GLM 5 Turbo

GLM-5-Turbo

GLM-5 Turbo is Z.ai's new model, specifically designed for fast inference and powerful performance in agent-driven environments such as OpenClaw scenarios. It is deeply optimized for real-world agent workflows involving long execution chains, improving complex instruction decomposition, tool use, planning and persistent execution, as well as overall stability for extended tasks.

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	0.74/M	3.24/M	0.18/M
> 32K	1.03/M	3.82/M	0.26/M

Overview

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GLM-5 Turbo API: A Fast Inference Tier for Agent Environments

GLM-5 Turbo is a fast inference model launched by Zhipu, designed for high-speed response and stable performance in agent-driven environments such as OpenClaw. It has been deeply optimized for real-world agent workflows involving long execution chains, with targeted improvements in complex instruction decomposition, tool use, planning and persistent execution, as well as overall stability in long-running tasks.

If your scenario is "the agent has to run many rounds of tool calls, and each round cannot be too slow," GLM-5 Turbo is usually a better fit than heavier inference tiers — it prioritizes speed while retaining the planning capability needed for long-chain execution.

SeaWhale AI provides GLM-5 Turbo through an OpenAI-compatible interface, with support for tool calling and streaming output.

Get API Key · Model ID: GLM-5-Turbo

Why Choose GLM-5 Turbo

Optimized for agent environments — Fast inference for agent-driven scenarios such as OpenClaw
Long execution chain stability — Less likely to go off track during multi-round tool calls
Complex instruction decomposition — Breaks ambiguous requirements into executable steps
131K context and output — Symmetric input/output, ample context for long tasks
Speed-first pricing tier — Cost control for high-frequency call scenarios

Core Capabilities

01 Agent-Driven Environments

GLM-5 Turbo is designed to run fast and stable in agent environments. In workflows requiring dozens or hundreds of tool calls, the cumulative effect of per-round latency is significant, and the Turbo tier is optimized specifically for this.

High-frequency tool call loops
Low per-round latency compounding into overall speedup
Execution core for agent frameworks

02 Complex Instruction Decomposition

Breaking a vague goal into a series of executable, concrete steps is key to whether an agent can complete the task. GLM-5 Turbo has been specially optimized for this stage.

Conversion from ambiguous requirements to execution plans
Identification of dependencies between steps
Verifiable intermediate goal setting

03 Planning and Persistent Execution

Maintaining memory of the original goal in long-chain tasks and not drifting off course due to intermediate detours is the core of "persistent execution" capability.

Goal consistency in long tasks
Path re-planning after intermediate failures
Continuous tracking of execution state

04 Overall Stability in Long Tasks

Compared with general-purpose models, GLM-5 Turbo is less prone to stability issues such as format corruption, tool parameter errors, and repeated loops in long-running tasks.

Accuracy of tool parameter construction
Consistency of output format
Avoidance of ineffective loops

Best Use Cases

Scenario	Description
Agent framework core	High-frequency execution model in environments such as OpenClaw
Multi-tool orchestration	Workflows requiring dozens or more tool calls
Business process automation	Long-chain enterprise process execution
Coding agents	Code modification loops requiring rapid iteration
High-concurrency agents	Scenarios running many agent instances simultaneously
Cost-sensitive automation	Production deployment balancing speed and cost

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

Capability	GLM-5 Turbo	GLM-5	GLM-5.1
Model ID	`GLM-5-Turbo`	`GLM-5`	`GLM-5.1`
Positioning	Fast inference for agent environments	Flagship open-source base model	Long-cycle engineering agents
Context window	131K tokens	131K tokens	131K tokens
Max output	131K tokens	131K tokens	131K tokens
Focus	Speed and execution stability	System design and deep reasoning	8-hour continuous execution
Best for	High-frequency tool call loops	Complex architecture problems	End-to-end autonomous development

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

How to Use the GLM-5 Turbo API

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

2. Use it for high-frequency loops The value of GLM-5 Turbo lies in the cumulative effect of multi-round calls. For single deep reasoning tasks, use GLM-5 or GLM-5.2.

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-Turbo",
    "messages": [
      {"role": "user", "content": "Break this deployment process into executable steps and call tools step by step to complete it."}
    ],
    "tools": [{"type": "function", "function": {"name": "run_shell", "parameters": {}}}],
    "stream": true
  }'

FAQ

What's the difference between Turbo and the standard version? Turbo prioritizes speed and long-chain execution stability, while the standard GLM-5 prioritizes deep reasoning and system design capability. Which one to choose depends on whether your bottleneck is latency or reasoning depth.

What exactly does "agent-driven environment" mean? It refers to runtime environments such as OpenClaw where agents take the lead and require heavy tool calling and autonomous decision-making. GLM-5 Turbo has been deeply optimized for such scenarios.

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

Is it suitable for multi-agent architectures? Yes. The speed advantage is further amplified when multiple agent instances run in parallel.

Will it drift off course in long tasks? GLM-5 Turbo has been specially optimized for planning and persistent execution, making it more stable than general-purpose models at maintaining goal consistency over long chains.

Can it be mixed with other GLM models? Yes, and it's recommended. Use Turbo for high-frequency execution loops, and switch to GLM-5 or GLM-5.2 when you hit nodes that require deep reasoning.

Why Choose SeaWhale AI for the GLM-5 Turbo API

No deployment, direct access — No need to build your own inference cluster
OpenAI-compatible interface — Existing agent frameworks connect directly
Full-series switching — GLM-5 / 5.1 / 5.2 / Turbo / 5V-Turbo routed on demand under the same account
Settlement in RMB — Domestic models priced in RMB

API

API integration

Model IDUse this value as the model in inference requests

GLM-5-Turbo

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-Turbo 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-Turbo",
    "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-Turbo",
    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-Turbo',
  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 ?? '')
}