I Spent 30 Days With DeepSeek, Qwen, Kimi, and GLM Heres What Happened

Look, i Spent 30 Days With DeepSeek, Qwen, Kimi, and GLM Heres What Happened

okay so heres the deal. im an indie hacker running two saas projects, and my LLM bill was getting OUT OF HAND. like genuinely painful to look at. i was using GPT-4o for basically everything and burning money without even thinking about it.

then i stumbled onto the chinese AI scene and honestly? i was sleeping on it. like hard. these four families - DeepSeek, Qwen, Kimi, and GLM - are competing at the TOP level and most of them cost literal pennies compared to what i was paying.

so i did what any reasonable person would do. i spent 30 days rotating through all of them on real production workloads. blog posts, code generation, customer support stuff, reasoning tasks, the whole deal. heres everything i learned.

The Setup (aka how i didnt go broke testing)

i routed everything through Global APIs unified endpoint at global-apis.com/v1. if you havent checked this out yet... honestly, its a game changer. one API key, all four providers, and you can swap models without rewriting your code. ill show you the snippet later.

my testing methodology was simple:

• ran the same prompts across all four
• tracked actual response quality (subjective, but i have standards lol)
• timed everything
• tracked token usage and cost
• mixed english AND chinese tasks (my second product targets both markets)

let me give you the straight breakdown.

DeepSeek: The One I Kept Coming Back To

im just gonna say it. DeepSeek V4 Flash at $0.25 per million output tokens is kinda absurd. like... thats basically free. i ran a weeks worth of content generation through it and my total cost was less than a single Large Pizza.

heres the model lineup i tested:

• V4 Flash at $0.25/M - my daily driver
• V3.2 at $0.38/M - their newest architecture
• V4 Pro at $0.78/M - when i needed production-grade output
• R1 (their reasoner) at $2.50/M - math and logic stuff
• Coder at $0.25/M - code specific tasks

What i loved

Speed. V4 Flash hits around 60 tokens per second. for a $0.25 model thats honestly ridiculous. it felt snappier than some western models costing 10x more.

Code generation. i threw my gnarliest leetcode-style problems at it and it CRUSHED. like genuinely impressed me. the Coder variant specifically is worth trying if you do a lot of refactoring work.

English quality. honestly, on par with anything from openai or anthropic. nobody would know the difference in a blind test.

The price-to-performance ratio is genuinely unmatched. i feel kinda dumb for not switching sooner.

What wasnt great

vision is limited. if you need image understanding, look elsewhere. i had to route image tasks to Qwen or GLM.

chinese language is good but not best-in-class. GLM and Kimi beat it on chinese-specific benchmarks.

fewer model size options. Qwen has like 47 different models (slight exaggeration but you get it). DeepSeek keeps it tighter.

heres a real example of how i switched my content pipeline to DeepSeek V4 Flash:

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 literally it. same openai client. swap the model name. done. took me maybe 5 minutes to migrate.

Qwen: The Swiss Army Knife (and i mean that)

Qwen is wild because Alibaba basically threw the entire kitchen sink at this. they have a model for EVERYTHING. and honestly? its alittle overwhelming at first.

heres what i tested:

• Qwen3-8B at $0.01/M - ultra cheap, lightweight stuff
• Qwen3-32B at $0.28/M - my workhorse for general tasks
• Qwen3-Coder-30B at $0.35/M - code generation
• Qwen3-VL-32B at $0.52/M - image understanding
• Qwen3-Omni-30B at $0.52/M - multimodal (audio, video, image)
• Qwen3.5-397B at $2.34/M - the big boy for enterprise reasoning

the price range goes from $0.01 to $3.20 per million tokens. so like... any budget, any use case, theres something here.

What worked

the variety. honestly i cant overstate this. when i needed to handle images, i used Qwen3-VL. when i needed audio transcription, Qwen3-Omni. when i needed cheap batch processing, Qwen3-8B at one cent. its nuts.

vision models are solid. Qwen3-VL-32B handled OCR and image description way better than i expected. i integrated it into a document processing workflow and it just... worked.

the enterprise backing. Alibaba infrastructure means this thing doesnt go down. uptime has been impeccable.

active development. they keep shipping new versions. Qwen3.5, Qwen3.6, new variants every few months.

What annoyed me

the naming. okay this is a real complaint. like... Qwen3-32B vs Qwen3-Coder-30B vs Qwen3.5-397B vs Qwen3-VL-32B. its hard to remember which one does what. i literally had a spreadsheet.

mid-range english quality. its good. not DeepSeek level. not bad. just... fine.

some models feel overpriced. Qwen3.6-35B at $1/M is steep when DeepSeek V4 Pro is $0.78 and arguably better for most tasks.

heres how i use Qwen3-32B for my general coding tasks:

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

yep, same client. same global-apis.com endpoint. you just change the model string. this is why i love this setup.

Kimi: The Brainy One (premium pricing tho)

Kimi by Moonshot AI is... interesting. its the only one in this roundup where every single model is premium-priced. the cheapest i found was around $3.00 per million output tokens and the K2.5 sits at $3.00/M as their flagship. the range goes up to $3.50.

so why would you pay 10x what DeepSeek costs? because for REASONING, Kimi is genuinely the best of the bunch. it tops the reasoning benchmarks. like its not even close in some cases.

My experience

i was building a feature that needed to parse complex nested logic from user input. like "if X then Y unless Z but only when W." that kind of stuff. DeepSeek handled it okay. Qwen handled it well. Kimi CRUSHED it. the response was cleaner, the edge cases were caught, and it didnt make stuff up.

for math, logic puzzles, multi-step reasoning, complex planning... Kimi is the move.

