I Tested Four Chinese LLMs So You Don't Have To — Here's What I Found

Look, I'll be honest with you — I almost didn't write this piece. The Western AI giants have trained us to believe that "serious" models must come from California, run in their walled gardens, and lock you into proprietary APIs with restrictive licenses. You know the drill: no fine-tuning, no self-hosting, rate limits that punish you for building something interesting, and ToS clauses that let the vendor read your prompts.

But then I started poking around at what Chinese labs were shipping under MIT and Apache licenses, and... well, my entire roadmap changed.

Over the past three months I've been hammering on DeepSeek, Qwen, Kimi, and GLM through Global API's unified endpoint. Same questions, same benchmarks, same prompts at 2am when nobody's watching. What follows is everything I learned, including the pricing surprises that made me question why I've been paying OpenAI prices for so long.

Why I'm even bothering with Chinese models in 2026

Before we get into the meat of it, let me explain my bias upfront: I'm an open source contributor. I maintain a couple of small libraries on GitHub, I file PRs when I can, and I personally believe that model weights under permissive licenses (Apache 2.0, MIT, that whole family) are the only way AI development doesn't calcify into another decade of vendor lock-in.

引用 Apache/MIT — that's the spirit. When a lab publishes weights under one of these licenses, you can run the model on your own hardware, audit what it actually does, and most importantly, you're not at the mercy of a quarterly price hike. DeepSeek famously publishes weights. Qwen has been MIT-licensed for ages. GLM, same deal. Kimi is more closed but their API is at least cheap.

Compare that to a typical walled garden vendor: your "API credits" can devalue overnight, your prompts train their next model, and good luck running it on your own GPU. I'll take permissive weights over a slick landing page any day of the week.

So when I say I've been testing these four, I'm coming at it from the angle of: which one would I actually deploy in production if I had to pick tomorrow?

The four contenders at a glance

Rather than burying the lede under paragraphs of prose, here's my shorthand cheat sheet:

DeepSeek — The price-to-performance champion. If you want MIT-licensed weights and a $0.25/M output rate that genuinely competes with GPT-4o, start here.
Qwen — Alibaba's Swiss Army knife. Eight different sizes from $0.01/M to $3.20/M. They've got a model for literally any job.
Kimi — The reasoning specialist out of Moonshot AI. Closed weights, but their K2.5 model crushes math benchmarks.
GLM — Zhipu's offering. Apache 2.0 friendly, brutally cheap at the low end, and the king of Chinese-language tasks.

All four expose OpenAI-compatible APIs, which is beautiful because I didn't have to rewrite a single line of code to swap between them. That's the killer feature right there — when you're not stuck inside a walled garden, you can A/B test freely.

Diving into DeepSeek

I'll start with DeepSeek because it's the one I keep coming back to. Their V4 Flash model at $0.25 per million output tokens is the single best deal in production AI right now, full stop. I ran it through my usual battery of tests — code generation, summarization, structured output — and it kept up with models that cost 8-10x more.

The full lineup:

V4 Flash — $0.25/M — my daily driver. Open-weight, Apache spirit, ridiculously fast at ~60 tokens/sec.
V3.2 — $0.38/M — newer architecture, slightly more expensive.
V4 Pro — $0.78/M — when I need a bit more headroom for production use.
R1 (Reasoner) — $2.50/M — DeepSeek's dedicated reasoning model. Heavy hitter for math and logic.
Coder — $0.25/M — code-specific variant.

What I love: the open-weight heritage, the blazing speed on Flash, and the fact that English output reads like a native English speaker wrote it. On HumanEval and MBPP, this thing is competitive with anything out there.

What bugs me: vision is limited. There's no native image understanding in V4 Flash, which means for multimodal tasks I have to route to something else. Chinese-language performance is also slightly behind GLM and Kimi on certain benchmarks — but honestly, only slightly.

Here's my actual production setup for 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 entanglement to a 10-year-old in 100 words"}]
)
print(response.choices[0].message.content)

Notice how that's literally just an OpenAI client? No proprietary SDK, no walled-garden headers, no "please accept our new terms before continuing" popups. This is what API design should feel like.

Qwen and the model variety problem

Alibaba's Qwen team has, frankly, gone a bit overboard with model variants. In a good way. Where DeepSeek gives you five choices, Qwen gives you... a lot more.

Their catalog spans from $0.01/M all the way up to $3.20/M, covering basically every budget tier you could ask for:

Qwen3-8B — $0.01/M — my go-to for ultra-light tasks. Throwaway summaries, regex tasks, things where I don't need a brain, just a finger.
Qwen3-32B — $0.28/M — the workhorse. At $0.28 it competes directly with DeepSeek's V4 Flash and gives you a slightly different style of output.
Qwen3-Coder-30B — $0.35/M — code generation specialist.
Qwen3-VL-32B — $0.52/M — vision-language. This is where DeepSeek falls short, so I route image tasks here.
Qwen3-Omni-30B — $0.52/M — multimodal (audio + video + image in one model).
Qwen3.5-397B — $2.34/M — the big beast for enterprise reasoning.

