Stop Guessing: Real Data Comparing DeepSeek, Qwen, Kimi, GLM
Last month I burned roughly $400 in API credits trying to figure out which Chinese LLM family actually deserved a spot in my production pipeline. I had four serious contenders — DeepSeek, Qwen, Kimi, and GLM — and a pile of vendor blog posts that all claimed to be the best at everything. That's not how I make infrastructure decisions. So I ran the same standardized prompt suite against all of them through Global API's unified endpoint, tracked the token-level cost, latency percentiles, and output quality scores, and sat down with the numbers. This is what I found.
A quick caveat before we dive in: my sample size per model is around 800 generations across coding, reasoning, multilingual, and creative writing tasks. That's enough to spot statistically meaningful differences in cost-per-quality-point, though it won't catch every edge case. Take the absolute scores with a grain of salt; the relative comparisons are what matter.
The Setup: How I Actually Tested These
I designed a test harness that hits the OpenAI-compatible endpoint at https://global-apis.com/v1 so I could swap models without rewriting infrastructure. Every model got the same 200 prompts per category. I logged:
- Time to first token (TTFT) in milliseconds
- Tokens per second during generation
- Total cost per task calculated from the model's published output rate
- Quality score — a blend of automated metrics (BLEU for translation, pass@1 for code, judge-LLM rubric for reasoning) and a blind human eval on a 50-prompt subset
The correlation between automated scores and human eval was strong (r ≈ 0.82), so I'm reasonably confident the rankings hold up.
The Big Picture: At-a-Glance Comparison
Here's the master table I wish someone had handed me before I started:
| Dimension | DeepSeek | Qwen | Kimi | GLM |
|---|---|---|---|---|
| Developer | DeepSeek (幻方) | Alibaba (阿里) | Moonshot AI (月之暗面) | Zhipu AI (智谱) |
| Output price range | $0.25–$2.50/M | $0.01–$3.20/M | $3.00–$3.50/M | $0.01–$1.92/M |
| Best budget pick | V4 Flash @ $0.25/M | Qwen3-8B @ $0.01/M | N/A (premium-only) | 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 | 5/5 | 4/5 | 4/5 | 3/5 |
| Chinese language | 4/5 | 4/5 | 5/5 | 5/5 |
| English language | 5/5 | 4/5 | 4/5 | 4/5 |
| Reasoning | 4/5 | 4/5 | 5/5 | 4/5 |
| Speed | 5/5 | 4/5 | 3/5 | 4/5 |
| Vision/multimodal | Limited | ✅ VL, Omni | ❌ | ✅ GLM-4.6V |
| Context window | 128K | 128K | 128K | 128K |
| OpenAI-compatible API | ✅ | ✅ | ✅ | ✅ |
Three things jumped out at me from this table alone. First, Kimi is the only family that doesn't play the budget tier — its cheapest model is $3.00/M, roughly 12× the cheapest Qwen or GLM option. Second, Qwen's model spread is genuinely wild: the same vendor ships an 8B model for a tenth of a cent and a 397B beast for $2.34/M. Third, every single one of these exposes an OpenAI-compatible schema, which is why Global API can route them through a single endpoint without any adapter shim.
DeepSeek: The Outlier on Cost-per-Useful-Token
When I plotted cost against my composite quality score, DeepSeek V4 Flash sat in a quadrant by itself. At $0.25/M output tokens, it scored within statistical noise of models costing 8–12× as much. That's the single most interesting finding in this whole exercise.
| Model | Output $/M | Sweet spot |
|---|---|---|
| V4 Flash | $0.25 | Daily driver, coding, content |
| V3.2 | $0.38 | Newest architecture, slightly slower |
| V4 Pro | $0.78 | Production-grade quality |
| R1 (Reasoner) | $2.50 | Multi-step math and logic |
| Coder | $0.25 | Code-specific workloads |
What worked for me: Code generation was the standout. On HumanEval and MBPP, V4 Flash landed in the top decile across all models I tested (Chinese or otherwise). At ~60 tokens/second, it was also the fastest family on average — my p95 latency for a 500-token response was under 9 seconds. English output quality is genuinely competitive with Western frontier models; in blind A/B tests, three of five reviewers picked DeepSeek output over the more expensive alternative.
Where it stumbled: Vision support is essentially absent. If your product needs to look at images, DeepSeek is not your model today. Chinese-language output is good but not best-in-class — both Kimi and GLM edged it out on C-Eval style tasks, though the gap is small (≈3 percentage points). The model family is also narrower than Qwen's; you don't get the same luxury of picking an exact parameter count.
Here's how I wired it into my test harness:
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 in 100 words"}]
)
print(response.choices[0].message.content)
That base_url swap is the only line I changed between tests for every family in this comparison. Huge quality-of-life win.
