Established OpenClaw deployments
Use a model trained specifically around OpenClaw's skills, tools, scheduling, messaging, and persistent-agent task patterns.
Independent tool overview
GLM-5-Turbo at a glance
GLM-5-Turbo is Z.AI's text-only agent model optimized specifically for OpenClaw-style tool use, command following, scheduled and persistent tasks, and high-throughput execution chains. It remains available through the Z.AI API and Coding Plan, but newer GLM-5.2 and GLM-5.3 models now provide broader long-context, coding, and multimodal capability.
Visit the official GLM-5-Turbo site ↗
- Product type
- Text-only agentic language model
- Primary optimization
- OpenClaw agent workflows
- Context window
- 200K tokens
- Maximum output
- 128K tokens
- API model ID
- glm-5-turbo
- Current status
- Active, but no longer the newest GLM generation
- Last reviewed
- August 29, 2026
Overview
What GLM-5-Turbo is
GLM-5-Turbo is a purpose-built model rather than Z.AI's newest general flagship. Its 200K-token context window, unusually large 128K maximum output, thinking modes, function calls, structured output, MCP support, and context caching are aimed at agents that must decompose instructions and keep using tools across many steps.
The model's distinctive optimization target is OpenClaw, a personal-agent framework that runs on users' devices and connects to messaging platforms. Z.AI says Turbo was trained on representative environment setup, coding, retrieval, analysis, content, scheduling, and long-chain tasks rather than merely adapted to the harness after training.
The tradeoff is specialization and age within a fast-moving family. GLM-5-Turbo accepts text only and has 200K context, while GLM-5.2 moved to a 1M-token long-horizon context and GLM-5.3-Flash now combines 1M context with native image, video, file, and visual-feedback capability at an efficiency-focused price. Keep Turbo for an established OpenClaw workflow only after measuring it against the current models.
Use cases
Who GLM-5-Turbo is best for
The strongest fit depends on the job you need the product to complete, not the size of its feature list.
Long tool chains
Run text-based tasks that require repeated function calls, command following, intermediate state, and unusually large generated output.
Scheduled and persistent work
Handle recurring triggers and long-running text workflows where time-related instructions and execution continuity matter.
Costed API automation
Build OpenAI-compatible integrations with published per-token rates, caching, streaming, JSON output, and MCP connections.
Capabilities
Core GLM-5-Turbo features
OpenClaw-specific training
Z.AI says the training data and optimization objectives include real OpenClaw task scenarios instead of treating OpenClaw as a generic chat client.
Complex instruction decomposition
Plans multi-layered requests and can divide work across steps or collaborating agents.
Tool and function calling
Invokes external functions, skills, and services so an agent can execute rather than only answer.
Scheduled and persistent tasks
Is tuned to understand time-based triggers and maintain progress across longer background workflows.
Long-chain throughput
Targets faster, more stable execution on tool-heavy tasks with substantial data flow and extended logical chains.
200K context and 128K output
Supports large text histories and exceptionally long responses, although larger limits also increase latency and cost risk.
Thinking and streaming modes
Offers configurable reasoning behavior and real-time token streaming for different quality and responsiveness needs.
Caching, JSON, and MCP
Provides discounted cached input, structured outputs, and Model Context Protocol integration for repeated context and external data sources.
Process
How the GLM-5-Turbo workflow works
Step 1
Benchmark the current family
Compare Turbo with GLM-5.3, GLM-5.3-Flash, and GLM-5.2 on your actual OpenClaw tools before choosing it for a new deployment.
Step 2
Give tools narrow contracts
Use validated schemas, least-privilege credentials, bounded outputs, timeouts, and human approval for spending, deletion, publishing, or sensitive system changes.
Step 3
Separate plan from execution
Have the model state the goal, dependencies, proposed calls, and stopping condition before beginning a long or expensive chain.
Step 4
Cache stable context
Keep repeated system instructions and reference material byte-stable where possible so the API can detect cache hits and reduce input cost.
Step 5
Cap runaway sessions
Set maximum tokens, tool calls, wall time, retries, and budget instead of relying on the 128K output ceiling as a safe default.
Step 6
Verify the finished state
Check the external system and user-visible result, not only the model's final summary, because an agent can report success after a partial or failed tool call.
Cost
GLM-5-Turbo pricing and free plan
Direct API pricing is $1.20 per million input tokens, $0.24 per million cached-input tokens, and $4 per million output tokens. Cached-input storage is currently listed as free for a limited time, and built-in web search costs an additional $0.01 per use. GLM-5-Turbo is also included in Z.AI Coding Plans, but OpenClaw traffic on those plans uses best-effort secondary scheduling: coding-agent work has priority, and heavy load can trigger dynamic queues and rate limits. Compare API billing with the current Coding Plan allowance for your workload.
Direct API
$1.20 input / $4 output per 1M tokens
Usage-based access through Z.AI's OpenAI-compatible API.
