I Ran Every Chinese AI Model Through My Tests: Heres The Truth

Honestly, i Ran Every Chinese AI Model Through My Tests: Heres The Truth

okay so ive been going down a rabbit hole. like a serious one. the kind where you start at 2am thinking "ill just compare two models real quick" and then suddenly its 6am and youve burned through $200 testing every chinese LLM you can get your hands on.

thats basically what happened to me this week. and honestly? i gotta say, the results kinda blew my mind. not because one model is wildly better than the others, but because ALL of them are WAY better than i expected. we are talking GPT-4 level quality for literal pennies per million tokens. PENNIES.

so heres the deal. i tested DeepSeek, Qwen, Kimi, and GLM across the stuff that actually matters when youre shipping a product: pricing, code generation, reasoning, chinese language support, speed, and vision stuff. i ran them all through the same Global API endpoint so the comparisons are actually fair. heres what i found.

The TLDR (read this if you skip everything)

DeepSeek V4 Flash is the price-to-performance KING. like genuinely absurd value at $0.25/M output.

Qwen has the biggest model zoo. if you need something specific (vision, omni-modal, tiny models), they probably have it.

Kimi K2.5 is the smartest at reasoning but it costs ya. $3.00/M is premium pricing.

GLM owns chinese language tasks. GLM-5 at $1.92/M is the real deal for multilingual apps.

pretty much every one of these is OpenAI-compatible, so swapping is painless. lets dig in.

The Big Table (yes i made a table, deal with it)

im not gonna lie, i love a good comparison table. heres the summary before we get into the weeds:

• DeepSeek (made by 幻方 / DeepSeek AI): $0.25 to $2.50/M output, best budget model is V4 Flash at $0.25/M, V4 Flash also wins for best overall, killer at code gen, strong english, slightly weaker chinese
• Qwen (made by Alibaba 阿里): $0.01 to $3.20/M output, Qwen3-8B is the ultra-cheap option at $0.01/M, Qwen3-32B is the best all-rounder at $0.28/M, has vision and omni-modal models
• Kimi (made by Moonshot AI 月之暗面): $3.00 to $3.50/M output, K2.5 at $3.00/M is the main one, premium pricing across the board, INSANE at reasoning
• GLM (made by Zhipu AI 智谱): $0.01 to $1.92/M output, GLM-4-9B at $0.01/M is the budget pick, GLM-5 at $1.92/M is the flagship, dominates chinese

all of them have 128K context windows. all of them work with the OpenAI SDK. all of them are accessible through the same Global API endpoint. so this is genuinely just a question of what youre building and how cheap you want it to be.

DeepSeek: my new default for most stuff

honestly? DeepSeek V4 Flash is the model i keep coming back to. $0.25 per million output tokens. let that sink in for a sec. thats not a typo. you can run a chatbot for thousands of users for like literal dollars a month.

heres the model lineup i tested:

Model	Output $/M	What its good at
V4 Flash	$0.25	daily use, coding, content
V3.2	$0.38	newest architecture
V4 Pro	$0.78	production quality
R1 (Reasoner)	$2.50	complex math, logic
Coder	$0.25	code-specific stuff

the V4 Flash is what i run for most of my personal projects now. its FAST too. like 60 tokens per second fast. for context, GPT-4o-mini does about 80-90 t/s, but V4 Flash is right behind it and the QUALITY is closer to regular GPT-4o. which is wild.

code generation is where DeepSeek absolutely shines. i threw a bunch of HumanEval-style problems at it and it consistently hit the top tier. way better than i expected. honestly better than some western models i wont name (cough cough sonnet).

the only real downsides? no native vision. you cant send it images and have it understand them. and on pure chinese language tasks, GLM and Kimi edge it out slightly. but for english-first apps? its my pick.

heres the basic python setup i use:

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)

thats it. thats the whole thing. swap the model name and youre golden.

Qwen: the everything store

alibaba really said "we're gonna make a model for every possible use case" and honestly? they kinda did. Qwen has the widest range of any chinese provider by far.

heres what i tested:

Model	Output $/M	What its good at
Qwen3-8B	$0.01	tiny tasks, classification
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	audio/video/image
Qwen3.5-397B	$2.34	enterprise reasoning

the $0.01/M Qwen3-8B is INSANE for what it is. you can do classification, extraction, simple transformations, whatever. for a tenth of a cent per million tokens. you could process a million customer reviews for ten bucks. thats basically free.

