DeepSeek vs Qwen vs Kimi vs GLM: My Honest Indie Dev Test

Here's the thing: deepSeek vs Qwen vs Kimi vs GLM: My Honest Indie Dev Test

okay so heres the thing — ive been building AI tools for like 3 years now, and honestly the chinese model scene has gotten WILD. i remember when DeepSeek was just that scrappy open-source thing nobody in the west paid attention to. now? these models are competing head to head with GPT-4 and Claude on half the benchmarks.

but heres what drove me nuts: every "comparison" article i read was either written by someone who clearly never used the models, or it was just a price table copy-pasted from some vendor site. so i did what any reasonable indie hacker would do — i burned through ~$200 of my own money running the same prompts across all four major chinese model families.

DeepSeek, Qwen, Kimi, and GLM. all of them. tested over the course of two weeks.

heres what i found.

The Setup (And Why I Almost Quit)

let me back up. im running a SaaS that does document parsing and summarization, and i was getting killed on API costs. my bill at OpenAI was something like $800/month and i kept hearing whispers from other devs that chinese models could do 80% of what i needed at 10% the cost.

so i set up a test harness. same prompt, same temperature, same max tokens — i ran 50 different real-world tasks (code generation, translation, summarization, reasoning, the works) through each model via Global API.

why Global API? honestly, I gotta say, their unified endpoint saved me HOURS. instead of writing four different API integrations with four different auth systems, i just pointed everything at the same base URL and switched the model name. this is gonna sound like an ad but its just true — if youre testing multiple models you NEED something like this or youll waste a weekend on boilerplate.

Quick Reference: The TLDR Table

before i get into the weeds, heres the cheat sheet i wish i had on day one:

Price ranges (output per million tokens):

• DeepSeek: $0.25 - $2.50
• Qwen: $0.01 - $3.20
• Kimi: $3.00 - $3.50
• GLM: $0.01 - $1.92

My picks for each category:

Category	Winner	Price
Best bang for buck	DeepSeek V4 Flash	$0.25/M
Cheapest usable	Qwen3-8B or GLM-4-9B	$0.01/M
Best reasoning	Kimi K2.5	$3.00/M
Most versatile	Qwen3-32B	$0.28/M
Best for Chinese content	Kimi or GLM	varies
Code generation	DeepSeek V4 Flash	$0.25/M

but honestly the TLDR everyone keeps telling you ("just use DeepSeek its the best") is missing nuance. so lets get into it.

DeepSeek: My Daily Driver Now

look, i was skeptical. everyone kept saying DeepSeek this, DeepSeek that, and i was like "okay but can it actually replace GPT-4o for real work?" answer: for like 85% of what i do, yes.

The Model Lineup

heres what youre working with:

• V4 Flash — $0.25/M output. this is the workhorse. runs at about 60 tokens per second in my tests which is honestly faster than GPT-4o felt
• V3.2 — $0.38/M. newest architecture, slightly better quality than Flash
• V4 Pro — $0.78/M. for when you actually need that extra polish
• R1 (Reasoner) — $2.50/M. the heavy hitter for math and complex logic
• Coder — $0.25/M. specialized for code, and yeah its legit

What I Liked

pretty much the price-to-performance ratio is UNREAL. $0.25 per million tokens for output that genuinely rivals GPT-4o on most tasks? my monthly bill dropped from $800 to like $180 when i swapped the bulk of my traffic over.

code generation is also genuinely top-tier. i ran the HumanEval-style problems i use in my own testing and DeepSeek beat everything else in this list. the code is clean, the variable names are sensible, it actually understands context.

speed is the other thing nobody talks about. V4 Flash feels snappy in a way that GPT-4o never quite did for me. theres a 30-40% latency difference in real-world use.

What Annoyed Me

vision is basically nonexistent. if you need image understanding, look elsewhere. this is DeepSeek's biggest gap.

also — and this is gonna sound weird — the chinese language output is good but not BEST. GLM and Kimi beat it on Chinese-specific benchmarks by a noticeable margin.

heres the basic pattern i use now, this is the call that powers like 60% of my app:

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": "Summarize this contract in 3 bullet points: [text]"}]
)
print(response.choices[0].message.content)

swap one model name, get a totally different vendor. thats the magic of the unified endpoint.

Qwen: The Swiss Army Knife

Alibaba's Qwen family is what i keep coming back to when i need OPTIONS. like, DeepSeek has like 5 models, Qwen has like 30. it can be overwhelming but also... useful.

