I gotta say, deepSeek vs Qwen vs Kimi vs GLM: Which AI API Actually Wins?
honestly I didn't plan on writing this. I was just trying to ship a small side project — a chat tool for my SaaS — and I figured I'd grab whatever LLM was cheapest and call it a day. that's how it always goes right? you think you're gonna spend 30 minutes on infra and then three days later you're neck deep in benchmark spreadsheets comparing four Chinese model families.
so yeah. here we are.
I've been building indie stuff for a while now and I kept seeing these names pop up — DeepSeek, Qwen, Kimi, GLM — in every Discord I'm in. people were RAVING about them. cheaper than OpenAI, sometimes smarter, and built by teams who clearly know what they're doing. but nobody was really telling me which one to pick. so I just tested them myself. all four. through Global API's unified endpoint (more on that later). I'm gonna walk you through what I found, what I'd actually use, and where I'd skip.
let's get into it.
so what's the deal with these four anyway?
quick backstory. over the past like 18 months, Chinese AI labs have gone from "cute experiments" to "genuinely world-class." you've got DeepSeek (made by 幻方, the quant hedge fund folks), Qwen (Alibaba's flagship — yeah, the 阿里 guys), Kimi (from Moonshot AI, aka 月之暗面, which is one of the coolest company names I've ever seen), and GLM (Zhipu AI, 智谱).
they're all OpenAI-compatible now, which means I can hit any of them with the same Python client. same SDK, same chat completions format, just different model strings. that's HUGE for indie hackers like me who don't wanna learn four different APIs.
the question isn't "are they good" — they obviously are. the question is which one wins for YOUR specific use case. and that's what we're gonna figure out.
the speed-run comparison (in case you don't wanna read all this)
heres the high level summary so you can skim:
| Feature | DeepSeek | Qwen | Kimi | GLM |
|---|---|---|---|---|
| Developer | 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 |
| Best Budget Model | V4 Flash @ $0.25/M | Qwen3-8B @ $0.01/M | N/A (all premium) | GLM-4-9B @ $0.01/M |
| Best Overall | 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/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 ✅ |
don't read too much into the star ratings — they're vibes more than science. what matters is the pricing tiers and the use cases. which we're getting to next.
DeepSeek — my new daily driver (probably)
I wanna start with DeepSeek because this is the one that genuinely shocked me.
the lineup
| Model | Output $/M | Best For |
|---|---|---|
| V4 Flash | $0.25 | Daily use, coding, content |
| V3.2 | $0.38 | Latest architecture |
| V4 Pro | $0.78 | Production quality |
| R1 (Reasoner) | $2.50 | Complex math, logic |
| Coder | $0.25 | Code-specific tasks |
why I keep coming back to it
look — V4 Flash at $0.25/M output is basically a joke. like, an insanely good joke. I'm running it for content generation in my apps and the quality is right up there with stuff I was paying 10x more for a year ago. that's not hyperbole. I literally copy-pasted outputs side by side with GPT-4o outputs and my non-technical friends couldn't tell which was which.
the speed is also NUTS. V4 Flash clocks around 60 tokens/sec on my tests, which is one of the fastest responses I've seen from any model in this price bracket. for chat UIs that matters a lot. nobody likes a laggy chatbot.
code generation is where DeepSeek really shines too. their V4 Flash and dedicated Coder model both score at the top on HumanEval and MBPP benchmarks. I've been using it as my "write a quick function" assistant for like 6 months now and I've basically stopped reaching for other tools.
the downsides (because nothing's perfect)
ok so DeepSeek isn't great at vision stuff. like, if you need image understanding, you're gonna wanna look elsewhere. they don't have a native multimodal model that I've found reliable.
also their Chinese language performance is good but not THE best. GLM and Kimi edge them out for pure Chinese tasks. if you're building something primarily for a Chinese-speaking audience, keep that in mind.
and their model variety is kinda limited compared to Qwen. you've got what, 5 main models? Qwen has like 12+ variants. less choice can be a pro or a con depending on your personality. I like fewer decisions, so I count it as a win.
how I'm actually using it
heres a real code snippet from my last 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", # V4 Flash
messages=[{"role": "user", "content": "Explain quantum computing in 100 words"}]
)
print(response.choices[0].message.content)
thats it. thats the whole setup. swap in your API key, change the model name, you're done. I love that I don't need a separate SDK or auth flow or whatever.
