GLM-4.5

glm-4.5

GLM-4.5 is an agent-oriented MoE model from Z.ai, described with 355B total parameters and 32B activated parameters. Official docs highlight reasoning, coding, tool use, and browser-style agent abilities, with both thinking and non-thinking modes. It works well as the GLM line’s earlier agent foundation model before GLM-5.

Context Window

131.1K tokens

Maximum Output

98.3K tokens

Release Date

Jul 28, 2025

Modalities

GLM-4.5 Pricing

Input Price	Output Price
$0.4286/M	$2/M

GLM-4.5 API Capabilities

Reasoning

Supported

Tool calling

Supported

Temperature parameter

Supported

Attachments

Not supported

Knowledge Base

2025-04-01

Endpoint Protocols

geminiCompletions APIMessages API

GLM-4.5 Model Highlights

GLM-4.5 is an open-weight text model that unifies reasoning, coding and agent capabilities with hybrid thinking modes and a 128K context.

Unified Agentic Reasoning

Combines reasoning, coding and agent-oriented training in one model for multi-step problem solving and software tasks.

Hybrid Thinking Modes

Offers a thinking mode for complex reasoning and an immediate-response mode for simpler workloads, allowing developers to match effort to the task.

Open-Weight MoE

Uses a mixture-of-experts architecture with 355B total and 32B active parameters, released under the MIT license for independent deployment.

GLM-4.5 Use Cases

GLM-4.5 suits software engineering agents, complex text reasoning and self-hosted development workflows that benefit from selectable thinking depth.

Software Engineering Agent

Plan code changes, inspect project files and carry out implementation and debugging steps across a multi-turn development task.

Complex Text Reasoning

Analyze long technical requirements or multi-step logic problems in thinking mode and produce a reasoned textual solution.

Private Model Deployment

Deploy the MIT-licensed open weights in controlled infrastructure for internal coding, reasoning or agent workloads.

How to Use GLM-4.5 via the TokenHub API

Create API key

Replace these path values before running: {model}

curl 'https://us-api.tokenhub.com/v1beta/models/{model}:generateContent' \
  -X 'POST' \
  -H "Authorization: Bearer $TOKENHUB_API_KEY"

GLM-4.5 Benchmarks

GLM-4.5 (Reasoning)

Index score
Artificial Analysis Intelligence IndexArtificial Analysis Intelligence Index summarizes broad model capability across evaluation families.	Artificial Analysis broad capability aggregate	19.5
Artificial Analysis Coding IndexArtificial Analysis Coding Index summarizes model performance on coding and software engineering tasks.	Artificial Analysis software task aggregate	26.3
Artificial Analysis Math IndexArtificial Analysis Math Index summarizes mathematical reasoning and competition-style problem solving.	Artificial Analysis math reasoning aggregate	73.7
Knowledge & Reasoning
MMLU-ProMMLU-Pro evaluates broad academic and professional knowledge with more challenging questions than the original MMLU.	Advanced multi-task knowledge	83.5%
GPQAGPQA is a graduate-level science benchmark designed to test expert reasoning.	Advanced science problem solving	78.2%
HLEHLE is a broad, challenging benchmark for expert-level knowledge and reasoning across many domains.	Broad expert-level exam set	12.2%
Coding & Engineering
LiveCodeBenchLiveCodeBench evaluates coding ability on recent programming problems designed to reduce benchmark contamination.	Live coding problems	73.8%
SciCodeSciCode evaluates Python programming for scientific computing, including algorithmic implementation from scientific context.	Scientific coding challenges	34.8%
Terminal-Bench HardTerminal-Bench Hard evaluates whether an AI agent can complete difficult tasks in a terminal environment.	Hard terminal task execution	22.0%
Math
MATH-500MATH-500 evaluates mathematical problem solving across a curated set of challenging questions.	Advanced math problem solving	97.9%
AIMEAIME measures mathematical reasoning on competition-style problems.	Competition math problems	87.3%
AIME 2025AIME 2025 measures performance on challenging competition math problems.	Competition math problems	73.7%
Instruction Following & Agent Tasks
IFBenchIFBench measures how reliably a model follows detailed instructions and constraints.	Prompt constraint adherence	44.1%
AA-LCRAA-LCR measures reasoning quality over extended context windows.	Long-context reasoning	48.3%
τ²-Benchτ²-Bench evaluates conversational agents in realistic workflows with tool and state constraints.	Agent workflow tasks	43.0%

Metrics sourced from Artificial Analysis

GLM-4.5 FAQ

GLM-4.5: capabilities, use cases, limits, and TokenHub guidance.

What is GLM-4.5?+

GLM-4.5 is a Z.AI model for reasoning, coding, and native agent workflows.

Which workloads suit GLM-4.5?+

Best for code reasoning, agent workflows and tool-heavy automation, especially when deep reasoning is the priority.

Which feature stands out?+

Key strength: a unified focus on reasoning, coding, and native agents.

When should teams avoid GLM-4.5?+

It belongs to an older generation and may lack newer capabilities. For the latest capabilities matter, consider GLM-5.

What should I verify in TokenHub?+

Confirm TokenHub availability; prefer the current successor for new work.

Ready to use GLM-4.5?

Use one API key to access GLM-4.5 and more AI models through TokenHub.

Create API key

GLM-4.5

GLM-4.5 Pricing

GLM-4.5 API Capabilities

GLM-4.5 Model Highlights

Unified Agentic Reasoning

Hybrid Thinking Modes

Open-Weight MoE

GLM-4.5 Use Cases

Software Engineering Agent

Complex Text Reasoning

Private Model Deployment

How to Use GLM-4.5 via the TokenHub API

GLM-4.5 Benchmarks

Models Similar to GLM-4.5

Media and Discussions

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GLM-4.5 FAQ

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