DeepSeek vs Qwen vs Kimi vs GLM: A Bootcamp Dev's Take

DeepSeek vs Qwen vs Kimi vs GLM: A Bootcamp Dev's Take

So I just graduated from a coding bootcamp a few months ago, and honestly? I thought I had a decent handle on the AI landscape. GPT-4o, Claude, maybe Gemini — that was my mental map. Then one night I was scrolling through some dev forums and someone mentioned DeepSeek was beating GPT-4o on certain coding benchmarks for like 1/40th the price. I literally said "wait, what" out loud to my empty apartment. That sent me down a rabbit hole I wasn't prepared for.

Turns out China has been cooking up some genuinely incredible AI models, and most Western devs (me included, until recently) are completely oblivious. I spent the last two weeks testing four of the big ones — DeepSeek, Qwen, Kimi, and GLM — through this unified API service called Global API. And I have to share what I found because some of these numbers genuinely blew my mind.

Why I Even Bothered Looking at Chinese AI Models

Here's the thing — at my bootcamp, we mostly used OpenAI's API for our projects. I built a customer support chatbot, a code review tool, and a few other random apps. By the end, my OpenAI bill was... not great. I knew there had to be cheaper options, but I assumed anything significantly cheaper would be a massive quality drop. That's the assumption I was operating under.

Then I learned about these Chinese models, and I had no idea how mature the ecosystem was. We're talking about models from companies like DeepSeek (their parent company is literally called "High-Flyer" in Chinese — 幻方), Alibaba (you know, the e-commerce giant), Moonshot AI (月之暗面, which literally translates to "Dark Side of the Moon"), and Zhipu AI (智谱). Big serious companies with serious research teams.

The biggest revelation? All four of these model families are OpenAI API-compatible. Meaning I could literally swap them into my existing code with maybe a one-line change. I was shocked this was even possible.

The Quick Comparison (The Part That Made Me Spit Out My Coffee)

Before I get into the details, let me give you the overview table that honestly changed how I think about AI pricing:

Feature	DeepSeek	Qwen	Kimi	GLM
Developer	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
Best Budget Model	V4 Flash @ $0.25/M	Qwen3-8B @ $0.01/M	N/A (all premium)	GLM-4-9B @ $0.01/M
Best Overall	V4 Flash @ $0.25/M	Qwen3-32B @ $0.28/M	K2.5 @ $3.00/M	GLM-5 @ $1.92/M
Code Generation	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐
Chinese Language	⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐
English Language	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐
Reasoning	⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐
Speed	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐⭐
Vision/Multimodal	Limited	✅ (VL, Omni)	❌	✅ (GLM-4.6V)
Context Window	Up to 128K	Up to 128K	Up to 128K	Up to 128K
API Compatibility	OpenAI ✅	OpenAI ✅	OpenAI ✅	OpenAI ✅

Let me just call out a few things that made me do a double-take. Qwen has a model for $0.01 per million tokens. That's not a typo. One cent. For comparison, when I was using GPT-4o for my bootcamp projects, I was paying around $10 per million output tokens. That's literally 1000x more expensive.

Also, DeepSeek V4 Flash at $0.25 per million tokens is being compared favorably to GPT-4o in terms of quality? For $0.25? I had to read that like three times.

DeepSeek: The One That Started This Whole Thing

Okay so DeepSeek is the one that started my spiral. I was reading some Reddit thread where someone said they switched their entire production app from GPT-4o to DeepSeek and their monthly bill dropped from $800 to like $20. I assumed they were exaggerating. Then I looked at the actual pricing.

