I Tested DeepSeek, Qwen, Kimi, and GLM Side by Side — Here's the Truth

Here's the thing: i Tested DeepSeek, Qwen, Kimi, and GLM Side by Side — Here's the Truth

Look, I'll be honest with you. I've been burned too many times by proprietary walled gardens. Every time I build something cool on a closed API, the rug gets pulled — prices jump, terms change, models get deprecated overnight. So when a new wave of Chinese models started shipping under Apache and MIT licenses with actual open weights, I paid attention. Real attention.

Over the past few months I've been hammering on four families through Global API's unified endpoint: DeepSeek, Qwen, Kimi, and GLM. All of them give me that sweet OpenAI-compatible interface, which means I can swap providers with one line of code instead of rewriting my whole stack. Let me walk you through what I actually found.

My Short Answer Before We Dive In

If you're impatient (I get it), here's the TL;DR from someone who's migrated three production apps this quarter:

• Best bang for your buck: DeepSeek V4 Flash at $0.25/M output — genuinely shocking quality at that price
• Most variety in one family: Qwen — they ship more model sizes than anyone else
• Pure reasoning nerd territory: Kimi K2.5 at $3.00/M
• Chinese-language tasks: GLM wins, no contest

But that table doesn't tell the whole story. Let me show you why.

The Full Cheat Sheet

What I Care About	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
Budget Pick	V4 Flash @ $0.25/M	Qwen3-8B @ $0.01/M	N/A	GLM-4-9B @ $0.01/M
My Daily Driver	V4 Flash @ $0.25/M	Qwen3-32B @ $0.28/M	K2.5 @ $3.00/M	GLM-5 @ $1.92/M
Code Generation	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐
Chinese Quality	⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐
English Quality	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐
Raw Reasoning	⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐
Tokens Per Second	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐⭐
Vision/Multimodal	Limited	✅ (VL, Omni)	❌	✅ (GLM-4.6V)
Context Window	Up to 128K	Up to 128K	Up to 128K	Up to 128K
OpenAI Compatible	✅	✅	✅	✅

All four run on Apache 2.0 or MIT-licensed weights for at least their smaller variants, which is more than I can say for most Western alternatives. That matters to me.

Why I'm Even Comparing These Four

Before I get into the deep dives, let me explain the philosophy here. I refuse to build on top of any closed-source walled garden I can't audit or self-host if I need to. The moment a vendor can change their pricing or kill a model on a Tuesday afternoon without warning is the moment I start sweating.

These four Chinese labs have done something interesting: they released actual model weights. Not "we published a paper about it" — the real weights, with permissive licenses. DeepSeek's older releases are MIT-licensed. Qwen3 models ship under Apache 2.0. GLM-4-9B and Kimi's smaller variants are similarly permissive. That's the kind of freedom I want to bet on.

Plus, the inference costs are roughly an order of magnitude lower than the equivalent Western proprietary models. I'm not going to apologize for caring about both.

DeepSeek: My Default Starting Point

I keep coming back to DeepSeek. There's something almost suspicious about how good V4 Flash is at $0.25 per million output tokens. Let me break down their current lineup:

Model	Output $/M	What I Use It For
V4 Flash	$0.25	Daily coding, content, everything
V3.2	$0.38	When I want the newer architecture
V4 Pro	$0.78	Production-critical work
R1 (Reasoner)	$2.50	Math, logic, chain-of-thought stuff
Coder	$0.25	Code-specific tasks

What I Love

The price-to-performance ratio is genuinely absurd. V4 Flash at $0.25/M produces output that I'd happily pay $5/M for elsewhere. I ran it against my standard battery of coding prompts — the same ones I use to evaluate models for my day job — and it consistently landed in the top tier.

Speed is another win. I'm seeing roughly 60 tokens per second on V4 Flash, which means my streaming UIs feel snappy instead of sluggish. For an interactive chatbot or a code completion widget, that latency difference is the difference between "feels magical" and "feels broken."

English output is excellent. Honestly, indistinguishable from the big proprietary Western models for most tasks I've thrown at it.

Where It Falls Down

Vision is the obvious gap. DeepSeek still doesn't ship a true multimodal flagship that handles images natively. For OCR or image captioning, I have to reach for Qwen or GLM.

Chinese-language quality is good — really good — but Kimi and GLM edge it out on the benchmarks I've run. If I'm building something specifically for a Chinese-speaking audience, I usually pick one of those instead.

Model variety is more limited. Qwen ships dozens of sizes; DeepSeek gives me maybe five serious options. That's a tradeoff.

My Actual DeepSeek Code

Here's what I run most often when I'm just exploring an idea:

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"}],
    stream=True
)

for chunk in response:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

One endpoint. One client. Switch models by changing a string. That's the dream.

Qwen: The Family That Has Everything

If DeepSeek is my scalpel, Qwen is my entire toolbox. Alibaba's team has been cranking out model variants at a pace that frankly exhausts me, but the upside is that there's almost always a Qwen that fits whatever weird constraint I have.

Model	Output $/M	What I Use It For
Qwen3-8B	$0.01	Stupid-cheap bulk tasks
Qwen3-32B	$0.28	My general-purpose default
Qwen3-Coder-30B	$0.35	Coding tasks that need context
Qwen3-VL-32B	$0.52	Anything with images
Qwen3-Omni-30B	$0.52	Audio + video + image in one
Qwen3.5-397B	$2.34	Heavy enterprise reasoning
Qwen3.6-35B	$1.00	Mid-tier with a price tag I question

Yes, you read that right. Qwen3-8B is $0.01 per million output tokens. For context, that's about 100x cheaper than what I was paying for GPT-3.5 Turbo back in 2023. The price collapse on smaller models has been wild to watch in real time.

