I Tested Every Chinese AI Model So You Don't Have To
honestly, I never thought I'd be writing a post about Chinese AI models. but here we are in 2026 and if youre building anything with LLMs, you CANNOT ignore whats coming out of China anymore. the pricing is just... absurd.
I spent the last two weeks running DeepSeek, Qwen, Kimi, and GLM through their paces for my own projects. figured id share what I found so you dont have to burn your weekend like I did.
heres the thing - these arent cheap knockoffs anymore. some of them genuinely punch above what youre paying. and one of them? honestly it kinda embarrassed me for how much I was spending on GPT-4o before.
let me break it down.
why I even started looking at Chinese models
so backstory - I run a small SaaS for indie hackers (nothing fancy, maybe 200 paying users). my API bill was getting stupid. like $800/month stupid. mostly because I was using GPT-4o for everything from customer support replies to code generation to summarizing user feedback.
I knew about DeepSeek for a while but kept putting off the switch. you know how it is - migration is annoying, what if the quality sucks, what if I break stuff in production.
then I saw a tweet comparing the per-token costs and I literally said "wait what" out loud.
Qwen3-8B at $0.01/M output? thats not a typo. ONE CENT per million tokens. for comparison, GPT-4o output is like $10/M. do the math. I did. my calculator caught fire.
ok I made that last part up but you get my point.
the setup - how I tested these things
I didnt do some academic benchmark thing. I ran them through actual tasks from my real workflow:
- generating customer support replies
- writing SQL queries for analytics
- summarizing user feedback (I get like 50 messages a week)
- code refactoring for an old Django project
- translating some marketing copy to Chinese
- one weird edge case where I needed the model to reason through a pricing logic bug
I used Global API's unified endpoint to test all of them - their base URL is global-apis.com/v1 and it speaks OpenAI format so I didnt have to rewrite any code. just swapped model names. pretty much plug and play.
heres what my actual test harness looked like:
from openai import OpenAI
client = OpenAI(
api_key="ga_xxxxxxxxxxxx",
base_url="https://global-apis.com/v1"
)
def test_model(model_name, prompt):
response = client.chat.completions.create(
model=model_name,
messages=[{"role": "user", "content": prompt}],
max_tokens=500
)
return response.choices[0].message.content
simple, no frills. if a model can handle my real-world prompts, it passes. if it hallucinates my company name or generates broken Python, it fails. pretty brutal rubric tbh.
DeepSeek - the one that made me question my life choices
lets start with the one that genuinely shocked me.
DeepSeek V4 Flash at $0.25/M output tokens. TWENTY FIVE CENTS. and the quality? its RIGHT THERE with GPT-4o for most practical tasks. like Im talking 95% of what youd use GPT-4o for, this thing handles it.
heres what I tested it on specifically:
the pricing breakdown:
- V4 Flash: $0.25/M - this is your daily driver
- V3.2: $0.38/M - newer architecture, slight quality bump
- V4 Pro: $0.78/M - when you need production-grade stuff
- R1 (their reasoner): $2.50/M - for math and logic nightmares
- Coder: $0.25/M - specialized for code
what worked great:
the code generation is INSANE for the price. I threw some gnarly refactoring tasks at it and it just... handled them. multi-file changes, dependency updates, the boring stuff. it didnt hallucinate weird imports. it didnt try to use libraries that dont exist. it just worked.
speed is also legit. V4 Flash pushes around 60 tokens per second which is among the fastest I tested. when youre generating customer support replies in real-time, that matters.
English language quality is on par with the Western models. I ran it through some technical writing tests and it held up fine.
what didnt work as well:
no vision capabilities. like at all. if you need the model to look at images, DeepSeek isnt gonna help you right now. youll need to look elsewhere.