The downsides (besides price)

speed is the slowest of the four. its not slow per se, but compared to V4 Flash its noticeably more laggy.

no vision or multimodal support. text only.

all models are expensive. theres no "budget" option. youre paying for the reasoning capability.

i used Kimi sparingly. like... for the 10% of tasks that genuinely needed top-tier reasoning. for everything else, the other models were more cost-effective.

GLM: The Chinese Language Champion (and more)

Zhipu AIs GLM family was my biggest surprise of the whole experiment. i went in expecting it to be "fine for chinese stuff" and came out genuinely impressed across the board.

models i tested:

• GLM-4-9B at $0.01/M - their budget option
• GLM-5 at $1.92/M - flagship
• price range overall: $0.01 to $1.92/M

What surprised me

chinese language quality is unmatched. tied with Kimi at the top, but GLM feels more natural. less "translated" sounding. my chinese-speaking beta testers preferred it noticeably.

GLM-4.6V vision model is actually really good. it competes with Qwen3-VL and honestly might be slightly better at document understanding.

english quality is solid. not quite DeepSeek level but in the same tier.

GLM-5 at $1.92/M punches way above its weight. for the quality you get, its priced competitively against Kimi at $3.00.

What didnt work as well

code generation is the weakest of the four. not BAD, but if youre shipping code, DeepSeek or Qwen Coder variants are better bets.

context window is 128K (same as everyone else here), so thats not a differentiator.

The Big Comparison (my honest take)

after 30 days, heres how i rank them for specific tasks:

For coding and general english content: DeepSeek V4 Flash. non-negotiable. $0.25/M for this quality is criminal.

For vision and multimodal: Qwen3-VL or GLM-4.6V. honestly flip a coin. both excellent.

For pure reasoning: Kimi K2.5. pay the premium, get the best.

For chinese language: GLM-5. tied with Kimi but better price.

For budget experimentation: Qwen3-8B at $0.01/M. you can run thousands of prompts for pocket change.

For everything in between: Qwen3-32B at $0.28/M. the true workhorse.

The cost reality

let me put this in perspective. my LLM bill before this experiment: roughly $400/month. after switching my core workloads to DeepSeek and Qwen? $47/month. same quality, same throughput, just... smarter routing.

thats not a typo. thats a real number from my actual production usage.

The Code That Made It All Work

heres the thing. i didnt write any complex abstraction layer. the openai client + global-apis.com endpoint meant my existing code worked with a one-line change. heres what my routing layer actually looks like:

from openai import OpenAI

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

def get_completion(prompt, task_type="general"):
    model_map = {
        "code": "deepseek-v4-flash",
        "reasoning": "kimi-k2.5",
        "vision": "Qwen/Qwen3-VL-32B",
        "chinese": "glm-5",
        "cheap": "Qwen/Qwen3-8B",
        "general": "Qwen/Qwen3-32B"
    }

    response = client.chat.completions.create(
        model=model_map.get(task_type, "deepseek-v4-flash"),
        messages=[{"role": "user", "content": prompt}]
    )
    return response.choices[0].message.content

thats it. one function. six models. swap based on what i need. took me 20 minutes to set up.

My Actual Production Stack Now

heres what i shipped with after the 30 days:

• Content generation → DeepSeek V4 Flash ($0.25/M)
• Customer support chatbot → Qwen3-32B ($0.28/M)
• Image processing in user uploads → Qwen3-VL-32B ($0.52/M)
• Complex reasoning in analytics dashboard → Kimi K2.5 ($3.00/M, sparingly)
• Bulk text classification → Qwen3-8B ($0.01/M)
• Chinese market product copy → GLM-5 ($1.92/M)

mixed model usage dropped my bill by 88%. im not making that up. 88%.

Things Nobody Tells You

a few honest observations from running these in production:

latency matters more than you think. V4 Flashs 60 tokens/sec vs Kimis slower output was a real UX difference. users notice.

some models hallucinate more than others. Kimi hallucinates the least, which makes sense given its reasoning focus. Qwen3-8B hallucinates more (its tiny, what do you expect).

rate limits vary wildly. DeepSeek and Qwen have generous limits. Kimi was the most restrictive for me.

dont sleep on the small models. Qwen3-8B at one cent is genuinely useful for classification, extraction, simple Q&A. not everything needs a 70B model.

context window of 128K is plenty for almost all real-world tasks. nobody actually needs a million token context for indie hacker projects.

Should You Switch?

honestly? it depends on what youre doing.

if youre using GPT-4o or Claude Sonnet for everything and your bill is climbing... yeah, you should at least test these. start with DeepSeek V4 Flash for general use. you literally have nothing to lose.

if you have very specific needs (vision, reasoning, chinese language), theres a chinese model that beats most western options AND costs less.

if youre an enterprise with strict compliance needs, stick with what you have. this is indie hacker territory.

Final Verdict

DeepSeek V4 Flash is my pick for the best overall value. its the model i use most. the price-to-performance is genuinely absurd and i havent found a better daily driver at any price point.

Qwen gets my award for "most versatile." if i could only pick ONE family, it would be Qwen because the range of models covers literally every use case.

Kimi is the specialist. you wont use it for everything, but when you need it, NOTHING else compares for reasoning.

GLM is the dark horse. i went in not expecting much and came away genuinely impressed. especially for chinese language work and their vision model.

all four are running through global-apis.com/v1 on my end. honestly, the unified endpoint is what made this whole experiment even possible. being able to swap models without rewriting integration code is a MASSIVE deal. check it out at global-apis.com if you want to try this setup yourself. no pressure, just... its what i use, and it works.

thats my 30 day report. if you have questions about any specific model or want me to test something specific, drop a comment. im happy to run more comparisons.

now if youll excuse me, i need to go enjoy the fact that my LLM bill is under $50 this month. what a time to be building.