Why I Keep Coming Back

The range is unmatched. From $0.01/M at the bottom to $3.20/M at the top, Qwen covers literally every budget I've ever had. When I'm building a side project on a shoestring, I run Qwen3-8B for classification and extraction. When a client is paying enterprise rates, I reach for Qwen3.5-397B.

Vision is solid through the Qwen3-VL series. Image understanding, document parsing, chart comprehension — it all works without me needing a separate vision provider.

Omni-modal is a genuine superpower. Qwen3-Omni-30B handles audio, video, and images in a single model. No pipeline of separate services. For my multimodal prototypes, that's been a game-changer.

Alibaba's infrastructure is no joke. These things stay up. Latency is consistent. I've never had a Qwen endpoint go dark during a demo, which is more than I can say for some Western alternatives.

What Annoys Me

Naming is genuinely confusing. Qwen3, Qwen3.5, Qwen3.6, Qwen3-Coder, Qwen3-VL, Qwen3-Omni — I have a notes file just to remember what's what. I get that they're shipping fast, but a version-numbering overhaul would save me from myself.

English is good, not stellar. Solidly above average, occasionally behind DeepSeek on tricky phrasing. For pure English copy where nuance matters, I usually pivot.

Some pricing puzzles me. Qwen3.6-35B at $1.00/M feels steep for what you get. I'd either go smaller or jump to Qwen3.5-397B if I needed that quality.

My Qwen Pattern

For general coding and reasoning, this is what I run:

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)

That string swap is the entire migration cost. No new SDK. No new auth flow. Just a different model name.

Kimi: The Brain That Costs You

I'll be upfront: Kimi is the model family I reach for least often, but when I do reach for it, I know I'm getting something special. Moonshot AI built these for reasoning, and you can feel it.

Model	Output $/M	What I Use It For
K2.5	$3.00	Hard reasoning, math, logic chains
K2 Thinking	$3.50	Multi-step planning, research

The Reasoning Edge

On benchmarks where you measure multi-hop reasoning, mathematical proof, and complex planning, Kimi is consistently at or near the top. When I gave it a research task that required connecting five different sources of context, it produced the cleanest output of any model I tested — including ones that cost twice as much.

The trade-off is straightforward: Kimi doesn't do budget. Every model in their lineup is $3.00/M or higher. For high-volume production traffic, that's a hard pill to swallow.

Other Trade-offs

There's no multimodal flagship. Kimi is text-only for now. If you need image or audio understanding, look elsewhere.

Speed is the slowest of the four. K2.5 clocks in around 25-30 tokens per second in my tests. Fine for batch processing, painful for interactive chat.

But when reasoning quality is the only thing that matters — research agents, complex planning pipelines, anything where wrong answers cost real money — Kimi earns its premium.

GLM: The Chinese-Language Champion

Zhipu AI's GLM family has a very specific identity: it's the best of these four for native Chinese-language tasks. If I'm building anything for a mainland China audience, GLM is almost always my pick.

Model	Output $/M	What I Use It For
GLM-4-9B	$0.01	Cheap Chinese classification
GLM-5	$1.92	Full-fat Chinese quality

Why GLM Earns Its Spot

Chinese benchmarks are where GLM shines brightest. Idioms, classical references, regional dialects — it handles nuances that other models flatten or miss entirely.

GLM-5 at $1.92/M is genuinely competitive with anything in this price range, and it comes with proper multimodal support through the GLM-4.6V variant.

GLM-4-9B at $0.01/M is the cheapest serious model in this entire comparison. For bulk Chinese-language processing — think comment moderation, customer support routing, content tagging at scale — it's absurdly cost-effective.

The Trade-offs

Code generation lags behind DeepSeek and Qwen. Not catastrophically, but noticeably. For greenfield code work, I keep coming back to the other two.

English is fine but unremarkable. If my project is English-first, GLM isn't my first call.

When I Use GLM

Honestly, my GLM usage pattern is narrow but important. Whenever a project touches Chinese content in a way that matters, GLM is the answer. Everything else, I'm reaching for DeepSeek or Qwen.

The Vendor Lock-In Test

Here's the thing I want to highlight. All four of these providers expose OpenAI-compatible APIs through Global API. That means I'm not actually locked into any single vendor. If DeepSeek raises prices overnight, I swap to Qwen by changing one string. If Qwen deprecates a model, I pivot to GLM. None of them have me hostage.

Compare that to the Western proprietary walled gardens. Their SDKs don't talk to each other. Their pricing pages change weekly. Their models disappear with three months of notice. I'm not building a business on top of that.

The open weights underneath these models — Apache 2.0, MIT, similar permissive licenses — mean I could self-host any of them if I really needed to. That optionality is worth real money to me.

How I Actually Decide Which Model to Use

After months of testing, here's my decision tree:

1. Is this for a Chinese-speaking audience at scale? → GLM-5 or GLM-4-9B
2. Do I need hard multi-step reasoning? → Kimi K2.5
3. Is this general English production work? → DeepSeek V4 Flash
4. Do I have weird constraints (ultra-cheap, vision, omni-modal)? → Some Qwen variant
5. Am I prototyping and want the best default? → Qwen3-32B

The beautiful thing is, I can A/B test any of these in production with basically