GLM-5.1

GLM-5.1

GLM-5.1 achieves a major leap in coding capabilities, particularly notable in handling long-horizon tasks. Unlike previous models built around minute-level interactions, GLM-5.1 can independently and continuously handle a single task for over 8 hours, autonomously planning, executing, and improving itself throughout the process, ultimately delivering complete engineering-level results.

Context window131K

Provider智谱AI

Released2026/04/14

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 context	Input	Output	Cache read
≤ 32K	0.88/M	3.53/M	0.19/M
> 32K	1.18/M	4.12/M	0.29/M

Overview

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Output

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GLM-5.1 API: Open-Source Coding Agent That Can Work Continuously for 8 Hours

GLM-5.1 is a flagship engineering agent model released and open-sourced by Zhipu AI in April 2026, built on a 744B MoE architecture and licensed under MIT. It achieves a significant leap in coding capability, with its most outstanding feature being the handling of long-horizon tasks: unlike previous models built around minute-level interactions, GLM-5.1 can independently work on a single task for over 8 hours continuously, autonomously planning, executing, and improving itself throughout the process, ultimately delivering complete engineering-grade results.

On the authoritative SWE-Bench Pro coding benchmark, GLM-5.1 scores 58.4, surpassing GPT-5.4, Claude Opus 4.6, and Gemini 3.1 Pro, making it the first open-source model to beat all closed-source flagship models on this leaderboard.

SeaWhale AI offers GLM-5.1 through an OpenAI-compatible interface, supporting tool calling, streaming output, and long-horizon agent workflows.

Get API Key · Model ID: GLM-5.1

Why Choose GLM-5.1

The first open-source model to beat all closed-source flagships on SWE-Bench Pro — scores 58.4, surpassing GPT-5.4, Opus 4.6, and Gemini 3.1 Pro
8 hours of continuous autonomous work — a single task can independently run through the entire engineering workflow
744B MoE architecture — a large-scale sparse expert model, balancing capability and inference efficiency
MIT open-source license — free for commercial use and private deployment
131K context and output — equal input and output lengths, suitable for long-form code generation

Core Capabilities

01 8-Hour Continuous Autonomous Execution

This is GLM-5.1's most core differentiator. Previous models were designed around minute-level interactions and required constant human intervention; GLM-5.1 can take on a complete task and then work continuously for more than 8 hours, autonomously planning, executing, self-checking, and improving.

Complete features from design to delivery
Self-checking and correction throughout the process
Long-chain execution without human intervention

02 First Open-Source on SWE-Bench Pro

The 58.4 score comes from real software engineering tasks—understanding repositories, locating issues, writing fixes, and passing verification. This is the first open-source model to surpass all closed-source flagship models on this benchmark.

Issue localization in real repositories
Cross-file fixes and verification
Engineering-grade delivery quality

03 744B MoE Architecture

The large-scale sparse mixture-of-experts architecture controls inference cost while maintaining strong capability, representing a typical path for open-source large models to balance capability and cost-effectiveness.

Capability ceiling from large parameter count
Sparse activation controls inference cost
Supports private deployment

04 Agent Planning and Tool Use

GLM-5.1 has a built-in agent architecture designed for autonomous planning, tool calling, web browsing, and multi-step workflow management, and can be directly used as the core model for coding agents.

Autonomous task decomposition and planning
Multi-tool orchestration
Multi-step workflow management

Best Use Cases

Scenario	Description
Long-horizon coding agents	Start with one instruction, work continuously for hours, and deliver
Repository-level bug fixing	Real engineering tasks of the SWE-Bench Pro type
Autonomous development workflows	End-to-end execution from requirement understanding to code delivery
Domestic substitution	Enterprise coding models that need to be independently controllable
Private deployment	Local deployment under the MIT license
Cost-sensitive agents	Cost advantages from open-source models

Differences Between GLM-5.1, GLM-5.2, and GLM-5

Capability	GLM-5.1	GLM-5	GLM-5.2
Model ID	`GLM-5.1`	`GLM-5`	`GLM-5.2`
Architecture	744B MoE	Flagship open-source base model	Large-scale reasoning model
Context window	131K tokens	131K tokens	1M tokens
SWE-Bench Pro	58.4 (first open-source)	—	Higher
Long-horizon capability	8+ hours continuous	Long-horizon agent	More stable
Open-source license	MIT	MIT	MIT

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

How to Use GLM-5.1 API

1. Create a SeaWhale AI API key Generate a key and add credits in the console.

2. Provide the full task description in one go GLM-5.1 excels at long-duration autonomous execution. Clearly state the complete requirements, constraints, and acceptance criteria in the first round and let it run on its own; this works far better than multiple rounds of piecemeal follow-up questions.

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-5.1",
    "messages": [
      {"role": "user", "content": "Implement a complete task scheduling service: data models, REST API, retry mechanisms, and unit tests. Deliver it once everything passes."}
    ],
    "stream": true
  }'

FAQ

When was GLM-5.1 released? It was released in April 2026 and open-sourced under the MIT license.

What does "working continuously for 8 hours" mean? It means the model can autonomously execute a single task for more than 8 hours, during which it plans on its own, calls tools, checks results, and improves itself without requiring repeated human intervention.

What level is a score of 58.4 on SWE-Bench Pro? It surpasses GPT-5.4, Claude Opus 4.6, and Gemini 3.1 Pro, making it the first open-source model to beat all closed-source flagship models on this benchmark.

How do I choose between GLM-5.1 and GLM-5.2? Choose GLM-5.2 if you need a 1M-token long context; GLM-5.1 remains a strong choice when 130K tokens are enough and cost is a priority.

What are the context and output limits? 131,072 tokens of context, with the same 131,072-token output limit; input and output are equal.

Can it be deployed privately? Yes. The MIT license permits free commercial use and private deployment, and the model weights are public.

Why Choose SeaWhale AI for the GLM-5.1 API

No self-built cluster required — the 744B model doesn't require you to prepare GPU resources yourself
OpenAI-compatible interface — integrate with existing code by changing two lines
Multiple versions coexist — switch and compare GLM-5 / 5.1 / 5.2 / 5-Turbo under the same account
RMB settlement — domestic models are billed in RMB

API

API integration

Model IDUse this value as the model in inference requests

GLM-5.1

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-5.1 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-5.1",
    "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-5.1",
    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-5.1',
  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 ?? '')
}