Chinese language quality is good but not the BEST. GLM and Kimi edge it out for pure Chinese tasks. if youre building something specifically for a Chinese audience, you might want to use something else for that part.
the model variety is also a bit limited. theyre focused on doing a few things really well rather than having 47 different variants like Qwen does.
my actual usage now:
about 70% of my queries go through DeepSeek V4 Flash. customer support, content generation, code stuff, you name it. my API bill dropped from $800/month to like... $120. I SAVED $680 A MONTH. thats not a typo.
quick example if you wanna try it:
response = client.chat.completions.create(
model="deepseek-v4-flash",
messages=[{"role": "user", "content": "Explain this regex to me like I'm 12: ^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}$"}]
)
print(response.choices[0].message.content)
the response is clean, well-structured, doesnt make stuff up. what more do you want for a quarter per million tokens?
Qwen - the one with too many options (in a good way?)
Qwen is Alibabas answer to everything. and I mean EVERYTHING.
they have SO many models it honestly gets confusing. like I had to make a spreadsheet just to track them all. heres the lineup:
the pricing breakdown:
- Qwen3-8B: $0.01/M - ultra cheap, for simple stuff
- Qwen3-32B: $0.28/M - the sweet spot
- Qwen3-Coder-30B: $0.35/M - code generation specialist
- Qwen3-VL-32B: $0.52/M - vision language model
- Qwen3-Omni-30B: $0.52/M - multimodal (audio/video/image)
- Qwen3.5-397B: $2.34/M - enterprise reasoning beast
what I liked:
the range is nuts. $0.01 to $3.20/M depending on what you need. if youre doing super simple stuff like extracting keywords or classifying short texts, Qwen3-8B at a penny per million tokens is genuinely ridiculous.
they have proper vision models. the VL series actually understands images. I tested it with some product mockups and it described them accurately.
the Omni model handles audio, video, AND image. I dont have a use case for that yet but its cool that it exists.
Alibaba backing means the infrastructure is solid. no random downtime, no flaky responses.
what annoyed me:
the NAMING. why are there so many versions? Qwen3, Qwen3.5, Qwen3.6, Qwen3-VL, Qwen3-Omni. I literally had to look up what the difference is every time. pretty much a UX nightmare for newcomers.
English quality is good but not DeepSeek level. like its 90% there but you can tell the difference on subtle stuff.
some models feel overpriced. Qwen3.6-35B at like $1/M when DeepSeek does similar quality for $0.25? hard to justify.
my verdict:
if you need ONE provider that covers everything from cheap classification to vision to multimodal, Qwen is your pick. but for pure text generation in English, DeepSeek is still my go-to.
Kimi - the brainy one
Kimi is from Moonshot AI and these guys are LEANING into reasoning. like hard.
the pricing:
- K2.5: $3.00/M - their flagship
- the range goes up to $3.50/M for their top tier
yeah its expensive. but theres a reason.
why people pay for it:
I tested K2.5 on my pricing logic bug and it actually walked through the problem step by step better than I could. it caught an edge case Id missed where the discount stacked wrong with the annual plan.
on math benchmarks, coding challenges, multi-step reasoning - Kimi is consistently at the top. its the model you use when you NEED the model to actually THINK, not just pattern match.
the downsides:
price. $3.00/M is steep for everyday use. I cant justify this for customer support replies, you know?
speed is also slower than DeepSeek. that makes sense because its doing more work internally, but if you need instant responses, its noticeable.
no vision either as far as I could tell. pure text and reasoning.
when I use it:
only when Im stuck on a genuinely hard problem. like once a week maybe? its my "consultant" model. I wouldnt run it as my primary.
GLM - the Chinese language champion
GLM is from Zhipu AI and if youre doing Chinese language work, this is your best friend.
the pricing:
- GLM-4-9B: $0.01/M - budget tier
- GLM-5: $1.92/M - flagship
what it does well:
Chinese language quality is genuinely top tier. like if youre doing translation, content creation for Chinese audiences, or customer support in Chinese - GLM is the move. it understands idioms, cultural context, all of it.
