Model comparison · updated July 2026

GLM-5.2 vs Qwen3.5: the practical comparison

GLM-5.2 (Zhipu AI (Z.ai)) and Qwen3.5 (Alibaba (Qwen team)) side by side — architecture, context, licensing, pricing, and a clear recommendation for when each one wins.

The verdict

GLM-5.2 for the coding agent, Qwen3.5 for everything at scale. GLM's 1M-token window and tool-native design make it the better brain for a repository-scale agent. Qwen3.5 is smaller-activated, dramatically faster to serve, Apache-licensed, and comes with a full model family. They compete less than they appear to — many stacks run GLM as the agent and Qwen as the workhorse.

GLM-5.2 vs Qwen3.5 at a glance

GLM-5.2Qwen3.5
VendorZhipu AI (Z.ai) (China)Alibaba (Qwen team) (China)
Open weightsYes — downloadableYes — downloadable
LicenseMIT — weights free to download, self-host, fine-tune, and use commerciallyApache 2.0 — permissive, patent-granting, free for commercial use
Parameters753B (~40B active)397B (17B active)
Context window1M tokens262K tokens (native)
Modalitiestexttext
PricingVaries by hosting provider (open weights)Varies by hosting provider (open weights)
Released2026-06-132026-02-16

Specs and pricing verified July 2026.

About GLM-5.2

GLM-5.2 is Zhipu AI's June 13, 2026 flagship: a ~753B-parameter mixture-of-experts model with ~40B active parameters, a 1-million-token context window, up to 128K output tokens, and MIT-licensed weights on Hugging Face. It is built deliberately as a coding and agent model — configurable thinking effort, native tool calling, MCP integration, structured JSON output — rather than a general-purpose chat model.

The engineering hook is IndexShare, Zhipu's sparse-attention refinement that reuses indexers across sparse-attention layers to cut per-token compute at long context. That is what makes the 1M-token window usable in practice for long-horizon coding agents, not just impressive on a spec sheet.

Full specs, benchmarks, and hardware guidance: the GLM-5.2 page.

About Qwen3.5

Qwen3.5 is Alibaba's February 2026 generation and the efficiency benchmark of the open-weight field: the 397B-A17B flagship activates just 17B parameters per token yet beats Alibaba's own trillion-parameter Qwen3-Max — at 8.6× the throughput at 32K context and 19× at 256K. All open-weight variants ship under Apache 2.0, the most enterprise-friendly license in common use.

The release is a full family, not one model: from the 397B flagship down to a 9B variant with a native 256K context window that runs on consumer hardware. That spread — one architecture, Apache 2.0 everywhere, sizes from phone to datacenter — is why Qwen has become the default base-model family for fine-tuners and the most-adopted open-weight line by download count.

Full specs, benchmarks, and hardware guidance: the Qwen3.5 page.

Choosing between them

Choose GLM-5.2 for:

  • Long-horizon coding agents (1M-token context built for repository-scale work)
  • Teams that want an MIT-licensed Claude-for-coding alternative
  • Tool-driven agentic workloads (native tool calling, MCP, JSON output)

Choose Qwen3.5 for:

  • Enterprise deployments that require Apache 2.0 licensing
  • Efficiency-critical serving (17B active parameters, extreme throughput)
  • Fine-tuning projects — the broadest size ladder in open weights

Frequently asked questions

Is GLM-5.2 better than Qwen3.5?

GLM-5.2 for the coding agent, Qwen3.5 for everything at scale. GLM's 1M-token window and tool-native design make it the better brain for a repository-scale agent. Qwen3.5 is smaller-activated, dramatically faster to serve, Apache-licensed, and comes with a full model family. They compete less than they appear to — many stacks run GLM as the agent and Qwen as the workhorse.

Which is cheaper: GLM-5.2 or Qwen3.5?

GLM-5.2: Varies by hosting provider (open weights). Qwen3.5: Varies by hosting provider (open weights). Both are open-weight, so self-hosting costs depend on your hardware and utilization.

Can I self-host GLM-5.2 and Qwen3.5?

GLM-5.2: yes — weights are published under MIT. Qwen3.5: yes — weights are published under Apache 2.0.