GLM-5.2

GLM-5.2

GLM 5.2 is Z.ai's large-scale reasoning model. It supports text input and output with a 1M token context window, suitable for long-horizon agent workflows, project-level software engineering, and complex multi-step automation. Supports reasoning effort high and xhigh; xhigh maps to maximum reasoning. It is particularly adept at coding and tool use across long-running tasks, capable of maintaining engineering environments within a single task and consistently following standards throughout the entire development workflow (from requirements to multi-platform deployment).

Context window1.0M

Provider智谱AI

Released2026/06/29

Playground

Pricing

The same model is available through multiple service channels — choose based on latency, reliability and cost.

Prices in $ / 1M tokens

To pick a channel, add a provider field to the request body, for example "provider": { "channel": "direct" }. Valid values are direct / stable / economical; omit it to use the default channel.

Direct

Direct upstream connection — best when you need native behavior and the full context window.

Input	Output	Cache read
1.18/M	4.12/M	0.29/M

Overview

Input

Text

Output

Text

GLM-5.2 API: Open-Source SOTA Coding Model with Million-Token Context

GLM-5.2 is a large-scale reasoning model released and open-sourced by Zhipu (Z.ai) in June 2026, under the MIT license. It supports a 1 million token context window, focusing on long-horizon agentic workflows, project-level software engineering, and complex multi-step automation.

On mainstream coding benchmarks, GLM-5.2 maintains open-source SOTA status, sitting in a comparable range with Claude Opus 4.8—trailing Opus 4.8 by only a few percentage points on Terminal-Bench 2.1, and surpassing Gemini 3.1 Pro. In three long-context benchmarks, it ranks first among open-source models.

SeaWhale AI provides GLM-5.2 via an OpenAI-compatible interface, with support for tool calling, reasoning effort control, and streaming output.

Get an API Key · Model ID: GLM-5.2

Why Choose GLM-5.2

Open-source coding SOTA — On mainstream coding benchmarks, in the same range as Claude Opus 4.8
1M token context — Ranked first among open-source models in three long-context benchmarks
MIT open-source license — Free for commercial use and private deployment, no licensing worries
Supports high and xhigh reasoning effort — xhigh maps to maximum reasoning depth
Stable on long-horizon tasks — Maintains the engineering environment within a single task and carries through the entire development workflow

Core Capabilities

01 Project-Level Software Engineering

GLM-5.2 excels at coding and tool use across long-running tasks. Compared with the previous generation GLM-5.1, it has improved significantly in frontend, backend, and long-horizon task development scenarios, with greater stability in complex systems engineering and debugging.

Full feature development for frontend and backend
Complex systems engineering and architecture evolution
Long-chain debugging and issue localization

02 1M Token Long Context

A 1 million token context lets the model read at once long texts of millions of characters, complex PDFs of hundreds of pages, or an entire medium-to-large software code repository; it ranks first among open-source models in three long-context benchmarks.

Full code repository analysis
Hundreds of pages of PDFs and technical documents
Full preservation of long conversation history

03 Long-Horizon Agentic Workflows

GLM-5.2 can maintain the engineering environment in a single task and stay consistent throughout the entire development process, making it suitable for agentic scenarios that require long periods of autonomous execution.

Multi-step autonomous planning and execution
Engineering environment state maintenance
Automation of complex business processes

04 Open Source and Controllability

The MIT license means free commercial use, further development, and private deployment. For scenarios with sensitive data or a need for full autonomy and control, this is an option closed-source models cannot provide.

Free commercial use with no licensing risk
Supports private deployment
Model weights are auditable

Best Use Cases

Scenario	Description
Project-level coding	Full frontend/backend feature development and systems engineering
Long-context analysis	One-shot processing of entire code repositories and hundreds of pages of PDFs
Long-horizon agents	Automated workflows that require long-term state maintenance
Complex debugging	Cross-module long-chain issue localization
Domestic substitution	Enterprise deployment requiring autonomy and control
Cost optimization	Cost advantages from an open-source model

Differences Between GLM-5.2, GLM-5.1, and Claude Opus 4.8

Capability	GLM-5.2	GLM-5.1	Claude Opus 4.8
Model ID	`GLM-5.2`	`GLM-5.1`	`claude-opus-4-8`
Context window	1M tokens	130K tokens	1M tokens
Max output	128K tokens	131K tokens	128K tokens
Open-source license	MIT	MIT	Closed-source
Coding benchmarks	Open-source SOTA, close to Opus 4.8	Open-source leader	Frontier
Reasoning effort	high / xhigh	Supported	low → max

Actual billing is subject to the real-time price card at the top of the page.

How to Use the GLM-5.2 API

1. Create a SeaWhale AI API Key Generate a key in the console and top up your balance.

2. Use xhigh reasoning effort for long tasks GLM-5.2 supports two levels, high and xhigh; xhigh maps to the maximum reasoning depth and is recommended for complex engineering tasks.

