Is DeepSeek better than Qwen?

For reasoning, maths and cost-sensitive high-volume inference, DeepSeek is generally the stronger pick. Qwen wins when you need many model sizes, multimodal inputs or the widest multilingual coverage from a single family.

How does GLM-5.2 compare with GLM-4?

GLM-5.2 is a 744B-parameter mixture-of-experts flagship and a generational jump over the GLM-4 family on reasoning, coding and agentic tool use. GLM-4 checkpoints are still widely deployed because they are far cheaper to serve, but new projects should benchmark against the 5.x line.

Which Chinese open model has the most permissive licence?

Qwen, because most sizes ship under Apache 2.0. DeepSeek's weights are also permissive, and Zhipu's flagship is open, while several older GLM checkpoints carry custom terms that should be reviewed before commercial use.

Which model is best for agents and tool calling?

GLM-5.2 has the most mature tool-calling and agent scaffolding of the three, which is why the GLM line shows up most often in Chinese enterprise agent deployments.

Can these models be self-hosted?

Yes. All three publish open weights that run on standard inference stacks such as vLLM and SGLang, so you can self-host rather than depend on each lab's hosted API. The GLM-5.2 flagship needs the most GPU memory of the three.

Comparison

DeepSeek vs Qwen vs GLM-5.2

Three labs dominate China's open-weight model output: DeepSeek, Alibaba's Qwen team and Zhipu AI's GLM line, now led by the 744B-parameter GLM-5.2. They are not interchangeable. This page compares what each family is actually good at, how their licences differ, and which one fits which workload.

At a glance

Comparison of DeepSeek, Qwen and GLM-5.2 across eight dimensions
Dimension	DeepSeek	Qwen	GLM-5.2
Reasoning / maths	Best of the three	Competitive at flagship size	Close behind DeepSeek since 5.2
Coding	Strong	Strong (dedicated Coder line)	Strong on the 5.x line
Multimodal	Limited	Broadest (VL, audio, omni)	Good (vision line, CogVideo)
Model sizes	Few, large	Widest ladder, edge to frontier	Mid-range plus 744B MoE flagship
Licence clarity	Permissive	Apache 2.0 on most sizes	Permissive flagship, mixed tiers
Context window	128K	Up to 1M	128K–1M
API cost profile	Cheapest per token	Low, tiered by size	Mid, enterprise-oriented
Agent / tool use	Improving	Good	Best of the three

The three families in detail

DeepSeek
MIT-style permissive weights
DeepSeek (High-Flyer) · Hangzhou · 128K tokens context
Flagship models
DeepSeek-V3 / DeepSeek-R1 line
Where it wins
Frontier-grade chain-of-thought reasoning and maths at the lowest cost per token of the three. R1-derived distillations are still the default open reasoning baseline.
Where it falls short
Narrower multimodal line-up and fewer small sizes for on-device work; release cadence is spikier.
Best for
Reasoning, maths, research agents and cheap high-volume inference.
www.deepseek.com
Qwen
Apache 2.0 for most sizes
Alibaba (Tongyi Qianwen) · Hangzhou · Up to 1M tokens on long-context variants context
Flagship models
Qwen3 family, Qwen-VL, Qwen-Coder
Where it wins
The broadest ladder of sizes (sub-1B to flagship MoE), the strongest multilingual coverage, and the deepest community fine-tune ecosystem.
Where it falls short
Quality varies a lot between sizes, so benchmark claims for the flagship rarely transfer to the small checkpoints teams actually deploy.
Best for
Product teams that need one family across edge, server and vision workloads.
qwen.ai
GLM-5.2
Open weights, permissive on the flagship, custom terms on some tiers
Zhipu AI / Z.ai · Beijing · 128K–1M depending on variant context
Flagship models
GLM-5.2 (744B MoE), GLM-5.1, GLM-4 legacy line, CogVideo
Where it wins
The 744B-parameter MoE flagship closed most of the reasoning gap with DeepSeek while keeping the strongest tool-calling and agent scaffolding of the three, plus the most enterprise-ready deployment story in China.
Where it falls short
Serving the flagship is heavier than DeepSeek or mid-size Qwen, API pricing sits above DeepSeek, and licences are not uniformly permissive across checkpoints.
Best for
Agentic apps, tool use and China-market enterprise deployments.
z.ai

How to choose

Cost-sensitive reasoning at scale: DeepSeek. Cheapest tokens, strongest chain-of-thought.
One family across edge and server: Qwen. The size ladder and Apache 2.0 terms make it the safest default.
Agents and enterprise tool use: GLM-5.2. Best tool-calling behaviour and deployment support in China.
Multimodal inputs: Qwen-VL first, Zhipu's vision line second. DeepSeek is the weakest here.

Frequently asked questions

Is DeepSeek better than Qwen?: For reasoning, maths and cost-sensitive high-volume inference, DeepSeek is generally the stronger pick. Qwen wins when you need many model sizes, multimodal inputs or the widest multilingual coverage from a single family.
How does GLM-5.2 compare with GLM-4?: GLM-5.2 is a 744B-parameter mixture-of-experts flagship and a generational jump over the GLM-4 family on reasoning, coding and agentic tool use. GLM-4 checkpoints are still widely deployed because they are far cheaper to serve, but new projects should benchmark against the 5.x line.
Which Chinese open model has the most permissive licence?: Qwen, because most sizes ship under Apache 2.0. DeepSeek's weights are also permissive, and Zhipu's flagship is open, while several older GLM checkpoints carry custom terms that should be reviewed before commercial use.
Which model is best for agents and tool calling?: GLM-5.2 has the most mature tool-calling and agent scaffolding of the three, which is why the GLM line shows up most often in Chinese enterprise agent deployments.
Can these models be self-hosted?: Yes. All three publish open weights that run on standard inference stacks such as vLLM and SGLang, so you can self-host rather than depend on each lab's hosted API. The GLM-5.2 flagship needs the most GPU memory of the three.

Related: guide to China's top AI labs.

At a glance

The three families in detail

DeepSeek

Qwen

GLM-5.2

How to choose

Frequently asked questions

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