great reason to renew the contract.
Code generation is where V4 Flash really impressed me. On HumanEval and MBPP-style prompts, it kept producing clean, runnable code on the first pass. I stopped needing to re-prompt to fix syntax errors. That saved me billable hours.
Speed-wise, V4 Flash was hitting around 60 tokens per second in my tests. When a client is waiting on a demo and I need quick iteration, that matters.
Where it falls short
No native vision. If the client wants OCR on an uploaded image or image-to-text workflows, DeepSeek isn't your first call. I had to route those to Qwen.
Chinese-language nuance is okay but not stellar. If a project involves serious Mandarin content like poetry, idioms, or formal business Chinese, I'd reach for GLM or Kimi.
The model variety is narrower than Qwen. There aren't as many size options in the middle. You basically have small, medium, and reasoning.
My code for the V4 Flash swap
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 base URL trick is the whole game. One line change, and I'm talking to a different provider. No SDK swap, no auth flow rebuild.
Qwen: The One-Stop Shop
Qwen is what I'd recommend to a freelancer who doesn't want to think too hard. It's the Swiss Army knife of Chinese models, and Alibaba's infrastructure backing means it's not going anywhere.
| Model | Output $/M | My Use Case |
|---|---|---|
| Qwen3-8B | $0.01 | Trivial stuff like formatting, simple Q&A |
| Qwen3-32B | $0.28 | My general-purpose workhorse |
| Qwen3-Coder-30B | $0.35 | Code generation that needs depth |
| Qwen3-VL-32B | $0.52 | Image understanding |
| Qwen3-Omni-30B | $0.52 | Audio + video + image together |
| Qwen3.5-397B | $2.34 | Big reasoning tasks for enterprise clients |
Why I keep coming back to it
The model range is the widest of the four. From a literal penny per million tokens up to $3.20, I can match a model to any task. When a client asks for "something cheap that just works" I spin up Qwen3-8B. When they want flagship quality, I jump to Qwen3.5-397B. Same API, same SDK, same call structure.
The vision models are actually good. Qwen3-VL handled product image tagging for a retail client way better than I expected, and the Omni model can chew through audio, video, and images without me stitching together three different APIs.
Alibaba's enterprise backing means uptime has been rock solid in my two weeks of testing. I've had Qwen drop exactly zero requests.
What's annoying
The naming is a mess. Qwen3, Qwen3.5, Qwen3-Coder, Qwen3-VL, Qwen3-Omni. I literally have a sticky note on my monitor reminding me which one does what. When you're billing hourly, the cognitive overhead of "wait, which Qwen was the cheap one?" is real.
English performance is solid but not DeepSeek-tier. For pure English creative writing, I still lean DeepSeek.
A few of the mid-tier models feel priced high for what they deliver. I'd personally pass on most of them.
Example: Qwen3-32B doing what it does best
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)
That's the same client object from the DeepSeek example. Swap the model name, and you're running on Alibaba's infrastructure. This is genuinely the easiest migration I've done in years.
Kimi: When the Brain Needs to Be Big
Kimi is the priciest of the four, and it knows it. Moonshot AI isn't competing on cost, they're competing on raw intelligence.
| Model | Output $/M | When It Earns Its Keep |
|---|---|---|
| K2.5 | $3.00 | Complex reasoning, math, multi-step logic |
Kimi's whole pitch is reasoning benchmarks, and the numbers back it up. When I fed it a chain-of-thought prompt that stumped the other three, Kimi worked through it methodically. If I had a client doing financial modeling, legal document review, or anything where one wrong step cascades, I'd pay the $3.00/M premium.
That said, Kimi has no vision support in the lineup I tested. It's text-only. And at $3.00 to $3.50 per million output tokens, it costs roughly 12x what V4 Flash costs. You really need the task to warrant it.
For a freelancer, I'd treat Kimi as a specialty tool. Don't route every request through it, but don't be afraid to call on it when the question is hard.
GLM: The Bilingual Powerhouse
GLM (from Zhipu AI) is the dark horse. It's the cheapest on the budget end and it speaks Chinese like a native, because it basically is one.
| Model | Output $/M | Best Fit |
|---|---|---|
| GLM-4-9B | $0.01 | Trivial Chinese tasks, high volume |
| GLM-5 | $1.92 | Production Chinese-language work |
When I tested GLM-5 on Chinese prompts, specifically idioms, classical references, and modern business Mandarin, it outperformed every other model in the lineup. If a client hands me a project with a Chinese user base, I'm routing through GLM. Period.
The 4.6V vision variant is solid. Image understanding is on par with Qwen's VL series in my experience, and the pricing is competitive.
The weakness is English creative generation. It's good, but DeepSeek and Qwen feel more natural to me on English prose. Also, the model range is narrower than Qwen, so if you want a middle ground between 9B and full-fat 5, you're stuck.
The Math That Actually Matters to Me
Let me show you how I think about this. If a client project generates roughly 10 million output tokens per month (which is normal for a moderately busy chatbot), here's what each model costs me:
- DeepSeek V4 Flash at $0.25/M: $2.50/month
- Qwen3-32B at $0.28/M: $2.80/month
- GLM-5 at $1.92/M: $19.20/month
- Kimi K2.5 at $3.00/M: $30.00/month
Compare that to GPT-4o at $10.00/M output: $100/month for the same workload.
I can pass those savings to my client as a value-add and
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