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Posted on • Originally published at apidog.com

What Is GLM-5.2?

GLM-5.2 is the newest flagship model from Z.ai (Zhipu AI). It is an open-weights, coding-first LLM positioned against closed frontier models. This guide explains what GLM-5.2 is, how it works, how to access it, and what to verify before you build with it.

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TL;DR

  • What it is: GLM-5.2 is an open-weights large language model from Z.ai, built for coding, reasoning, and agentic tool use.
  • Size: Roughly 753B parameters in a Mixture-of-Experts (MoE) architecture, served in BF16, with a sparse-attention technique called IndexShare.
  • Context: 1M tokens, or 1,048,576 tokens. Max output is listed as up to 128K in z.ai docs, but verify this on your provider.
  • License: MIT. You can download, self-host, fine-tune, and use it commercially.
  • Headline benchmark: Terminal-Bench 2.1 increased from GLM-5.1’s 62.0 to 81.0, according to Z.ai. SWE-bench Pro is listed at 62.1.
  • Access: Z.ai API, Claude Code through the GLM Coding Plan, OpenRouter, and Ollama.
  • Caveat: GLM-5.2 is text in, text out. There is no confirmed vision variant.

Who makes GLM-5.2?

GLM-5.2 comes from Z.ai, also known as Zhipu AI. It is the latest model in the GLM, or “General Language Model,” family after GLM-5.1.

The practical difference from many frontier models is distribution: GLM-5.2 ships with open weights instead of being available only through a closed API.

GLM-5.2

If you read the earlier GLM-5.1 overview, treat GLM-5.2 as the same lineage with stronger emphasis on coding, reasoning, and agentic workflows.

GLM-5.2 is still a general-purpose model. It handles reasoning, math, and multilingual text, with English and Chinese as first-class use cases. But Z.ai tuned it especially for software engineering and tool-driven multi-step work.

Model identifiers by platform

Open models often use different names depending on where you run them. Use this table when configuring clients or deployment scripts.

Platform Identifier
Hugging Face zai-org/GLM-5.2
Z.ai API glm-5.2
Ollama glm-5.2
OpenRouter z-ai/glm-5.2

The weights are MIT-licensed and not region-gated. You can inspect the model files on the GLM-5.2 Hugging Face page.

Architecture: 753B MoE with IndexShare

GLM-5.2 is a Mixture-of-Experts model with roughly 753B total parameters, served in BF16.

MoE means the model contains many expert subnetworks, but only part of the model activates for a given token. This gives the model high total capacity without paying the full compute cost of activating every parameter on every forward pass.

GLM-5.2 architecture

The newer optimization is sparse attention. GLM-5.2 introduces a method Z.ai calls IndexShare.

In standard attention, cost grows quickly as context length increases because tokens attend to other tokens across the sequence. IndexShare reuses a single “indexer” across every group of four sparse-attention layers instead of recomputing one per layer.

The implementation takeaway:

  • GLM-5.2 is designed for very large context windows.
  • It is intended to make large-codebase and long-document prompts more practical.
  • You still need to benchmark latency and cost on your chosen host.

Context window: 1M tokens

GLM-5.2 supports a 1M-token context window:

1,048,576 tokens
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That is enough to place a large spec, a repository snapshot, or multiple related documents into one prompt and ask the model to reason across them.

Be careful with output length. The z.ai docs list output up to 128K tokens, but not every provider exposes or documents the same limit. If your workflow depends on long generations, check the live limit for the exact endpoint you use.

For a release-to-release breakdown, see the GLM-5.2 vs GLM-5.1 comparison.

Configure reasoning effort

GLM-5.2 supports controllable reasoning behavior.

You can use:

  • High: stronger reasoning with lower compute cost.
  • Max: deepest reasoning. Z.ai recommends this for coding tasks.
  • Disabled thinking: useful for simple formatting, extraction, rewriting, or short factual calls.

