I Tested DeepSeek, Qwen, Kimi And GLM Heres The Real Winner
okay so listen, ive been building AI tools for like 2 years now and I kept hearing the same question over and over from other indie hackers in my Discord: "should I use DeepSeek or Qwen or whatever else is coming out of China these days?" and honestly, I had no good answer. every blog post I found was either outdated, sponsored, or just regurgitating press releases.
so I did what any slightly unhinged solo dev would do. I spent my own money, wired up all four model families to my side project, and ran them through actual real-world tasks. not benchmarks, not synthetic tests. the messy stuff I actually need to do every week: write code, summarize docs, translate chinese customer feedback, generate product descriptions, the whole grind.
heres what I found. and yeah, theres a clear winner. kinda.
The Setup, Real Quick
before I dump numbers on you, lemme explain the playing field. all four of these (DeepSeek, Qwen, Kimi, GLM) speak OpenAI's API dialect. which means you can hit them with the same client lib, swap models, and youre done. no weird SDKs, no bespoke auth flows. that alone is a HUGE deal if youre a one-person team like me.
I routed everything through Global APIs unified endpoint because honestly, juggling 4 different API keys and 4 different dashboards is not what I wanna do with my life. one key, one bill, swap models with a string change. well talk more about that later.
heres the full landscape I tested:
Pricing Breakdown (all output $ per million tokens)
DeepSeek lineup:
- V4 Flash — $0.25
- V3.2 — $0.38
- V4 Pro — $0.78
- R1 (Reasoner) — $2.50
- Coder — $0.25
Qwen lineup:
- Qwen3-8B — $0.01
- Qwen3-32B — $0.28
- Qwen3-Coder-30B — $0.35
- Qwen3-VL-32B — $0.52
- Qwen3-Omni-30B — $0.52
- Qwen3.5-397B — $2.34
Kimi lineup:
- K2.5 — $3.00
- Range goes $3.00–$3.50/M across the family
GLM lineup:
- GLM-4-9B — $0.01
- GLM-5 — $1.92
- Range $0.01–$1.92/M
all four sit at 128K context windows. all four are OpenAI-compatible. thats where the similarities end though.
Why I Almost Went With DeepSeek (And Kinda Did)
honestly? my first instinct going into this was "DeepSeek wins, everyones using it, why even test the others." and I was mostly right? heres the deal.
DeepSeek V4 Flash at $0.25/M is genuinely absurd value. I was running it on a chatbot feature and got responses that felt indistinguishable from GPT-4o for 1/40th the price. I literally checked my dashboard twice because I thought something was broken with the billing.
the code generation is the real standout though. I threw my gnarliest refactoring task at it (had to rewrite a 800-line Next.js component to use server actions) and it just... did it. clean, worked on the first try, no weird hallucinated imports. on HumanEval and MBPP-style stuff it consistently outperformed everything else I tested except maybe Qwen3-Coder.
speed is also wild. V4 Flash was hitting around 60 tokens per second in my tests, which is basically realtime. great for chat UX where you dont want that "thinking..." pause to feel like a funeral.
english performance? honestly on par with the Western models. I had a user email me saying "I cant tell this isnt Claude" which is either a compliment to DeepSeek or an insult to Claude, depending on your perspective.
the downsides are real though. vision is basically a no-go, you cant send it images. chinese language tasks — DeepSeek does fine but GLM and Kimi genuinely edge it out. I had a customer send me a feedback doc in mandarin and the difference was noticeable, especially with idioms. and the model lineup isnt as deep as Qwens, you dont get 15 different sizes to pick from.
heresa quick code snippet for hooking up DeepSeek V4 Flash through Global APIs:
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)
see how clean that is? thats the whole integration. same code works for everything else in this post btw.
Qwen: The One With Too Many Models (In A Good Way)
Qwen is the family I keep coming back to when I need something specific. Alibaba has gone absolutely feral with model variants and I mean that as a compliment. need a tiny model for classification? Qwen3-8B at $0.01/M. need a 397B monster for enterprise reasoning? Qwen3.5-397B. need vision? Qwen3-VL. need omni-modal (audio + video + image)? Qwen3-Omni. its got a tool for every job.
