GLM-5.2 is Z.ai's (Zhipu AI) open-weight flagship model, released on June 16, 2026. It has 753 billion parameters with a Mixture-of-Experts architecture (~40B activated per token), a 1-million-token context window, and an MIT license, so the weights can be downloaded freely from Hugging Face. It targets agents, coding, and long-horizon tasks.

GLM-5.2: The Open-Source SOTA With 1M Context That Beats GPT-5.5

On June 16, 2026, Z.ai (Zhipu AI) released GLM-5.2 — and it lands as one of the most capable open-weight models in the world. It packs 753 billion parameters (Mixture-of-Experts, ~40B activated per token), a 1-million-token context window, and — crucially for enterprises — an MIT license, with the weights free to download from Hugging Face.

⚡ TL;DR

GLM-5.2 is open-source SOTA for agents, coding, and enterprise workflows. On several long-horizon coding benchmarks it beats GPT-5.5 at about 1/6 the cost. Being open-weight (MIT), you can run it on your own GPU — in Brazil, with data sovereignty — with no per-token fees.

What's new in GLM-5.2

The jump from GLM-5.1 is big, especially on two fronts: genuinely usable long context and inference efficiency.

1M-token context (up from 200K in GLM-5.1) — and it's not just a marketing window: it's built to hold whole project-level engineering context.
IndexShare: reuses the same indexer across every four sparse-attention layers, cutting per-token FLOPs by ~2.9× at the maximum 1M-token context.
Upgraded Multi-Token Prediction for speculative decoding, boosting accepted token length by up to 20% at inference.
Two effort levels ("High" and "Max"), letting you trade speed for depth of reasoning.

Benchmarks reported after launch set the tone: 81.0 on Terminal-Bench 2.1, 62.1 on SWE-bench Pro, 77.0 on MCP-Atlas (tool use), and 54.7 on Humanity's Last Exam with tools — strong numbers for agentic and software-engineering work.

Why it matters

1. Open-source SOTA for agents, coding, and enterprise

You get frontier performance for autonomous agents, code generation, and enterprise workflows without being locked into a closed API. MIT weights mean you can fine-tune, audit, and deploy it anywhere.

2. 1M-token context

Larger codebases, longer documents, and deeper project state fit in a single call — less RAG plumbing, less context loss on long tasks. (For a long-context comparison, see also Llama 4 Scout.)

3. Confidential inference path

For sensitive prompts, code, documents, and enterprise data, there's a confidential inference path: GLM-5.2 is served in a TEE (Trusted Execution Environment) via Phala, running on secure hardware (Intel TDX + NVIDIA Confidential Computing on H100/H200), with data encrypted end-to-end and a signed attestation attached to the response — at ~99% of native speed.

4. OpenAI-compatible access through Phala and Redpill

You get OpenAI-compatible access through Phala and the broader Redpill ecosystem — just point your OpenAI client at a new endpoint and existing code keeps working, now with receipts/attestation attached to the response.

How to run GLM-5.2 on GPUBrazil

Because it's open-weight, you have two paths — and both keep your data inside Brazil:

Self-hosted on a dedicated GPU: serve GLM-5.2 with vLLM and expose an OpenAI-compatible endpoint. Full sovereignty: prompts and code never leave your instance.
Confidential inference (TEE): the H100/H200 GPUs we offer support NVIDIA Confidential Computing — the basis for the TEE confidential path, ideal for regulated data.

💡 Hardware reality

The full GLM-5.2 (753B MoE) needs multiple high-VRAM GPUs (H100/H200 class), especially to use the 1M context. Quantized builds cut the cost a lot and fit smaller setups. See how to choose the right GPU.

# OpenAI-compatible endpoint pointed at your GLM-5.2 (vLLM on GPUBrazil)
from openai import OpenAI

client = OpenAI(
    base_url="https://your-instance.gpubrazil.com/v1",
    api_key="your-local-key",
)

resp = client.chat.completions.create(
    model="zai-org/GLM-5.2",
    messages=[{"role": "user", "content": "Refactor this module while keeping the public API."}],
)
print(resp.choices[0].message.content)

Run GLM-5.2 on a Brazilian GPU

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Frequently asked questions

What is GLM-5.2?

It's Z.ai's (Zhipu AI) open-weight flagship, released June 16, 2026. It has 753 billion parameters (MoE, ~40B activated per token), a 1-million-token context window, and an MIT license — weights free on Hugging Face. It targets agents, coding, and long-horizon tasks.

Is GLM-5.2 better than GPT-5.5?

On several long-horizon coding benchmarks, GLM-5.2 beats GPT-5.5 at roughly one-sixth the cost per token (e.g. 81.0 on Terminal-Bench 2.1 and 62.1 on SWE-bench Pro, reported after launch). And being open-weight, you can self-host it with no per-token fees.

Can I run GLM-5.2 with full privacy?

Yes. Open weights let you host GLM-5.2 on a dedicated GPU in Brazil, keeping prompts, code, and sensitive data in-country (sovereignty and LGPD). There's also a confidential inference path running in a TEE (via Phala/Redpill, OpenAI-compatible) with attestation.

Conclusion

GLM-5.2 reinforces the trend we've seen all year: open-source models matching — and beating — proprietary ones, now with 1M context and a clear privacy path. For enterprises, MIT license + data sovereignty + confidential inference is a powerful combination: you run a frontier model in Brazil, under your control, without depending on an API that can change price or vanish.