For API usage, this maps to parameters such as:

{
  "thinking": {
    "type": "enabled"
  },
  "reasoning_effort": "max"
}
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For simpler calls:

{
  "thinking": {
    "type": "disabled"
  }
}
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Use a simple rule:

  • Turn reasoning on for debugging, refactoring, architecture, and repo-level tasks.
  • Turn reasoning off for deterministic transforms and low-complexity requests.

For request details, see the GLM-5.2 API guide.

What the MIT license enables

GLM-5.2’s MIT license is important if you need control over deployment or data handling.

It allows:

  • Self-hosting: Run the model on your own infrastructure or rented GPUs.
  • Fine-tuning: Adapt it to your domain, coding style, or internal task format.
  • Commercial usage: Build paid products or internal tools on top of it.
  • No regional lockout: The weights are not gated behind a region check.

For teams with data-residency, compliance, or source-code privacy constraints, self-hosting can matter more than a small benchmark difference.

If you want to explore local deployment patterns, see:

The same general approach carries over to GLM-5.2, with the hardware requirements of a much larger model.

Coding and agentic benchmark results

Z.ai positions GLM-5.2 as a coding and agentic tool-use model. The results below are Z.ai’s published measurements, so treat them as vendor-reported numbers rather than independent third-party validation.

Benchmark GLM-5.2 Notable comparison
Terminal-Bench 2.1 81.0 GLM-5.1 scored 62.0
SWE-bench Pro 62.1 GPT-5.5 58.6, GLM-5.1 58.4
MCP-Atlas 77.0 GPT-5.5 75.3, Claude Opus 4.8 77.8
Humanity’s Last Exam, with tools 54.7 GPT-5.5 52.2
AIME 2026 99.2 n/a
GPQA-Diamond 91.2 n/a

The key result is Terminal-Bench 2.1, where GLM-5.2 moves from GLM-5.1’s 62.0 to 81.0. That benchmark focuses on whether a model can operate in a terminal and complete tasks, which is directly relevant for agentic coding.

SWE-bench Pro at 62.1 is also notable because it points to repository-level issue solving rather than isolated code snippets.

Z.ai also reports GLM-5.2 as the highest open-source model on FrontierSWE, PostTrainBench, and SWE-Marathon. VentureBeat framed the cost angle by writing that GLM-5.2 “beats GPT-5.5 on long-horizon coding at ~1/6 the cost” in its GLM-5.2 coverage. That is VentureBeat’s framing, not an Apidog measurement.

For more detail, read:

How to access GLM-5.2

You have four practical access paths.

Access path Best for Quick note
Z.ai API Direct hosted calls OpenAI-compatible endpoint at https://api.z.ai/api/paas/v4/
Claude Code, GLM Coding Plan Agentic coding in your terminal Anthropic-compatible base URL, select the [1m] variant
OpenRouter One key for multiple models Model ID: z-ai/glm-5.2
Ollama Local or offline usage Pull glm-5.2 from the library

Option 1: Call the Z.ai API

The Z.ai API is OpenAI-compatible. You call:

https://api.z.ai/api/paas/v4/chat/completions
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Example request:

curl https://api.z.ai/api/paas/v4/chat/completions \
  -H "Authorization: Bearer $ZAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "glm-5.2",
    "messages": [
      {
        "role": "user",
        "content": "Refactor this function for readability."
      }
    ],
    "thinking": {
      "type": "enabled"
    },
    "reasoning_effort": "max",
    "stream": true
  }'
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Use this path when you want a hosted API with familiar chat-completions semantics. Function and tool calling are supported.

Option 2: Use GLM-5.2 with Claude Code

Z.ai exposes an Anthropic-compatible coding endpoint, so you can route Claude Code through GLM-5.2.

Set the environment variables:

export ANTHROPIC_BASE_URL="https://api.z.ai/api/coding/paas/v4"
export ANTHROPIC_API_KEY="your-glm-coding-plan-key"
export ANTHROPIC_DEFAULT_SONNET_MODEL="glm-5.2[1m]"
export ANTHROPIC_DEFAULT_OPUS_MODEL="glm-5.2[1m]"
export CLAUDE_CODE_AUTO_COMPACT_WINDOW=1000000
export API_TIMEOUT_MS=3000000
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The [1m] suffix selects the 1M-context variant.