Qwen3-32B at $0.28/M is probably my daily driver these days. its the sweet spot — fast enough for interactive stuff, smart enough for real work. I use it for everything from generating marketing copy to writing SQL queries to summarizing user research notes. rarely disappoints.
the vision models are particularly good. I was building a feature that lets users upload screenshots and get help with their bug reports. Qwen3-VL-32B nailed it where DeepSeek just couldnt even try. multimodal is genuinely a Qwen superpower.
heres where it gets annoying though. the naming is BAD. like, genuinely bad. Qwen3-8B, Qwen3-32B, Qwen3-Coder-30B, Qwen3.5-397B, Qwen3-Omni-30B. which is newer? which is better? god only knows. I had to make a Notion doc just to keep track of which Qwen does what.
and some of the pricing is weird. Qwen3.6-35B at $1/M is honestly a tough sell when V4 Pro from DeepSeek is $0.78/M and arguably better. you do feel like youre paying for the Alibaba brand tax on the premium models.
but the biggest range. you can spend $0.01/M or $2.34/M. thats flexibility no one else in this list matches.
quick example with Qwen3-32B:
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)
literally the same client, model name change, done.
Kimi: The Brains Of The Operation
okay so Kimi is what I pull out when I need the model to actually THINK. like really think, not just autocomplete cleverly. its from Moonshot AI and it shows up specifically on reasoning benchmarks. hard math, multi-step logic, anything where the chain of thought actually matters.
heres the catch: its expensive. K2.5 sits at $3.00/M output and the family goes up to $3.50/M. thats not a typo. its the priciest of the bunch by a wide margin. so you dont use Kimi for everything. you use it for the stuff where cheaper models just spit out confidently wrong answers.
I had a situation last month where I was trying to debug a particularly nasty race condition. I asked three models in parallel: DeepSeek V4 Flash, Qwen3-32B, and Kimi K2.5. DeepSeek gave me a plausible-looking but wrong answer in 2 seconds. Qwen got the gist but missed an edge case. Kimi went full research mode, asked clarifying questions, traced through the actual code logic, and caught the issue. I shipped the fix that afternoon.
thats the Kimi experience. its not the tool for bulk content generation or chat features. its the tool you bring in when accuracy matters more than cost.
the weaknesses: speed is the slowest of the four, by a lot. and theres no vision support at all. if you need to do anything with images, look elsewhere.
for me Kimi is the "premium" tier I dip into for the 5% of tasks that really need it. every indie hacker I know does some version of this routing — cheap models for the bulk, premium models for the hard stuff.
GLM: The Quiet Winner For Chinese Work
I was honestly surprised by GLM. didnt expect much going in. Zhipu AI hasnt gotten the same hype cycle as DeepSeek and thats kinda their loss.
heres the thing: if youre doing anything in Chinese — and I mean real Chinese, not just translated english prompts — GLM is the best of the four. full stop. I tested it on customer feedback analysis, on a translation task with colloquialisms, on a creative writing brief for a chinese market campaign. GLM beat everyone, including Kimi which is itself chinese-trained.
GLM-4-9B at $0.01/M is the cheapest serious model in this whole comparison. tied with Qwen3-8B. its not gonna win any reasoning awards but for classification, simple generation, and high-volume tasks in chinese? its absurd value. I routed all my chinese-language customer support triage through it and it paid for itself in like 2 days.
GLM-5 at $1.92/M is the flagship. its good. not DeepSeek-fast, not Kimi-smart at reasoning, but genuinely well-rounded. solid english, solid code, solid chinese, has a vision variant (GLM-4.6V).
the main weakness is kinda weird: speed is mid-tier, slower than DeepSeek and Qwen, and the brand recognition is lower. but honestly? I think GLM is the most underrated of the four. if youre building anything for chinese markets, just use it.
My Actual Recommendation After All This
okay heres what I run on my own stuff right now, for anyone who wants to copy my homework:
- Default daily work → DeepSeek V4 Flash ($0.25/M). unbeatable value, fast, smart enough.
- Specific tasks → Qwen3-32B ($0.28/M) when I need a slight quality bump, or Qwen3-Coder-30B for code.
- Hard reasoning → Kimi K2.5 ($3.00/M) for the gnarly stuff.
- Chinese work → GLM-4-9B for bulk, GLM-5 when I need quality.
- Vision → Qwen3-VL-32B ($0.52/M) basically the only serious option here.
theres no single winner. but DeepSeek V4 Flash is the model I tell people to start with. its the one that makes you go "wait, this is THIS cheap?" and that feeling is addictive.
the meta-lesson though: dont get locked into one provider. the smartest thing I did this year was wire everything up through Global APIs unified endpoint so I can swap models in 30 seconds. when Kimi drops a new model, I can test it the same day. when DeepSeek prices something lower, I switch with one line change. thats the actual superpower —
Top comments (1)
Great breakdown! Curious what your testing methodology
looked like — were you evaluating purely on output quality
or did latency and cost per token factor in too?
Also wondering how they compared on longer context tasks.
That's usually where the real differences show up in
production.