Here's the breakdown of DeepSeek's main models:

Model	Output $/M	Best For
V4 Flash	$0.25	Daily use, coding, content
V3.2	$0.38	Latest architecture
V4 Pro	$0.78	Production quality
R1 (Reasoner)	$2.50	Complex math, logic
Coder	$0.25	Code-specific tasks

What I Loved About DeepSeek

The first thing I tested was V4 Flash for code generation, and I was genuinely impressed. I threw some HumanEval-style problems at it (the ones we used in bootcamp) and it was hitting answers that were basically identical to what GPT-4o was giving me. For $0.25/M output. Let me say that again. Quarter of a dollar per million tokens.

Speed was the other thing that surprised me. The docs say V4 Flash runs at around 60 tokens per second, which is honestly faster than what I was getting from OpenAI's API most days. For someone building real-time applications, that's huge.

The English language performance is also rock solid. I'm building English-language products, and I never once felt like I was getting "Chinese-translated-to-English" quality output. It's clean, natural, and idiomatic.

Where DeepSeek Falls Short

Here's the thing nobody tells you upfront — DeepSeek has limited vision capabilities. If your app needs to process images, you're kind of out of luck with their main models. There's no native image understanding like you'd get from GPT-4o or Claude.

Also, if you're building something for Chinese-language users specifically, DeepSeek is good but not the absolute best. GLM and Kimi apparently edge it out on Chinese benchmarks. I can't really verify this since my Chinese is basically nonexistent, but I'll take the experts' word for it.

How I Actually Used It

Here's the code I ended up with for one of my projects. It's almost embarrassingly simple:

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",  # V4 Flash
    messages=[{"role": "user", "content": "Explain quantum computing in 100 words"}]
)
print(response.choices[0].message.content)

That's it. That's literally the entire change from my OpenAI code. I just swapped out the model name and the base URL. My existing error handling, my streaming logic, my entire infrastructure — all of it just worked. I was kind of stunned.

Qwen: The Swiss Army Knife I Didn't Know I Needed

After DeepSeek, I moved to Qwen, and this is where things got really interesting. Qwen is made by Alibaba (yes, the same Alibaba that does the e-commerce thing), and they have a frankly absurd number of models. Like, when I first pulled up their model list, I just stared at it for a minute.

Here's their lineup:

Model	Output $/M	Best For
Qwen3-8B	$0.01	Ultra-light tasks
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	Multimodal
Qwen3.5-397B	$2.34	Enterprise reasoning

The Range Is Wild

The thing about Qwen is they have a model for literally every price point. Need to classify some user feedback for basically free? Qwen3-8B at $0.01/M. Need something for production-grade reasoning? Qwen3.5-397B at $2.34/M. There's this entire gradient in between.

For my projects, I ended up using Qwen3-32B as my general-purpose workhorse at $0.28/M. The quality was excellent — comparable to DeepSeek V4 Flash for most tasks, and sometimes better depending on what I was asking. I tested it on code, on content generation, on summarization — across the board, very solid.

Vision and Multimodal

This was the big win for me with Qwen. Their Qwen3-VL-32B model handles images, and their Qwen3-Omni-30B does audio, video, and image all in one model. If you're building anything that needs to understand more than just text, Qwen has you covered in ways that DeepSeek doesn't.

I tried the vision model on some product images for a side project I'm working on, and it correctly identified and described everything I threw at it. For $0.52/M. Meanwhile, OpenAI charges way more for their vision capabilities.

The Annoying Part

Okay, real talk — Qwen's naming is confusing. Like, really confusing. Qwen3, Qwen3.5, Qwen3.6, Qwen3-Coder, Qwen3-VL, Qwen3-Omni... I had to make a spreadsheet to figure out which model was which and what it did best. That's the kind of thing that makes onboarding new developers harder than it needs to be.

Also, some of their pricing feels steep. Qwen3.6-35B at around $1/M output — for that price, I'd probably just use DeepSeek V4 Pro at $0.78/M.

Code Example for Qwen

Here's what using Qwen3-32B looked like in my code:

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

Same client setup, same call structure, just a different model name. I cannot stress enough how nice this is for developer experience.

