I Benchmarked DeepSeek, Qwen, Kimi, and GLM — Here's What Won

I Benchmarked DeepSeek, Qwen, Kimi, and GLM — Here's What Won

Last month I spent an embarrassing amount of time bouncing between four Chinese LLM APIs trying to figure out which one I should actually wire into production. Spoiler: there isn't a single winner, but the answer surprised me more than I expected. fwiw, I'd been a DeepSeek loyalist for over a year, so going into this benchmark I genuinely expected the results to confirm my bias. They didn't. Not entirely, anyway.

If you're a backend engineer staring at model selection the way I was — juggling pricing spreadsheets at 11pm while your staging environment screams — this one's for you. I tested all four families (DeepSeek, Qwen, Kimi, and GLM) through Global API's unified endpoint, hit them with the same prompts, and tracked latency, output quality, and cost per million tokens. Here's the field report.

Why These Four, and Why a Unified Endpoint Matters

Before we dive in, let me explain why I bothered. Under the hood, each of these providers has its own SDK quirks, rate limit policies, and billing dashboards that differ just enough to be annoying. Routing everything through https://global-apis.com/v1 means I write one client, handle one auth header, and swap model strings like config flags. It also means my benchmark methodology stays consistent — same network path, same serialization, same retry behavior. ime this is the only way to get apples-to-apples numbers.

The four model families in question come from very different corners of China's AI scene:

• DeepSeek — out of 幻方 (High-Flyer), the quant fund that pivoted hard into AI
• Qwen — Alibaba's 阿里 contribution, which has become the most sprawling lineup
• Kimi — Moonshot AI's 月之暗面, focused heavily on reasoning and long-context work
• GLM — Zhipu AI's 智谱 lineup, which has deep roots in the Chinese academic NLP community

Each has a distinct personality once you start prompting them. Let me show you what I mean.

The Cheat Sheet

Here's the TL;DR table I wish I'd had before starting. I'm going to reference this throughout, so bookmark it mentally:

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
Budget Pick	V4 Flash @ $0.25/M	Qwen3-8B @ $0.01/M	—	GLM-4-9B @ $0.01/M
Overall Pick	V4 Flash @ $0.25/M	Qwen3-32B @ $0.28/M	K2.5 @ $3.00/M	GLM-5 @ $1.92/M
Code Generation	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐
Chinese	⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐
English	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐
Reasoning	⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐
Speed	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐⭐
Multimodal	Limited	✅ (VL, Omni)	❌	✅ (GLM-4.6V)
Context Window	Up to 128K	Up to 128K	Up to 128K	Up to 128K
API Style	OpenAI-compatible	OpenAI-compatible	OpenAI-compatible	OpenAI-compatible

The pattern that jumps out immediately: Kimi is the premium tier across the board, while Qwen and GLM fight it out at the bottom of the price ladder. DeepSeek sits in the middle on price but punches above its weight on quality.

DeepSeek: The Per-Toke Bargain That Actually Delivers

I'll start with the one I had the most existing bias toward. DeepSeek's V4 Flash became my daily driver months ago, and at $0.25/M output tokens, it's still the model I recommend to anyone who asks "what should I use for general coding and content work?"

Model Lineup

Model	Output $/M	What I Used It For
V4 Flash	$0.25	Default for almost everything
V3.2	$0.38	When I wanted the newer architecture
V4 Pro	$0.78	Higher-stakes generation jobs
R1 (Reasoner)	$2.50	Multi-step math and logic chains
Coder	$0.25	Code-specific tasks

What I Liked

V4 Flash is the standout. At ~60 tokens/sec on Global API's routing, it's one of the fastest models I've measured, and the output quality genuinely rivals things I was paying 10x more for twelve months ago. The English capabilities are rock solid — no weird idiomatic failures, no stilted phrasing, just clean output.

For code, DeepSeek's lineage shows. It posts top-tier HumanEval and MBPP scores, and in my own ad-hoc tests (refactoring a gnarly Python module, generating SQL from natural language, writing Terraform configs), it consistently produced runnable output on the first pass more often than the alternatives.

What I Didn't

No native vision. If you need to feed it images, you're out of luck — and that's a real limitation for some pipelines. Chinese-language generation is good but not best-in-class; GLM and Kimi both edge it out on C-Eval-style benchmarks. Also, the model lineup is narrower than Qwen's, which means fewer knobs to tune for specific constraints.

Switching to V4 Flash in Python

Here's the swap I made when I consolidated everything onto Global API:

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 literally the whole integration. The OpenAI client library works because every one of these providers speaks the same wire protocol — which is honestly the only reason I'm able to A/B test them in the same afternoon.

Qwen: The Kitchen-Sink Model Family

Qwen is what happens when a company treats LLM development like cloud services. There are a lot of models. Like, embarrassingly many.

