I Spent 30 Days Pitting DeepSeek Against Qwen, Kimi, and GLM
honestly, I never thought I'd care this much about Chinese AI models. Like, a year ago I was happily paying OpenAI $10/M output and calling it a day. but then I started hearing whispers in dev communities about these four model families coming out of China that were... actually really good? and WAY cheaper?
so I did what any self-respecting indie hacker would do. I dropped everything, grabbed my credit card (carefully lol), and spent a solid month hammering these models through Global API's unified endpoint to figure out which one actually deserves my money.
heres what I learned. buckle up, this is gonna be a long one.
Why I Even Bothered
Look, my SaaS was eating API costs like crazy. I was running somewhere around 8 million tokens a month through GPT-4o and watching my profit margins shrink every single billing cycle. Something HAD to give.
I kept seeing posts about DeepSeek and Qwen especially, with developers claiming they switched and cut their bill by 80-90%. That sounded fake honestly. But I was desperate enough to find out.
The TL;DR after my testing? DeepSeek V4 Flash absolutely crushed it on price-to-performance. Qwen has the most options. Kimi K2.5 is a reasoning BEAST. And GLM is the secret weapon for Chinese-language work.
Let me break it all down.
The Cheat Sheet (a.k.a. what I wish I knew on day one)
before I dive into the long version, heres a quick table that would have saved me like a week of trial and error:
| What I Cared About | DeepSeek | Qwen | Kimi | GLM |
|---|---|---|---|---|
| Who made it | DeepSeek (幻方) | Alibaba (阿里) | Moonshot AI (月之暗面) | Zhipu AI (智谱) |
| Price range | $0.25-$2.50/M | $0.01-$3.20/M | $3.00-$3.50/M | $0.01-$1.92/M |
| Budget pick | V4 Flash @ $0.25 | Qwen3-8B @ $0.01 | nope, all premium | GLM-4-9B @ $0.01 |
| Best overall | V4 Flash @ $0.25 | Qwen3-32B @ $0.28 | K2.5 @ $3.00 | GLM-5 @ $1.92 |
| Code quality | stellar | great | good | decent |
| Chinese skills | great | great | GOD tier | GOD tier |
| English skills | stellar | great | great | great |
| Reasoning brain | good | good | ACTUALLY smart | good |
| Speed demon? | YES | fast | meh | fast |
| Can see images? | limited | yes (VL, Omni) | no | yes (GLM-4.6V) |
| Context length | 128K | 128K | 128K | 128K |
| OpenAI compatible? | yes | yes | yes | yes |
All four speak OpenAI's API dialect, which is HUGE. Means I didn't have to rewrite any of my existing client code. I just swapped the base URL and tweaked the model name. took like 20 minutes total.
DeepSeek: The Underdog That Made Me Question Everything
okay so deepseek was the FIRST one I tested. I had heard so much hype I rolled my eyes a little, ngl.
I started with V4 Flash at $0.25/M output. Twenty. Five. Cents. Per. Million. Tokens.
I ran my standard test prompt — "explain quantum computing in 100 words" — and... it was good. like, really good. I literally went back to the same prompt on GPT-4o to compare and honestly, I couldn't tell the difference 8 times out of 10. the other 2 times GPT-4o was slightly more concise, but not $9.75/M better. no way.
The DeepSeek Lineup I Actually Tested
heres what I ended up using and how much it cost me:
- V4 Flash at $0.25/M — became my daily driver. coding, content, general chatbot stuff. never let me down
- V3.2 at $0.38/M — newest architecture, felt snappier in some edge cases
- V4 Pro at $0.78/M — when I needed that extra quality bump for client deliverables
- R1 (the reasoner) at $2.50/M — pulled this out for math-heavy or logic puzzles. it's SLOW but accurate
- Coder at $0.25/M — specifically tuned for code. honestly, V4 Flash did code just as well for me
What Made Me Love It
the price-to-performance is INSANE. like genuinely, $0.25/M for something that competes with $10/M models? thats a 40x difference. my monthly bill went from like $80 to about $2. I had to triple check I wasn't being charged wrong lol.
code generation? chef's kiss. I ran the usual HumanEval and MBPP benchmarks and it consistently hit the top tier. my actual real-world testing (shipping features, debugging random errors) backed this up.
speed was another surprise. V4 Flash pushes around 60 tokens per second, which is among the fastest I've ever used. felt snappy in my Streamlit demos.
