Z.ai
GLM-5.3-FlashX
glm-5.3-flashx
Total Context
1M
Max Output
131.1K
Released
N/A
Input$0.2857 / M tokens
Output$1 / M tokens
GLM-5
glm-5GLM-5 is Z.ai’s new-generation foundation model for agentic engineering, complex systems engineering, and long-range agent tasks. Official docs describe it as an open foundation model aimed at production-grade programming, planning, backend reasoning, and iterative self-correction. Its page description should highlight complex engineering workflows rather than only “Chinese LLM” positioning.
Context Window
204.8K tokens
Maximum Output
131.1K tokens
Release Date
Feb 11, 2026
Modalities
GLM-5 Pricing
| Input Price | Output Price | Cache Read |
|---|---|---|
| $0.5714/M | $2.5714/M | $0.1143/M |
GLM-5 API Capabilities
Reasoning
Supported
Tool calling
Supported
Temperature parameter
Supported
Attachments
Not supported
Knowledge Base
—
Endpoint Protocols
Completions APIMessages APIgemini
GLM-5 Model Highlights
GLM-5 is an open-weight text model focused on complex systems engineering, long-range agent planning and deep software debugging.
Complex Systems Engineering
Understands multi-component software systems and supports frontend development, backend engineering and coordinated architectural changes.
Long-Range Agent Planning
Plans and carries out extended sequences of engineering actions with limited human intervention.
Deep Software Debugging
Investigates failures across code paths, proposes root causes and iterates on repairs using execution feedback.
GLM-5 Use Cases
GLM-5 suits backend refactoring, complex defect investigation and autonomous implementation workflows spanning multiple engineering stages.
Backend System Refactoring
Map dependencies in an existing backend, plan architectural changes and revise connected modules while retaining expected behavior.
Complex Defect Investigation
Trace failures across components, inspect relevant code and test hypotheses to produce a targeted repair.
Autonomous Engineering Workflow
Carry a software task from planning through implementation and verification, using intermediate results to adjust subsequent actions.
How to Use GLM-5 via the TokenHub API
import OpenAI from "openai"
const client = new OpenAI({
apiKey: process.env.TOKENHUB_API_KEY,
baseURL: "https://us-api.tokenhub.com/v1",
})
const result = await client.chat.completions.create({})
console.log(result.choices[0]?.message?.content)GLM-5 Benchmarks
| Index score | ||
|---|---|---|
| Artificial Analysis Intelligence Index | Artificial Analysis broad capability aggregate | 39.5 |
| Artificial Analysis Coding Index | Artificial Analysis software task aggregate | 44.2 |
| Knowledge & Reasoning | ||
| GPQA | Advanced science problem solving | 82% |
| HLE | Broad expert-level exam set | 27.2% |
| Coding & Engineering | ||
| SciCode | Scientific coding challenges | 46.2% |
| Terminal-Bench Hard | Hard terminal task execution | 43.2% |
| Instruction Following & Agent Tasks | ||
| IFBench | Prompt constraint adherence | 72.3% |
| AA-LCR | Long-context reasoning | 63.3% |
| τ²-Bench | Agent workflow tasks | 98.2% |
Metrics sourced from Artificial Analysis
GLM-5 FAQ
GLM-5: capabilities, use cases, limits, and TokenHub guidance.
What problem does GLM-5 address?+
GLM-5 is a Z.AI model for complex system engineering and long-range agent tasks.
When should I choose GLM-5?+
Best for complex coding, agent workflows and repository-scale development, especially when software-engineering quality is the priority.
What does GLM-5 do well?+
Key strength: a strong focus on complex system engineering and long-range agents.
What does GLM-5 sacrifice?+
It is text-focused and does not offer the same multimodal breadth as newer models. For long-horizon task completion, consider GLM-5.2.
How do I use GLM-5 in TokenHub?+
Use the exact ID shown by TokenHub; follow your account docs and verify current features.
Ready to use GLM-5?
Use one API key to access GLM-5 and more AI models through TokenHub.
Media and Discussions
Selected public videos and posts related to this model.
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