The Lineup I Actually Touched

Model	Output $/M	Primary Use Case
Qwen3-8B	$0.01	Tiny classification, embeddings-adjacent tasks
Qwen3-32B	$0.28	Default general-purpose model
Qwen3-Coder-30B	$0.35	Code-heavy workloads
Qwen3-VL-32B	$0.52	Vision-language tasks
Qwen3-Omni-30B	$0.52	Multimodal (audio/video/image)
Qwen3.5-397B	$2.34	Enterprise reasoning workloads

The price spread here is wild — $0.01/M at the low end up to $3.20/M for the biggest flagships. That's the widest range of any family I tested, and it's why I keep coming back to Qwen when I need flexibility.

What I Liked

The breadth. There is a Qwen model for basically every workload category I can think of: tiny models for classification, multimodal models for video understanding, vision-language models for document parsing, code-specialized variants, and a giant 397B flagship for when you need maximum reasoning. If you build systems that need different model sizes for different stages of a pipeline (small model for routing, big model for the heavy lift), Qwen is the only family that gives you all of those under one roof.

Alibaba's infrastructure backing also shows in uptime and throughput consistency. Under the hood, I never saw the kinds of rate-limit surprises that plague some smaller providers. And the multimodal models (Qwen3-VL-32B, Qwen3-Omni-30B) genuinely work — I tested the Omni model on a video QA task and got usable output without weird hallucinations.

What I Didn't

The naming convention is a maze. Qwen3-8B, Qwen3-32B, Qwen3-Coder-30B, Qwen3-VL-32B, Qwen3-Omni-30B, Qwen3.5-397B, Qwen3.6-35B... I had to keep a spreadsheet just to remember which one I was actually calling. ime this is a real cost — engineers waste time picking models when the difference between two adjacent SKUs is often negligible.

Mid-range English quality is good but not DeepSeek-level. I noticed more awkward phrasings and slightly weaker chain-of-thought reasoning on Qwen3-32B compared to V4 Flash at similar price points. And some of the upper-tier models feel overpriced — Qwen3.6-35B at $1/M output doesn't feel like a 3.5x improvement over the 8B at $0.01/M for most workloads.

Code Example

Here's how I'm calling Qwen3-32B for general-purpose tasks through the same Global API client:

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, same auth header, same base URL. Just swap the model string. This is the part that makes unified routing genuinely valuable rather than a marketing checkbox.

Kimi: The Reasoning Premium

Kimi is the odd one out in this comparison, and not just because Moonshot AI picked the most anime-coded company name in the industry (月之暗面 literally means "dark side of the moon," which, sure).

The Pricing Reality

Model	Output $/M	Notes
K2.5	$3.00	Current flagship
(top of range)	$3.50	Highest-tier model

There is no budget Kimi. The cheapest model in the family costs $3.00/M output tokens — twelve times what you'd pay for V4 Flash. That's a real premium, and you'd better believe I went in skeptical.

Where the Money Goes

Reasoning. Specifically: long-horizon reasoning tasks where the model has to plan, backtrack, verify its own work, and produce a coherent multi-step answer. On the benchmarks I ran — math olympiad-style problems, multi-hop question answering, code planning that requires architectural reasoning before writing any code — Kimi K2.5 was the clear winner.

If you read RFC 2119 the way I do (the one that defines MUST/SHOULD/MAY), you start appreciating models that reason about normative language well. Kimi was the best at parsing those distinctions, which is a niche but illustrative example of the kind of careful semantic work it excels at.

Context handling is also impressive. The 128K window is the same nominal size as the others, but Kimi actually uses it — it doesn't start forgetting details at token 80K the way some models do.

The Catch

Speed is mediocre. If you're building anything latency-sensitive (interactive chat UIs, real-time copilots), Kimi will feel sluggish compared to DeepSeek. And at $3.00+/M, it's not something you sprinkle around casually. My rule of thumb after testing: use Kimi only for the call where reasoning quality is the bottleneck, and route everything else to cheaper models.

I don't have a Kimi code snippet here because honestly, my production use of it is limited to one specific classification of task. It's a specialist, not an all-rounder.

GLM: The Chinese-Language Champion (and a Strong Budget Option)

GLM was the biggest surprise of my testing. I'd written it off years ago as a research-oriented model family, and the current generation genuinely surprised me on quality.

The Lineup

Model	Output $/M	What I Used It For
GLM-4-9B	$0.01	High-volume, low-stakes tasks
GLM-5	$1.92	Production flagship
GLM-4.6V	(vision)	Multimodal image tasks

The price floor ties with Qwen at $0.01/M, and the ceiling at $1.92/M is lower than Qwen's flagship. So if you're price-sensitive but want a full-size model, GLM-5 at $1.92/M is genuinely compelling.

What I Liked

Chinese language quality is outstanding — tied with Kimi at the top of my benchmarks. If your product touches Chinese-language content at any non-trivial volume, GLM deserves serious consideration. It handles classical references, idiomatic expressions, and tonal nuance better than DeepSeek in my testing.

GLM