Where It Fell Short
vision support is limited. if I need to analyze screenshots or product images, I have to jump to another model. not ideal.
chinese-language quality is good but not the best. GLM and Kimi both edged it out in my Chinese-content tests (I have a few Mandarin-speaking beta testers who helped me blind-test responses).
also, fewer model sizes. Qwen has like 15 different SKUs. DeepSeek has maybe 6. sometimes you want more granularity.
Heres My V4 Flash Setup
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)
Works perfectly. no weird errors, no format issues, just clean responses.
Qwen: The One With Too Many Models (and I Mean That As A Compliment)
after deepseek won me over, I figured I'd try Qwen since everyone in the Alibaba dev community was raving about it.
Alibaba basically took the "throw everything at the wall" approach. and I kinda respect it.
The Models Worth Knowing
- Qwen3-8B at $0.01/M — ONE CENT. for ultra-light tasks like classification, simple extraction, autocomplete. you cant beat this
- Qwen3-32B at $0.28/M — the sweet spot. my general-purpose recommendation
- Qwen3-Coder-30B at $0.35/M — specialized for code. slightly better than V4 Flash for tricky refactors
- Qwen3-VL-32B at $0.52/M — vision-language model. actually understands images
- Qwen3-Omni-30B at $0.52/M — audio, video, image, all at once. kinda wild
- Qwen3.5-397B at $2.34/M — enterprise-tier reasoning. heavy hitter
What I Loved
the RANGE. from $0.01 to $3.20/M output, you can find a Qwen model for literally any budget. when I was bootstrapping and watching every penny, I used Qwen3-8B for simple stuff and saved a fortune.
the vision models are legit. Qwen3-VL-32B handled my product image classification tasks better than some dedicated vision APIs I've tried. and the Omni model? I piped some YouTube transcripts + video frames through it for a research project and it actually synthesized coherent summaries. blew my mind a little.
also, Alibaba's infrastructure is no joke. uptime was solid, latency was consistent, and I never hit weird rate limits.
The Annoying Parts
the naming. GOD, the naming. Qwen3-8B, Qwen3-32B, Qwen3-Coder-30B, Qwen3.5-397B, Qwen3.6-35B... I had to keep a literal spreadsheet. when you're switching models in your code, its easy to typo and suddenly you're paying 100x more than you planned.
english quality is good, not great. noticeably a step behind DeepSeek for nuanced English content. if your product is English-first, I'd lean DeepSeek.
and some models feel overpriced. Qwen3.6-35B at $1/M output is steep when Qwen3-32B at $0.28/M gets you 90% of the way there for most tasks.
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)
Same OpenAI client. Just swap the model name. it really is that easy.
Kimi: The Brainy One That Costs A Premium
okay so Kimi was the curveball for me. while deepseek and Qwen are duking it out on price, Kimi said "no thanks, we're gonna charge $3.00+/M and you're gonna LIKE it."
and honestly? for certain tasks, I did.
The Kimi Reality
- K2.5 at $3.00/M output — their flagship. THE reasoning model
- rest of the lineup sits in the $3.00-$3.50/M range
yep. no budget tier. no "lite" version. Kimi is premium-only.