Qwen — the swiss army knife (with some quirks)
Qwen is what I recommend to people who don't know what they need. because Alibaba basically makes a model for every possible use case.
the lineup
| Model | Output $/M | Best For |
|---|---|---|
| Qwen3-8B | $0.01 | Ultra-light tasks |
| Qwen3-32B | $0.28 | General purpose |
| Qwen3-Coder-30B | $0.35 | Code generation |
| Qwen3-VL-32B | $0.52 | Image understanding |
| Qwen3-Omni-30B | $0.52 | Multimodal |
| Qwen3.5-397B | $2.34 | Enterprise reasoning |
wait, did you catch that Qwen3-8B is $0.01/M output?? yeah. one cent. per MILLION tokens. I had to double-check that wasn't a typo when I first saw it. for simple stuff like classification, intent detection, tiny reformatting tasks — that's basically free.
what I like
the model RANGE is unmatched. you've got a $0.01/M model for trivial stuff and a $3.20/M model for whatever heavy lifting you need. everything in between? also covered. if you're the type of dev who likes fine-tuning model choice to cost, Qwen is a playground.
they also have the best vision/multimodal story. Qwen3-VL-32B handles image inputs well, and Qwen3-Omni-30B does audio + video + image in one model. for someone building a multimodal product, that's a big deal.
Alibaba's enterprise-grade infra means the uptime has been rock solid in my testing. I've never had a weird outage or rate limit issue that wasn't my fault.
what bugs me
honestly? the naming is a MESS. Qwen3, Qwen3.5, Qwen3.6, Qwen3-32B, Qwen3.5-397B... like, I get it, you release a lot of models, but please hire a naming consultant. I had to make a spreadsheet just to remember which one was which.
also some of their models feel overpriced. Qwen3.6-35B at like $1/M output — for what? you can get comparable quality from DeepSeek V4 Pro at $0.78/M. the pricing curve on Qwen is uneven.
and their English performance is good but not DeepSeek-tier. for English-heavy apps, I default to DeepSeek first.
code example using Qwen
response = client.chat.completions.create(
model="Qwen/Qwen3-32B",
messages=[{"role": "user", "content": "Write a Python function to merge two sorted lists"}]
)
same client, same endpoint, just swap the model. I literally use the same client object across all my projects now. its so nice.
Kimi — the brainy one (for when you need it to THINK)
I'll be real with you — Kimi is the one I use least, but when I use it I'm always impressed.
the lineup
| Model | Output $/M | Best For |
|---|---|---|
| K2.5 | $3.00 | Reasoning, math, complex logic |
the pricing on Kimi is $3.00-$3.50/M across their lineup, which makes them the most expensive of the four. but heres the thing — they're not really competing on price. they're competing on raw reasoning power.
why it earns its price tag
if you've got a task that requires actual THINKING — multi-step reasoning, math proofs, logical puzzles, that kind of thing — Kimi is the best of the four. the reasoning benchmarks show it consistently outpacing the others on chain-of-thought tasks. and honestly, the outputs feel more "thoughtful." like, you can tell the model is actually reasoning through the problem rather than pattern matching to a likely answer.
Chinese language performance is also elite. like, top of the pile alongside GLM. if you're doing translation, cultural context understanding, or Chinese-specific NLP, Kimi is fantastic.
why I don't reach for it more often
its slow. like, noticeably slower than the others. the ⭐⭐⭐ speed rating isn't kidding. for chat interfaces where response time matters, this can feel sluggish.
and yeah — the price. $3.00/M is real money when you're doing volume. for a chatbot where users are sending thousands of messages a day, that math gets uncomfortable fast. I use Kimi selectively, for "hard" requests only. like, classify the user's intent with a cheap model, then route the genuinely hard queries to Kimi.
there's also no native vision/multimodal model from Kimi as far as I can tell. text-only. which is fine for a lot of use cases but limiting if you need image stuff.
GLM — the quiet overachiever
GLM is the one I think more people should be using but aren't. Zhipu AI doesn't have the same hype machine as the other three, but the models are legit.
the lineup
| Model | Output $/M | Best For |
|---|---|---|
| GLM-4-9B | $0.01 | Ultra-light tasks |
| GLM-5 | $1.92 | Production flagship |
the price range is $0.01-$1.92/M, which is competitive. you've got a dirt-cheap tier for trivial work and a flagship model that punches way above
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