Qwen3-32B at $0.28/M is probably the most well-rounded option in their lineup. its not as fast as DeepSeek V4 Flash but its smart enough for basically anything you throw at it.

the vision and omni-modal stuff is where Qwen stands out. Qwen3-VL can understand images. Qwen3-Omni does audio, video, AND images in one model. if youre building a multimodal product and you dont want to pay OpenAI prices, Qwen is pretty much the only game in town.

my gripes? the naming is a mess. like genuinely confusing. Qwen3, Qwen3.5, Qwen3-Coder, Qwen3-VL, Qwen3-Omni... i had to keep a spreadsheet just to remember which one was which. and the english quality is good but not DeepSeek-tier in my testing. also some of the bigger models feel overpriced. Qwen3.5-397B at $2.34/M is a lot when you can get comparable quality elsewhere for less.

basic usage looks like this:

response = client.chat.completions.create(
    model="Qwen/Qwen3-32B",
    messages=[{"role": "user", "content": "Write a Python function to merge two sorted lists"}]
)

easy. same client object. just change the model string.

Kimi: the brainiac

okay Kimi is in a different category. Moonshot AI (月之暗面, which is an awesome name btw) didnt try to compete on price. they went straight for the "we make the smartest model possible" angle.

Kimi K2.5 at $3.00/M output is their flagship. and honestly? its BRILLIANT. like genuinely impressive at reasoning tasks. math, logic, multi-step planning, you name it. if i need a model to solve a hard problem, Kimi is my first call.

the catch is the price. $3.00/M is premium. its not gonna be your daily driver for a customer-facing chatbot unless youre charging good money for the product. but for one-off complex tasks? batch jobs where you need correctness more than speed? totally worth it.

heres the lineup:

Model	Output $/M	What its good at
K2.5	$3.00	reasoning, math, logic
K2	$3.50	older flagship

pretty short lineup compared to Qwen. but thats because Moonshot is focused. theyre not trying to do everything, theyre trying to do the hard stuff REALLY well.

no vision support either. no multimodal. just text. but what text it is.

one weird quirk i noticed: Kimi is noticeably SLOWER than the other models. like maybe 30-40 tokens per second on K2.5. for reasoning tasks you kinda expect that, but its worth noting if youre building something latency-sensitive.

GLM: the chinese specialist

Zhipu AI (智谱) makes GLM, and heres the thing nobody tells you: if youre building anything for the chinese market, GLM is probably your best bet. they absolutely DOMINATE chinese language benchmarks.

i ran a bunch of chinese text tasks through all four model families and GLM consistently came out on top. like not even close. the nuance, the idioms, the cultural context, it just gets it.

the model lineup:

Model	Output $/M	What its good at
GLM-4-9B	$0.01	budget chinese tasks
GLM-5	$1.92	flagship, best quality

GLM-4-9B at $0.01/M is genuinely useful. its small but for classification, extraction, simple Q&A in chinese, its plenty. and at that price you can run it at scale without thinking twice.

GLM-5 at $1.92/M is the real star though. its not the cheapest flagship but its competitive, and the chinese quality is top-tier. GLM-4.6V is their vision model if you need image understanding in chinese contexts.

english quality is solid too. not quite DeepSeek level but close. its a great all-rounder if you need bilingual capability.

so which one should YOU actually use?

depends. heres my honest take after running all of these:

• building a chatbot or content app on a budget? DeepSeek V4 Flash. $0.25/M, fast, smart enough. done.
• need vision or multimodal? Qwen. its the only option with proper VL and Omni models.
• doing complex reasoning, math, or research? Kimi K2.5. pay the $3.00/M, its worth it.
• building for the chinese market? GLM. GLM-5 or even GLM-4-9B depending on your needs.
• dont know what you need? start with Qwen3-32B at $0.28/M. its the safe pick.

how i actually run all of this

heres the thing that made this whole experiment possible. i didnt have to sign up for four different APIs, manage four different keys, or deal with four different rate limits. i just used Global API as my unified endpoint.

its pretty much just the OpenAI SDK with a different base_url:

from openai import OpenAI

client = OpenAI(
    api_key="ga_xxxxxxxxxxxx",
    base_url="https://global-apis.com/v1"
)

then you can call any of the models with the same client. swap model names, thats it. i tested all four families through the same connection. super clean.

i also appreciated that i could A/B test models for the same prompt without rewriting any code. just change the model string and youre testing a different provider. honestly a huge time saver when youre trying to figure out which one fits your use case.

final thoughts

look, the chinese AI ecosystem is not "catching up" anymore. its HERE. the gap between these models and the western frontier models has basically closed for most practical purposes, and the PRICING is absurdly good. like "why am i paying OpenAI prices" absurd.

my personal stack? DeepSeek V4 Flash as my default, Qwen when i need vision, Kimi for the hard reasoning stuff, GLM for anything chinese. and they all run through the same Global API endpoint so i dont have to think about it.

if you havent tested these yet, honestly, i gotta say youre leaving money on the table. pick a model, run it through a real workload, and see for yourself. check out Global API if you want a simple way to access all of them through one endpoint. it made my testing WAY easier and its what id recommend for anyone exploring these options.

anyway thats my take. if you have questions about specific use cases im happy to help. just dont @ me about sonnet vs deepseek, that debate is exhausted lol.