DeepSeek vs Qwen vs Kimi vs GLM: I Tested All Four to Save Money

DeepSeek vs Qwen vs Kimi vs GLM: I Tested All Four to Save Money

I burned through my AI budget last quarter. Like, really burned through it. My OpenAI statement hit $847 in a single month and I had that sick feeling in my stomach when I opened the email notification. So I did what any developer with a credit card problem would do: I went hunting for cheaper alternatives. That's how I ended up spending two solid weeks putting four Chinese AI model families through their paces — DeepSeek, Qwen, Kimi, and GLM — all routed through Global API's unified endpoint.

Here's the thing nobody tells you about Chinese models: the pricing gap is absolutely wild. We're talking about models that handle 90% of what GPT-4o does for literally 2.5% of the cost. When I ran my first cost comparison, my jaw literally dropped. Check this out — I'm now spending around $40/month for the same workload that cost me $847. That's a 95% reduction, and I haven't even talked about the quality trade-offs yet (spoiler: there barely are any for most use cases).

Let me walk you through everything I learned.

Why I Picked These Four Models

I wanted to test models from each major Chinese AI lab to see what the real story was. The four big players right now are:

• DeepSeek — made by the folks at 幻方 (High-Flyer)
• Qwen — built by Alibaba (阿里)
• Kimi — developed by Moonshot AI (月之暗面)
• GLM — created by Zhipu AI (智谱)

All four offer OpenAI-compatible APIs, which means I could swap them in with basically zero code changes. That's wild if you think about it — the entire Western AI ecosystem has trained us to expect vendor lock-in, and these Chinese providers just said "yeah, we'll match that interface."

I tested everything through https://global-apis.com/v1 so I wouldn't have to manage four different API keys and dashboards. More on that later.

The Numbers That Made Me Do a Double-Take

Before I get into the deep dive on each model family, here's the cost breakdown that started this whole investigation:

Family	Price Range	Cheapest Option	Premium Option
DeepSeek	$0.25 – $2.50/M	V4 Flash @ $0.25/M	R1 Reasoner @ $2.50/M
Qwen	$0.01 – $3.20/M	Qwen3-8B @ $0.01/M	Qwen3.6-35B+ tiers
Kimi	$3.00 – $3.50/M	K2.5 @ $3.00/M	Flagship tiers
GLM	$0.01 – $1.92/M	GLM-4-9B @ $0.01/M	GLM-5 @ $1.92/M

Read that Qwen column again. $0.01 per million output tokens. That's not a typo. One penny for a million tokens. I had to verify that three times before I believed it.

For context, GPT-4o charges $10/M output tokens. So Qwen3-8B is literally 1000x cheaper for certain tasks. Even comparing apples to apples (Qwen3-32B at $0.28/M vs GPT-4o at $10/M), you're still looking at a 97% cost reduction. That's not optimization — that's a paradigm shift.

My Spending Comparison: Before vs After

I crunched the numbers on my actual usage. I run a SaaS product that does roughly 50 million output tokens per month across various features (content generation, code assistance, customer support). Here's what I was paying before vs what I pay now:

• GPT-4o (before): $500/month just on output tokens
• DeepSeek V4 Flash (after): $12.50/month
• Savings: $487.50/month, or roughly $5,850/year

And the quality? For my use cases, it's basically indistinguishable. The few tasks where I genuinely need top-tier reasoning, I route to Kimi K2.5 at $3.00/M, which is still 70% cheaper than GPT-4o.

DeepSeek: The Budget Champion

DeepSeek was the first model family I tested, and it immediately became my daily driver. Here's what stood out.

The standout model is V4 Flash at $0.25/M output tokens. That's the model doing the heavy lifting in my production app right now. It hits about 60 tokens per second in my benchmarks, which makes it one of the fastest models I've tested, period.

Here's the full DeepSeek lineup I evaluated:

Model	Output $/M	My Take
V4 Flash	$0.25	The sweet spot — fast, cheap, surprisingly good
V3.2	$0.38	Newer architecture, marginally better quality
V4 Pro	$0.78	When you need extra polish
R1 Reasoner	$2.50	Complex math and logic tasks
Coder	$0.25	Code-specific workloads

What I love about DeepSeek is the price-to-performance ratio. V4 Flash at $0.25/M genuinely rivals GPT-4o quality for 97.5% less money. I ran it through my standard test suite (HumanEval, MBPP, custom business logic tasks) and it scored within a few percentage points of GPT-4o on most code generation benchmarks.

The downsides? Vision capabilities are limited. If you need image understanding, you're out of luck with DeepSeek. Also, on Chinese-language benchmarks, GLM and Kimi tend to edge it out slightly. But for English workloads and code? It's hard to beat at this price.

Here's how I'm calling it in my actual code:

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)

That's it. Two lines of config, and I'm running DeepSeek instead of GPT-4o. My bill thanks me.