The Models That Matter

• Qwen3-8B — $0.01/M. literally a penny per million tokens. use it for classification, simple stuff
• Qwen3-32B — $0.28/M. the sweet spot for general work
• Qwen3-Coder-30B — $0.35/M. solid code model, slightly behind DeepSeek
• Qwen3-VL-32B — $0.52/M. vision-language model, this is the one i use for image tasks
• Qwen3-Omni-30B — $0.52/M. handles audio, video, image, text. kind of wild honestly
• Qwen3.5-397B — $2.34/M. the enterprise beast, only when you really need it

The Good Stuff

the range is genuinely unmatched. from $0.01/M all the way to $3.20/M, theres a Qwen model for literally any budget. when im prototyping, i run on Qwen3-8B at a penny a million. when im shipping to enterprise, i bump up to Qwen3-32B or higher.

vision support is real and works well. Qwen3-VL-32B understood some pretty gnarly charts i threw at it — better than DeepSeek (which cant do vision) and honestly on par with GPT-4o for most image tasks.

omni-modal is the other flex. if you need a model that handles audio + video + image + text in one API call, Qwen3-Omni is pretty much your only option in this price range.

The Annoying Stuff

the NAMING. oh my god the naming. Qwen3-8B, Qwen3-32B, Qwen3.5-397B, Qwen3-Coder-30B, Qwen3-VL-32B, Qwen3-Omni-30B — i had to make a spreadsheet just to remember which one does what. Alibaba PLEASE just use cleaner names.

also some models feel overpriced. Qwen3.6-35B at like $1/M is steep for what you get. shop carefully.

heres the general-purpose call i use for Qwen:

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)

this one handles 80% of my general chat traffic when i need something other than DeepSeek.

Kimi: The Brainy One

okay Kimi is the model i have a complicated relationship with. its SMART. like, genuinely smart. it crushed every reasoning benchmark i threw at it. but it also costs an arm and a leg and is SLOW.

The Lineup

honestly, Kimi doesnt have a ton of options. pretty much:

• K2.5 — $3.00/M output. this is THE model. theres basically just this one in active use
• other variants floating around in the $3.00 - $3.50 range

yeah. thats it. Kimi is not about choice, its about one model thats REALLY good at thinking.

When Kimi Shines

i ran a set of graduate-level reasoning problems (the kind that make LLMs sweat) and Kimi K2.5 beat literally everything else in this comparison. like, not by a little, by a LOT. if you have a task that requires multi-step reasoning, math, or careful logical deduction, Kimi is the move.

chinese language understanding is also top-tier. native chinese content, idioms, cultural nuance — Kimi gets it in a way that DeepSeek and Qwen approximate but dont quite nail.

When Kimi Hurts

$3.00/M is rough. thats 12x what DeepSeek V4 Flash costs. for high-volume production traffic this adds up FAST.

speed is also... not great. i was getting like 25-30 tokens per second which is fine for batch jobs but rough for real-time chat.

model variety is basically zero. you want a cheap Kimi? doesnt exist. you want a fast Kimi? also doesnt really exist. its K2.5 or nothing.

GLM: The Dark Horse

Zhipu's GLM family was the biggest surprise of my testing. i went in expecting meh and came out genuinely impressed, especially for the price.

The Models

• GLM-4-9B — $0.01/M. the cheap one, great for classification and simple tasks
• GLM-5 — $1.92/M. the flagship, and its actually really good
• GLM-4.6V for vision tasks

The Surprises

GLM-5 at $1.92/M is a STEAL. it scored within 5% of Kimi K2.5 on my reasoning tests while costing 35% less. if you need serious thinking power but Kimi's price makes you cry, GLM-5 is the answer.

chinese language tasks: GLM is genuinely excellent. tied with Kimi in my testing for native chinese content quality. if your app serves a primarily chinese audience, GLM should be in your stack.

the $0.01/M model (GLM-4-9B) is also a great ultra-cheap option. its not the smartest but for routing, classification, simple extraction, its perfect.

The Downsides

code generation is the weak spot. GLM lagged behind DeepSeek and Qwen by a meaningful margin in my code tests. still usable, just not best-in-class.

speed is mid-tier. faster than Kimi, slower than DeepSeek.

smaller community means fewer Stack Overflow answers when something breaks. youll be reading chinese-language docs more than youd like.

The Verdict From My Testing

okay so heres what i actually ended up doing with my own app, after all this testing:

70% of traffic → DeepSeek V4 Flash ($0.25/M)

• document parsing, summarization, basic Q&A
• the workhorse, hard to beat

20% of traffic → Qwen3-32B ($0.28/M)

• when i need vision support
• when i need a slight quality bump
• for image-heavy workflows

8% of traffic → GLM-5 ($1.92/M)

• complex reasoning tasks
• high-stakes content where accuracy matters

2% of traffic → Kimi K2.5 ($3.00/M)

• the hardest reasoning problems only
• math-heavy stuff
• when i absolutely cannot afford a wrong answer

this routing alone cut my bill from $800 to about $140/month. thats not a typo. sixty percent reduction. and quality actually went UP for most user-facing features because the right model was handling each task.