GLM-4.6V is their vision model and it works decently for image understanding.
the pricing range is reasonable. $0.01 to $1.92/M.
what I didnt love:
for English-heavy workloads, it felt a step behind DeepSeek and Qwen. not bad, just not as refined.
speed is okay but not blazing fast.
my take:
unless youre specifically building for Chinese markets or need the vision capabilities, you can probably skip GLM for now. but keep it in your back pocket.
the actual numbers side by side
heres the quick reference table I wish I had before starting:
| What I care about | DeepSeek | Qwen | Kimi | GLM |
|---|---|---|---|---|
| Cheapest model | $0.25/M | $0.01/M | N/A | $0.01/M |
| Best daily driver | V4 Flash @ $0.25 | Qwen3-32B @ $0.28 | K2.5 @ $3.00 | GLM-5 @ $1.92 |
| Code generation | Top tier | Great | Great | Decent |
| Chinese language | Great | Great | Best | Best |
| English language | Top tier | Great | Great | Good |
| Reasoning | Great | Great | Best | Great |
| Speed | Fastest | Fast | Slowest | Fast |
| Vision support | Limited | Yes (VL, Omni) | No | Yes (4.6V) |
| Context window | 128K | 128K | 128K | 128K |
what I actually run my SaaS on now
after all this testing, heres my setup:
70% DeepSeek V4 Flash - for everything routine. support replies, code stuff, content drafts. $0.25/M and it handles it all.
20% Qwen3-32B - when I need a different perspective or when DeepSeek gives me something weird. also for image-related tasks. $0.28/M.
5% Kimi K2.5 - for the gnarly reasoning problems. once a week max. $3.00/M but worth it when I need it.
5% GLM-5 - only when I have Chinese-language work. $1.92/M.
monthly bill now? around $150-180 instead of $800. thats a 75% reduction. for the SAME quality of output (sometimes better honestly).
the honest truth about switching
migration wasnt that bad. heres what I wish someone told me before I started:
all four are OpenAI API compatible - which means if youre using the OpenAI Python client like I am, you literally just change the model name. maybe 5 minutes of work.
test BEFORE you commit - dont just switch blindly. run your actual production prompts through each one and see how they do. what works for my SaaS might not work for yours.
have a fallback - dont put all your eggs in one basket. I run DeepSeek as primary but have Qwen ready to go if something breaks.
watch the latency - some of these models are slower than youre used to. if youre doing real-time stuff, test that specifically.
the pricing is REAL - like I know I keep saying this but when youre processing millions of tokens, the difference between $0.25/M and $10/M is the difference between a viable side project and an expensive hobby.
things that surprised me
- DeepSeek V4 Flash is genuinely close to GPT-4o quality for most tasks. not all, but most.
- Qwen3-8B at $0.01/M actually works for simple stuff. I thought it would be garbage. its not.
- Kimi reasoning is legitimately impressive. worth the premium when you need it.
- the response quality from Chinese models has caught up WAY faster than I expected.
- I havent paid OpenAI in two months. that feels weird to say but its true.
things that didnt surprise me but annoyed me anyway
- the documentation is sometimes spotty. like model names dont always match what you see on the pricing page.
- some models get deprecated without much warning. check before you build your whole stack around one.
- the English-language content around these models is still catching up. lots of the best info is in Chinese-language forums.
should you switch?
honestly? probably yes, for at least SOME of your workloads.
if youre running anything that processes a lot of text - content generation, customer support, code stuff, data extraction - theres zero reason to pay GPT-4o prices when DeepSeek V4 Flash does 95% of the work for 2.5% of the cost.
if you need vision or multimodal, Qwen has you covered.
if you need pure reasoning power, Kimi is your best bet.
if youre building for Chinese markets, GLM is the move.
theres no reason to pick just one. mix and match based on what youre actually doing. thats literally what I do and my bill is 75% lower.
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