Strengths? Wide model range, strong vision models, omni-modal capabilities, Alibaba's enterprise infrastructure, frequent releases. The lab ships new versions constantly.

Weaknesses? Confusing naming conventions, mid-tier English that doesn't quite match DeepSeek, and some of the larger models feel overpriced. Qwen3.6-35B at $1/M made me wince a bit.

Here's a typical Qwen3-32B call:

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 client object, different model name, completely different price point. That's the magic of an OpenAI-compatible interface — switching vendors shouldn't require refactoring your codebase.

Kimi, the closed-but-clever one

I'll be upfront: Kimi is the only one of the four I'm slightly skeptical of from an open source standpoint. Moonshot AI doesn't publish weights, it's more of a traditional proprietary play. But I keep it in my rotation because, honestly, K2.5 is a reasoning beast.

Their pricing sits between $3.00-$3.50/M across the board. There's no "budget tier" model here — Kimi is premium positioning all the way. Their flagship K2.5 at $3.00/M hits reasoning benchmarks harder than anything else I've tested from China. If you need complex math, multi-step logic, or chain-of-thought stuff, Kimi is your friend.

Where it falls short: speed. It's noticeably slower than DeepSeek's V4 Flash. For real-time applications where latency matters, I'd route to something else. And on Chinese-language tasks, surprisingly, GLM still edges it out on a few benchmarks I ran (CEVAL, C-Eval subsets).

But here's my honest take: if I were building a research assistant or an agent that needs to actually think through problems, K2.5 would be my pick. The $3/M feels steep until you realize it replaces multiple reasoning calls in a single pass — you save money by spending more per call.

GLM, the underrated dark horse

And then there's GLM, which I think is the most underrated model family in this bunch. Zhipu AI publishes under Apache 2.0, their ecosystem is genuinely open, and the pricing is aggressive at both ends of the spectrum.

The lineup:

GLM-4-9B — $0.01/M — same floor as Qwen3-8B. For trivial tasks, perfect.
GLM-5 — $1.92/M — the flagship.

GLM wins decisively on Chinese-language benchmarks. If your workload is bilingual or heavily Chinese-centric — say, you're processing customer service tickets in Mandarin — GLM outperforms everything else I tested, including Kimi. The model just has a fundamentally better grasp of classical Chinese references, idioms, and the kind of subtle linguistic distinctions that make or break a translation.

It's not the fastest kid on the block (around mid-pack speed), and on code generation it's slightly behind DeepSeek's coder specialization. But for any Chinese-heavy workload, it's a no-brainer.

Head-to-head: the metrics that actually matter

Let me run through my real benchmark results so you can see what this looks like in practice.

Code generation (HumanEval pass@1 equivalent):

DeepSeek Coder — top tier, basically tied with the best Western models.
Qwen3-Coder-30B — solid, but a half-step behind DeepSeek.
Kimi K2.5 — surprisingly good for a reasoning-focused model.
GLM — competent, not the primary strength here.

Reasoning (math olympiad-style problems):

Kimi K2.5 — clear winner. Chain-of-thought reasoning is where it shines.
DeepSeek R1 — close second.
Qwen3.5-397B — strong on multi-hop tasks.
GLM-5 — solid but not class-leading.

Chinese language (CEVAL, CGCE):

GLM-5 — king of the hill.
Kimi K2.5 — excellent.
Qwen3-32B — strong.
DeepSeek V4 Flash — good, slightly behind.

English writing quality:

DeepSeek V4 Flash — surprisingly native-sounding, ties with Western top tier.
Qwen3-32B — good, slightly formal.
Kimi — competent but occasionally stiff.
GLM — solid for translation but tone can feel translated.

Speed (tokens/sec at batch=1):

DeepSeek V4 Flash — ~60 t/s, the fastest.
Qwen3-8B — fast for its tier.
GLM — middle of the pack.
Kimi — slowest, but you're paying for depth.

My real production stack

So what do I actually ship? After three months of testing, here's my routing logic:

Default to DeepSeek V4 Flash for 80% of requests. The $0.25/M price and the speed means I can serve more users with less infrastructure.
Route vision/image tasks to Qwen3-VL-32B at $0.52/M. DeepSeek's weak spot, Qwen's strength.
For reasoning-heavy agent loops, escalate to Kimi K2.5 at $3.00/M — but only when I actually need the reasoning depth.
Any Chinese-language work goes to GLM-5 at $1.92/M.

This routing saves me roughly 60% on inference costs compared to running everything through a single Western vendor. That's not a marketing claim, that's my actual monthly bill.

The open source angle, one more time

I want to circle back to why this