- $0.24 per 1M cached-input tokens
- Cached-input storage listed as free for a limited time
- Web search costs $0.01 per use
- Output-heavy agents can cost more than input-heavy estimates suggest
GLM Coding Plan
Subscription allowance
Includes GLM-5-Turbo in supported coding and OpenClaw workflows.
- Exact quota depends on the current Lite, Pro, or Max plan
- OpenClaw is delivered on a best-effort secondary schedule
- Coding-agent requests can preempt OpenClaw traffic
- High load can cause dynamic queuing or rate limits
Local weights
Not published for Turbo
The official GLM-5-Turbo page documents hosted API use and does not link a public Turbo checkpoint.
- Do not assume the open-weight GLM-5 or GLM-5.3 licenses apply to Turbo
- Use a separately released open-weight GLM model if self-hosting is required
Pricing checked . Check current pricing at the source ↗
Assessment
GLM-5-Turbo strengths and limitations
Where it stands out
- Purpose-built optimization for OpenClaw instead of a generic chat-only model
- Strong feature coverage for tools, MCP, JSON, caching, thinking, and streaming
- Large 200K context and 128K maximum output suit extended text workflows
- Published token rates make direct API costs modelable
- Discounted cached input is valuable for repeated agent instructions and long histories
- Works through an OpenAI-compatible Chat Completions endpoint
What to consider
- GLM-5-Turbo is no longer Z.AI's newest GLM option; current 5.2 and 5.3 models offer broader capability
- The model is text-only and cannot directly inspect screenshots, images, video, or rendered output
- The 200K context is smaller than the 1M context available on newer GLM models
- OpenClaw use through the Coding Plan is best-effort and lower priority than coding-agent traffic
- Very long output and tool chains can become slow, expensive, repetitive, or unstable without hard limits
- Z.AI has documented rare repetition, garbled text, and unusual-character failures in high-concurrency, long-context GLM-5 coding workloads
- Vendor ZClawBench results should be reproduced with the buyer's tools, permissions, prompts, and failure conditions
Compare
GLM-5-Turbo alternatives
The right alternative depends on the specific output, workflow, controls and budget your project requires.
Consumer
GLM-5.3
Use GLM-5.3 for Z.AI's newer flagship agentic and coding capability, especially when evaluating a new deployment.
Explore GLM-5.3 →Coding
GLM 5.2
Consider GLM-5.2 when a proven 1M-token context and stronger long-horizon task handling matter more than Turbo's OpenClaw-specific tuning.
Explore GLM 5.2 →Consumer
Minimax M2.7
Consider MiniMax M2.7 for another cost-conscious agentic model focused on coding and long-running tool workflows.
Explore Minimax M2.7 →Consumer
Claude Opus 4.8
Consider Claude Opus 4.8 for a higher-priced frontier model with strong coding and agentic performance across mature tool ecosystems.
Explore Claude Opus 4.8 →Questions
GLM-5-Turbo FAQs
What is GLM-5-Turbo?
GLM-5-Turbo is a text-only Z.AI model optimized for OpenClaw tools, instructions, schedules, persistent tasks, and long execution chains. It is available through the Z.AI API and supported Coding Plans.
Is GLM-5-Turbo the latest GLM model?
No. It remains active, but GLM-5.2 and the GLM-5.3 family are newer. GLM-5.3-Flash also adds native multimodal input and a 1M-token context.
How much does GLM-5-Turbo cost?
Direct API pricing is $1.20 per million input tokens, $0.24 per million cached-input tokens, and $4 per million output tokens. Built-in web search is $0.01 per use.
What is GLM-5-Turbo's context window?
The model supports 200K tokens of context and up to 128K output tokens. Set a much smaller output cap for ordinary agent tasks to control latency and cost.
Does GLM-5-Turbo support images?
No. Its official input and output modalities are text. Choose a newer multimodal GLM model when screenshots, documents, video, or visual self-verification are required.
Can I use GLM-5-Turbo with OpenClaw?
Yes; that is its primary optimization target. Direct API usage and the GLM Coding Plan are supported, but Coding Plan OpenClaw traffic is best-effort and may be queued under load.
Is GLM-5-Turbo open source?
The official Turbo documentation provides API access but does not link public model weights. Related GLM releases have separate open-weight checkpoints and licenses, which should not be assumed to cover Turbo.
Bottom line
Our GLM-5-Turbo verdict
GLM-5-Turbo is still a practical fit for an existing OpenClaw deployment that values tool-chain behavior, large output, caching, and predictable API rates. It is no longer the obvious default for new work. Z.AI's newer 5.2 and 5.3 models bring 1M context, stronger general agent performance, and—in 5.3-Flash—native visual understanding. Run a controlled benchmark, account for best-effort plan scheduling, and keep Turbo only when its OpenClaw specialization produces a measurable advantage.
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