Why People Pay The Premium
I tested K2.5 on some genuinely hard reasoning problems. like, the kind of multi-step logic puzzles that make most LLMs hallucinate halfway through. Kimi just... got them right. consistently.
if you need a model to actually THINK through complex problems (math olympiad style stuff, scientific reasoning, planning tasks), K2.5 is the real deal. its in a different league from the others on this metric.
for my SaaS specifically? I dont need that level of reasoning. but if I were building, say, a research assistant or an AI tutor or a code review tool that needs deep analysis, I would absolutely pay $3/M for this.
The Downsides
obvious one: PRICE. 12x more expensive than V4 Flash, 10x more than Qwen3-32B. you better REALLY need those reasoning chops.
speed is also slower. K2.5 takes its sweet time. for interactive chat where users want snappy responses, this is a real concern.
no vision support at all. limited multimodal capabilities.
Who Should Use Kimi
basically anyone doing serious reasoning work. if your product needs the model to actually solve problems, not just generate plausible text, K2.5 is worth every penny. for everyone else, the value math gets tough.
GLM: The Chinese-Language Champion (And A Solid All-Rounder)
GLM was the last one I tested, and it ended up surprising me the most.
Zhipu AI made it, and its basically the "well-rounded" option in this lineup. doesnt win any single category outright, but it shows up strong everywhere.
The GLM Lineup
- GLM-4-9B at $0.01/M — tied with Qwen3-8B as the cheapest viable model I've found
- GLM-5 at $1.92/M — the flagship, competes with top-tier Western models
What Impressed Me
chinese-language quality. wow. for Mandarin content, GLM tied with Kimi at the top of my rankings. if you serve Chinese users, this is a MUST test.
GLM-4.6V (vision model) handled image tasks well. not as polished as Qwen3-VL in my tests, but totally serviceable.
the price spread is also nice. $0.01 for budget work, $1.92 for premium, with a few models in between. I could route tasks intelligently: cheap model for classification, premium model for generation.
The Cons
code generation is the weakest of the four. not BAD, just not at DeepSeek/Qwen level. for a code-heavy product, I'd go elsewhere.
english quality, like Qwen, is good but a step behind DeepSeek. not a deal-breaker, just noticeable.
The Real-World Numbers From My Testing
okay so lemme put some actual data on this. over 30 days I ran the following workloads through each model:
- 2.3M tokens of customer support chat
- 1.8M tokens of code generation/debugging
- 1.2M tokens of content writing
- 0.8M tokens of classification/extraction
- 0.5M tokens of reasoning-heavy tasks
heres what I spent on each:
- DeepSeek (mostly V4 Flash + some R1): $1.47 total
- Qwen (mix of 8B, 32B, Coder-30B, VL-32B): $0.89 total
- Kimi (just K2.5 for the hard stuff): $15.00 total
- GLM (mix of 4-9B and 5): $2.18 total
for comparison, the SAME workload on GPT-4o would have been roughly $58.00.
yeah, you read that right. I went from ~$80/month to under $20/month total across all four providers.
What I Actually Ship With Today
heres my current routing strategy:
- 80% of traffic → DeepSeek V4 Flash ($0.25/M). best bang for buck
- 15% of traffic → Qwen3-32B ($0.28/M). when I need a slight quality bump or vision
- 4% of traffic → GLM-4-9B ($0.01/M). classification and routing logic
- 1% of traffic → Kimi K2.5 ($3.00/M). the gnarly reasoning problems
this setup gives me GPT-4o-tier quality for most things, premium reasoning when I need it, and a bill under $10/month. I literally smile every time I check my dashboard.
The Honest Truth About Quality
now heres the part where I gotta be real with you. ALL of these models are good. like, genuinely impressive. the gap between the worst and best on my quality tests was way smaller than the price gap suggested.
for 90% of indie hacker use cases — chatbots, content gen, code help, data extraction — DeepSeek V4 Flash is more than enough. stop overthinking it. stop paying OpenAI $10/M. seriously.
if you need vision? go Qwen.
if you need reasoning? go Kimi.
if you need Chinese? go GLM.
if you need to save money? go DeepSeek.
its that simple.
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