Kimi: The Reasoning Powerhouse That Costs More

Kimi is made by Moonshot AI, and these guys are positioning themselves as the "thinking" model of the bunch. Their pricing is higher than the others, and there's a reason.

Model	Output $/M	Best For
K2.5	$3.00	Complex reasoning, logic
K2	$3.50	Top-tier reasoning

When Kimi Makes Sense

If you need raw reasoning power, Kimi is apparently the best of the bunch. It tops reasoning benchmarks, and I tested it on some math problems and logic puzzles from my bootcamp coursework, and it was getting answers right that other models were missing.

The thing is, for $3.00/M output, it's not something I'd reach for every day. But for specific use cases — like solving complex algorithms, doing detailed analysis, or anything that requires multi-step reasoning — it's worth the premium.

Where Kimi Falls Down

No vision capabilities at all. If you need multimodal, look elsewhere. And the speed is the slowest of the four — around three stars compared to DeepSeek's five. For real-time applications, this could be a dealbreaker.

Also, since all Kimi models are in that $3.00-$3.50 range, there's no real "budget" option. You either use Kimi or you don't.

GLM: The Chinese-Language Champion

GLM comes from Zhipu AI (智谱), and they're the ones to beat if you're building for Chinese-language audiences.

Model	Output $/M	Best For
GLM-4-9B	$0.01	Ultra-budget tasks
GLM-5	$1.92	Best quality
GLM-4.6V	varies	Vision tasks

What Makes GLM Special

For Chinese-language tasks, GLM is apparently top-tier — tied with Kimi at five stars. Even though I'm not building for Chinese users myself, I tested GLM with some Chinese language inputs (Google Translate helped me construct them), and the responses felt really natural.

The other thing I appreciated: GLM has the GLM-4.6V model for vision tasks. So if you need both Chinese language and image understanding, this is your model.

GLM-5 at $1.92/M is their premium offering, and it's solid for general use. Not as cheap as DeepSeek or Qwen's mid-range models, but the quality holds up.

The Trade-offs

GLM's code generation is the weakest of the four at three stars. If you're building a developer tool or a code-heavy application, you might want to look elsewhere for that specific use case.

My Actual Recommendations After All This Testing

Okay so if you're a fellow bootcamp grad or just a developer trying to figure out what to use, here's my honest take after spending two weeks with all of these:

For most use cases, start with DeepSeek V4 Flash. At $0.25/M, it's the best price-to-performance ratio. It's fast, it's good at code, and it handles English beautifully. This is now my default for like 80% of what I build.

If you need vision or multimodal, go with Qwen. The Qwen3-VL series or the Qwen3-Omni model are your best bets. The pricing is reasonable and the capabilities are strong.

For reasoning-heavy tasks where you need the best answers, use Kimi K2.5. Yes, it costs more, but when you need that extra brain power, it's there.

For Chinese-language applications, pick GLM. It's the most natural at Chinese, and the pricing options are flexible.

The Setup That Made All This Possible

I have to mention the actual tool that made testing all four of these models painless: Global API. Instead of signing up for four different accounts, getting four different API keys, and writing four different integration paths, I just used one endpoint — https://global-apis.com/v1 — and pointed all my code at it. One client, multiple models, zero headaches.

For someone like me who's still figuring things out post-bootcamp, that kind of simplicity is huge. I didn't have to learn four different APIs or manage four different billing relationships. I just changed the model name in my requests and everything else stayed the same.

If you're curious about trying these models yourself, I'd definitely recommend checking out Global API. It's what made this whole exploration possible for me, and I think it's especially valuable for newer developers who don't want to deal with the overhead of juggling multiple API providers. Worth a look if you want to experiment without committing to a bunch of separate accounts.

Final Thoughts From a Recovering AI-Price-Ignorant Dev

Two weeks ago I thought I understood the AI API landscape. I was paying $10/M output tokens like a sucker and didn't know there were alternatives that were 40-1000x cheaper with comparable