Qwen: The Versatile Workhorse

If DeepSeek is the budget champion, Qwen is the Swiss Army knife. Alibaba's model family has the widest range I've ever seen, from $0.01/M all the way up to $3.20/M.

Here's the Qwen lineup I tested:

Model	Output $/M	What I Used It For
Qwen3-8B	$0.01	Ultra-light classification, simple extraction
Qwen3-32B	$0.28	General-purpose workhorse
Qwen3-Coder-30B	$0.35	Code generation
Qwen3-VL-32B	$0.52	Image understanding
Qwen3-Omni-30B	$0.52	Multimodal tasks
Qwen3.5-397B	$2.34	Enterprise reasoning

The Qwen3-8B at $0.01/M is honestly absurd. I use it for tiny tasks like intent classification, extracting structured data from short inputs, and routing user queries. These are jobs where I was previously burning GPT-4o-mini tokens ($0.60/M) and overpaying by 60x.

The naming is a bit chaotic — I'm looking at you, Qwen3-VL-32B vs Qwen3-Omni-30B — but once you map out which model does what, the flexibility is unmatched. Qwen has dedicated vision models (VL series), omni-modal models that handle audio/video/image, and everything in between.

For my application, I ended up using Qwen3-32B ($0.28/M) as the "general purpose" default when I need slightly more capability than DeepSeek V4 Flash provides but don't want to pay Kimi prices.

One note: some of the larger Qwen models feel overpriced. Qwen3.6-35B at around $1/M output is steep unless you really need that specific capability. For me, the sweet spot in the Qwen lineup is definitely the 8B and 32B tiers.

Kimi: The Premium Reasoning Pick

Now here's where things get interesting. Kimi from Moonshot AI is the priciest of the four — ranging from $3.00 to $3.50/M output tokens. That's still 65-70% cheaper than GPT-4o, but it's clearly positioned as a premium option.

Kimi doesn't play the budget game. Every model in their lineup is positioned as premium, and honestly? The quality justifies it for reasoning-heavy workloads.

The flagship model I tested is K2.5 at $3.00/M output tokens. It's the leader on reasoning benchmarks among Chinese models. When I threw complex multi-step logic problems at it, Kimi consistently outperformed DeepSeek and Qwen. We're talking about a model that can chain together 8-10 reasoning steps without losing the thread.

My use case for Kimi is narrow but valuable: anything that requires genuine reasoning — mathematical proofs, complex business logic, multi-hop question answering. I route maybe 10-15% of my traffic through Kimi, and the rest goes through DeepSeek or Qwen.

The tradeoff is speed. Kimi models feel slower than DeepSeek (I'd estimate roughly half the tokens/sec), and the per-token cost is 12x higher than V4 Flash. But for tasks where reasoning quality matters, it's worth every penny.

If your workload is heavily reasoning-focused and budget isn't the primary constraint, Kimi K2.5 is probably the best Chinese model you can buy right now. At $3.00/M, it's still a bargain compared to Western alternatives.

GLM: The Chinese-Language Specialist

GLM from Zhipu AI rounds out my testing. It's interesting because it has both the cheapest option in my entire comparison AND a competitive premium model.

The GLM lineup:

Model	Output $/M	Sweet Spot
GLM-4-9B	$0.01	Tied with Qwen for cheapest option
GLM-5	$1.92	Premium tier, $1.28 cheaper than Kimi

At $0.01/M for GLM-4-9B, you've got another contender for the ultra-budget tier alongside Qwen3-8B. I haven't found a meaningful quality difference between them for simple tasks.

The standout feature of GLM is Chinese language performance. It ties with Kimi for the top score on Chinese-language benchmarks. If your application serves a primarily Chinese-speaking audience, GLM deserves a serious look.

GLM also offers multimodal capabilities through GLM-4.6V, which gives it an edge over DeepSeek for vision tasks. The premium GLM-5 at $1.92/M is priced competitively — it's 36% cheaper than Kimi K2.5 while delivering comparable quality on most non-reasoning tasks.

For my use case, GLM ended up being my backup model. I route maybe 5% of traffic through it, primarily when I'm handling Chinese-language inputs or need a second opinion on a tricky problem.

What I Actually Run in Production

Here's my real routing logic after two weeks of testing:

• 70% of traffic → DeepSeek V4 Flash ($0.25/M) — the default for everything
• 15% of traffic → Qwen3-32B ($0.28/M) — when I need slightly more capability
• 10% of traffic → Kimi K2.5 ($3.00/M) — reasoning-heavy queries only
• 5% of traffic → GLM-5 ($1.92/M) — Chinese language and multimodal

My blended cost per million tokens comes out to about $0.52/M — that's a 95% reduction from my previous GPT-4o spend.

The Speed Question

One thing I didn't expect: DeepSeek V4 Flash hitting ~60 tokens/sec makes it genuinely faster than my experience with GPT-4o. For interactive applications where latency matters,