The timeout value matters. Large-context coding tasks can exceed default client timeouts, so increase API_TIMEOUT_MS to avoid killing long requests prematurely.

Some sources show open.z.ai/api/paas/v4 as the base URL, so verify the current endpoint in the live docs before deployment.

For IDE and agent setup details, see:

Option 3: Use OpenRouter

If your app already routes models through OpenRouter, use:

z-ai/glm-5.2
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Live listing:

https://openrouter.ai/z-ai/glm-5.2

There is no free OpenRouter lane for this model, so do not design around a free hosted tier.

Option 4: Run with Ollama

For local usage, pull the model from the Ollama GLM-5.2 library page.

Use this route when you need:

  • Offline workflows
  • Local experimentation
  • Stronger data-control boundaries

The tradeoff is hardware. A 753B MoE model requires serious GPU resources to serve comfortably.

For free-access options, see how to use GLM-5.2 for free.

Pricing notes

On hosted API access, OpenRouter confirms pricing at:

  • $1.40 per 1M input tokens
  • $4.40 per 1M output tokens

VentureBeat cites cached input at around $0.26 per 1M tokens.

The GLM Coding Plan has tiered subscriptions, including Lite, Pro, Max, and Team. Exact monthly figures vary across secondary sources, so confirm current pricing at z.ai before committing.

For a maintained summary, see the GLM-5.2 pricing breakdown.

Where Apidog fits in a GLM-5.2 workflow

If you are building against the GLM-5.2 API or connecting it to tools, you still need to design, test, and document your own APIs.

Apidog helps with that workflow:

  • Mock LLM-backed endpoints before the real integration is complete.
  • Debug request and response payloads.
  • Validate streaming responses and tool-call schemas.
  • Keep API documentation in sync with implementation changes.
  • Design, test, mock, debug, and document APIs in one platform.

When your integration is ready, you can download Apidog and point it at your GLM-5.2 API flow.

How GLM-5.2 compares with other models

GLM-5.2 is the coding-and-agentic peak of the current GLM line. If you are comparing it with earlier GLM models or closed frontier models, start here:

FAQ

What is GLM-5.2?

GLM-5.2 is Z.ai’s open-weights flagship LLM. It is a roughly 753B-parameter MoE model tuned for coding, reasoning, and agentic tool use, with a 1M-token context window and an MIT license.

Is GLM-5.2 free?

The weights are free to download and self-host under the MIT license.

Hosted access through the Z.ai API, GLM Coding Plan, or OpenRouter is paid. In this case, “free” means open weights, not a free hosted endpoint.

Can GLM-5.2 process images?

No. GLM-5.2 is text in, text out according to the API docs. There is no confirmed vision variant. Use a separate vision model if your workflow needs image input.

How is GLM-5.2 different from GLM-5.1?

The biggest visible improvement is agentic coding. Terminal-Bench 2.1 increased from 62.0 to 81.0 according to Z.ai’s published results. GLM-5.2 also adds IndexShare sparse attention and improves SWE-bench Pro performance.

For details, read the GLM-5.2 vs GLM-5.1 comparison.

What context and output lengths does GLM-5.2 support?

The context window is 1M tokens.

Output is documented at up to 128K tokens by z.ai, but provider limits may differ. Always verify the current limit on your target endpoint.

Bottom line

GLM-5.2 is a serious open-weights coding model: a 753B MoE architecture, 1M-token context, controllable reasoning effort, MIT licensing, and vendor-reported benchmarks that put it in the same conversation as GPT-5.5 and Claude Opus 4.8 for coding tasks.

Before production use, validate three things on your target provider:

  1. Actual context and output limits.
  2. Latency and cost for your prompt sizes.
  3. Tool-calling and streaming behavior in your app.

If you are ready to build against it, start with the GLM-5.2 API guide.

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