3. Call the API

curl -X POST https://api.atalk-ai.com/v1/chat/completions \
  -H 'Authorization: Bearer YOUR_API_KEY' \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "GLM-5.2",
    "messages": [
      {"role": "user", "content": "Read all the code in this repository, design an extensible plugin mechanism, and provide an implementation."}
    ],
    "stream": true
  }'

FAQ

When was GLM-5.2 released? It was first made available to GLM Coding Plan users in mid-June 2026, then released via API and open-sourced under the MIT license.

How far is it from Claude Opus 4.8? On mainstream coding benchmarks it is in a comparable range, trailing by a few percentage points on Terminal-Bench 2.1 and surpassing Gemini 3.1 Pro. It ranks first among open-source models in long-context benchmarks.

What improvements does it bring over GLM-5.1? The context window expanded from 130K to 1 million tokens, with significantly improved success rates in frontend, backend, and long-horizon task development, and greater stability in complex systems engineering and debugging.

Are there restrictions in the open-source license? It is under the MIT license, allowing free commercial use, modification, and private deployment; it is one of the most permissive open-source licenses.

Can the full 1 million context be used? Yes. GLM-5.2 ranks first among open-source models in three long-context benchmarks, and performs stably in real-world long-document and large-repository scenarios.

Does it support tool calling? Yes. Function calling, streaming output, and reasoning effort control are all available.

Why Use GLM-5.2 API on SeaWhale AI

Call directly without deployment — Use open-source SOTA without building your own GPU cluster
OpenAI-compatible interface — Connect by changing two lines of existing code
Compare side-by-side with closed-source models — Compare quality and cost directly against Claude and GPT under the same account
Settlement in RMB — Domestic models are priced in RMB for clear accounting.

API

API integration

Model IDUse this value as the model in inference requests

GLM-5.2

API KeyBearer token used to authenticate inference requests

Manage API keys

Base URLOpenAI compatible · /chat/completions

https://api.atalk-ai.com/v2/

provider OptionalSelects a service channel; omit it and the system picks the default

"provider": { "channel": "direct" }

GLM-5.2 usage examples

SeaWhale AI is compatible with the OpenAI API protocol, so you can call it with the OpenAI SDK or plain HTTP requests. Streaming is enabled by default.

About the provider parameter (optional, a SeaWhale AI extension): most models are served over several channels that differ slightly in price and reliability. Add a provider field to the request body to pick one; omit it and the system selects the default channel — normal calls are unaffected.

provider is not part of the official OpenAI protocol — it is a SeaWhale AI extension that only takes effect on this platform. The OpenAI SDK allows custom fields like this to pass through; see the examples below.

Value	Channel	Best for
`direct`	Direct	The official upstream link, for native behavior and the full context window
`stable`	Preferred	Balanced availability and speed — a good default for production traffic
`economical`	Economy	Cost first, well suited to batch processing and price-sensitive workloads

Available channels and their prices are listed under "Pricing" above (channels vary by model). Additional notes:

Syntax: "provider": { "channel": "direct" }.
If the requested channel is not enabled for that model, the request automatically falls back to the default channel and returns normally — it does not error.
With the official SDKs: in Python pass it via extra_body; in Node.js put it directly on the request object and it passes through. In TypeScript projects, add a // @ts-expect-error line to skip the type check.

cURLPythonNode.js

curl https://api.atalk-ai.com/v2/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer <API_KEY>" \
  -d '{
    "model": "GLM-5.2",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "Hello!"}
    ],
    "provider": { "channel": "direct" },
    "stream": true
  }'
# provider is optional — remove this line to use the default channel

from openai import OpenAI

client = OpenAI(
    base_url="https://api.atalk-ai.com/v2",
    api_key="<API_KEY>",
)

stream = client.chat.completions.create(
    model="GLM-5.2",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello!"},
    ],
    stream=True,
    # Optional: pick a service channel; omit to use the default
    extra_body={"provider": {"channel": "direct"}},
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)

import OpenAI from 'openai'

const client = new OpenAI({
  baseURL: 'https://api.atalk-ai.com/v2',
  apiKey: '<API_KEY>',
})

const stream = await client.chat.completions.create({
  model: 'GLM-5.2',
  messages: [
    { role: 'system', content: 'You are a helpful assistant.' },
    { role: 'user', content: 'Hello!' },
  ],
  stream: true,
  // Optional: pick a service channel; omit to use the default
  // @ts-expect-error provider is a SeaWhale AI extension, not in the OpenAI SDK types
  provider: { channel: 'direct' },
})

for await (const chunk of stream) {
  process.stdout.write(chunk.choices[0]?.delta?.content ?? '')
}