Qwen: The Vendor That Ships Everything
Alibaba's Qwen team is the most prolific model factory in this space. I counted six production tiers shipping right now, from 8B up to 397B, with at least three architectural variants (text, VL, Omni).
| Model | Output $/M | Use case |
|---|---|---|
| Qwen3-8B | $0.01 | Classification, extraction, light tasks |
| Qwen3-32B | $0.28 | General-purpose sweet spot |
| Qwen3-Coder-30B | $0.35 | Code generation |
| Qwen3-VL-32B | $0.52 | Image understanding |
| Qwen3-Omni-30B | $0.52 | Audio + video + image |
| Qwen3.5-397B | $2.34 | Enterprise reasoning |
What I liked: The breadth is the whole pitch. If I need a $0.01/M model to label 10 million support tickets, Qwen3-8B is there. If I need a multimodal model that can watch a screen recording and narrate it, Qwen3-Omni-30B is there. Alibaba's infrastructure also means the API has been the most reliable in my uptime tests — 99.97% over a 30-day window, which beats the others by a hair. The naming is genuinely confusing though. Qwen3, Qwen3.5, Qwen3.6, plus the size suffix and the modality suffix — my team has a shared Notion doc just to remember which model name does what.
Where I'd push back: English-only quality at the mid-tier (Qwen3-32B) was good but not class-leading. DeepSeek still beat it on the same prompts in roughly 55% of blind comparisons. And some of the catalog entries feel like they're priced for a captive enterprise audience rather than the open market — a few models in the $1–$2/M band didn't show a quality gain proportional to the cost jump.
Here's a quick Qwen3-32B call for general coding:
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 same client object, same base_url — only the model string changes. This is honestly the part of the Global API setup that sold me; I'm not maintaining four SDKs or four auth flows.
Kimi: The Reasoning Specialist That Costs What It Costs
I'll be direct: Kimi is the most expensive family in this comparison, and it earns that premium exactly one way — by being the best reasoner of the four.
| Model | Output $/M | Notes |
|---|---|---|
| K2.5 | $3.00 | Flagship; top reasoning scores |
| K2 (older tier) | $3.50 | Still shipping for stability |
The raw numbers: on my chain-of-thought reasoning subset, Kimi K2.5 posted a 14-point lead over the next-best model. On a curated math olympiad-style set, the gap widened to 19 points. If your workload involves multi-step agentic reasoning, legal analysis, or scientific problem-solving, this is the family I'd pick.
The tradeoff: That quality shows up directly in your invoice. $3.00/M is roughly 12× the cost of V4 Flash for output. For a chatbot that just answers "what's the weather in Tokyo," Kimi is overkill. For a research assistant that needs to synthesize ten PDFs and not hallucinate, the extra cost is defensible. Speed is also the slowest of the four families — p95 latency on a 1,000-token response was 18 seconds in my tests, which is fine for async pipelines but noticeable in a chat UI. There's no vision support either.
GLM: The Quietly Excellent Chinese-Language Choice
Zhipu's GLM family was the one I underestimated going in. I kept treating it as the "budget alternative" and that was a mistake.
| Model | Output $/M | Use case |
|---|---|---|
| GLM-4-9B | $0.01 | Ultra-cheap tier |
| GLM-5 | $1.92 | Flagship |
| GLM-4.6V | (multimodal tier) | Image and video |
On Chinese-language benchmarks, GLM-5 tied or beat Kimi on three of the five sub-tasks I measured. The gap was especially large on classical Chinese text and on long-form Chinese creative writing — likely a function of training data composition rather than raw capability. If you're building for a Chinese-speaking user base and quality matters more than the last 5% of latency, GLM deserves a serious look.
Speed is mid-pack. Code generation is the weakest category — GLM-4-9B at $0.01/M is great for what it is, but for serious code work I'd pick DeepSeek's Coder or Qwen3-Coder-30B instead.
The Statistically Honest Summary
If I had to compress all of this into four sentences a stakeholder could read, it would be:
- DeepSeek V4 Flash is the best price-to-performance pick in the entire Chinese LLM landscape right now — and the margin is large enough that the other families only win on specialized axes.
- Qwen wins on breadth — six production tiers, multimodal coverage, and the most reliable uptime.
- Kimi wins on raw reasoning quality — and charges accordingly.
- GLM is the dark horse for Chinese-language production workloads.
For my own production pipeline, the answer ended up being a routing layer: DeepSeek V4 Flash handles 70% of traffic at $0.25/M, Qwen3-32B handles the vision and multimodal 20%, and Kimi K2.5 handles the long-tail reasoning workload at
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