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GLM-5

Open model line from Z.ai focused on agentic engineering and longer coding workflows.

Visit official site Open repository
MIT License
Open source
GLM-5 MIT License chat.z.ai verified 2026-04-19
About

GLM-5 overview

GLM-5 is Z.ai's open model line positioned around agentic engineering: workflows where a model reasons across files, tools, tests, and implementation steps rather than only completing code snippets.

✦

Agentic engineering direction

GLM-5 is framed around engineering workflows that involve planning, tool use, code edits, and verification.

That makes it more relevant to coding agents than a model that only optimizes short answer quality.
✦

Open model access path

The public repository gives builders a starting point for reviewing model materials and launch details.

Open access lets teams test the model against their own codebases instead of relying on a closed demo.
✦

Workflow-oriented evaluation target

The project language emphasizes the shift from vibe coding toward more structured agentic work.

That is the same direction OpenAgent tracks across models, agents, and skills.
Use cases

When to use GLM-5

Coding agent experiments

Evaluate GLM-5 inside an agent loop that plans, edits, runs checks, and revises code.

Software engineering benchmarks

Use it as a candidate when testing repository-level issue fixing rather than isolated prompts.

Open model comparison

Compare it against Qwen, Kimi, and DeepSeek-style coding models on the same code tasks.

Compare

How it compares

Best compared against coding-agent models vs Qwen3.6 and Kimi-Dev

GLM-5 belongs in the agentic engineering comparison set, where the question is not only code generation but whether the model can support longer tool-driven workflows.

FAQ

Questions

What should I check before using GLM-5?

Run GLM-5 on a fixed prompt set from your own workflow. Compare quality, latency, context handling, retry behavior, deployment path, and license fit against nearby open models before adopting it.

Is GLM-5 open source?

GLM-5 is listed with MIT based on the official source links in this profile. Re-check the repository, model card, or docs before production use.

Who should evaluate GLM-5?

GLM-5 is most worth evaluating for developers comparing open coding models for agentic engineering tasks.

Tags

Capabilities

workflow orchestrationtool callinglocal inferenceopen sourceopen weightsdeveloper workflow
Decision brief

Should you use GLM-5?

JSON
Best for
  • Developers comparing open coding models for agentic engineering tasks
  • Teams testing long-running code modification and review workflows
  • Researchers tracking open model progress in tool use and software engineering
Not for
  • Users who only need a hosted chat assistant
  • Teams that require a mature managed SLA around the model runtime
Trust and freshness
  • Verified 2026-04-19
  • License: MIT
  • Repo: zai-org/GLM-5
  • Open-source signal
Deployment

cloud

Permission surface

shell/files

Decision signals

No extra signals recorded

Agent packet

Structured decision data for GLM-5

This packet is the compact machine-readable view agents should use before following source links or taking action.

Full JSON Agent packet Markdown brief
Capabilities

workflow orchestration, tool calling, local inference

Constraints

open source, open weights

Deployment

cloud

Permission surface

shell/files

Recommended workflows

Coding agent workflow, Local or private AI stack

Overview

What GLM-5 does

What it is

GLM-5 is an open model resource to evaluate by workload, serving path, context behavior, license terms, and how reliably it supports the agent or local AI tasks you actually plan to run.

Why it matters

GLM-5 matters because coding models are moving from autocomplete toward agentic engineering. For OpenAgent readers, it is a useful signal that open model labs are optimizing for longer tool-using loops, not just static benchmarks.

How to evaluate it

Run GLM-5 on a fixed prompt set from your own workflow. Compare quality, latency, context handling, retry behavior, deployment path, and license fit against nearby open models before adopting it.

Facts

Known metadata and operating surface

These fields are separated from editorial interpretation so agents can reason over facts and missing checks.

Resource type model
Category Models
Maturity active
Difficulty Unknown
License MIT
Pricing open source
Verified 2026-04-19
Source confidence high
Risk level moderate
Fit matrix

Where GLM-5 fits in an agent stack

strong

Coding agent workflow

GLM-5 has multiple signals for coding agent workflow, including matching tags, capabilities, category, or positioning.

  • Run a small repository change and inspect the diff, tests, and rollback path.
  • Confirm official docs, current maintenance, license, and runtime constraints before production use.
strong

Local or private AI stack

GLM-5 has multiple signals for local or private ai stack, including matching tags, capabilities, category, or positioning.

  • Verify hardware requirements, data path, storage, and whether all calls stay in your environment.
  • Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial

Evaluation and observability

GLM-5 has at least one signal for evaluation and observability, but should be checked against a real task before adoption.

  • Add one repeatable test case and confirm results can run again in review or CI.
  • Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial

Reusable skill workflow

GLM-5 has at least one signal for reusable skill workflow, but should be checked against a real task before adoption.

  • Run one skill end to end and check whether it produces evidence or structured output.
  • Confirm official docs, current maintenance, license, and runtime constraints before production use.
weak

Browser automation

GLM-5 is not primarily positioned for browser automation in the current metadata.

  • Run one non-sensitive website task and inspect clicks, waits, retries, and changed URLs.
  • Confirm official docs, current maintenance, license, and runtime constraints before production use.
weak

Connector or protocol layer

GLM-5 is not primarily positioned for connector or protocol layer in the current metadata.

  • Connect one low-risk service, then inspect schemas, auth scope, errors, and logs.
  • Confirm official docs, current maintenance, license, and runtime constraints before production use.
Inputs and outputs

What an agent should inspect

Likely inputs

  • Repositories, files, issues, terminal output, and test results
  • Prompts, messages, documents, images, or model inputs
  • Official setup instructions and a small real workflow

Likely outputs

  • Diffs, commits, explanations, test results, or review notes
  • A decision on whether this resource fits the target workflow
Evidence

Sources, claims, and missing checks

Claims are marked separately from source links so future crawlers and reviewers can update them without rewriting the page.

GitHub github

Repository source for code, license, issues, releases, and implementation details.

 Homepage homepage

Official or project-controlled source for this resource profile.
verified

GLM-5 is listed as open source.

License metadata: MIT
verified

GLM-5 has a recorded GitHub repository: zai-org/GLM-5.

Resource facts and GitHub source link.
inferred

GLM-5 supports these recorded deployment modes: cloud.

OpenAgent decision signal metadata.
inferred

GLM-5 is tagged with workflow orchestration, tool calling, local inference capabilities.

OpenAgent capability taxonomy.
Missing checks
  • Dedicated docs link is missing.
  • Repository freshness has not been recorded.
Next action

How to start evaluating GLM-5

Inspect repository

Check license, recent activity, issues, examples, and security-sensitive code paths.

Open source

Open Homepage

Start from the official source before adopting third-party instructions.

Open source

Clone the repository

Use the official repository as the starting point for model notes, examples, and updates.

git clone https://github.com/zai-org/GLM-5.git
Compare

Alternatives and nearby resources

Use related resources to compare category fit, license, deployment model, and first-workflow behavior.

Rapid-MLX

Apple Silicon local AI engine with OpenAI-compatible API, tool calling, prompt cache, and MLX acceleration.

Models LiteRT-LM

Google's open-source inference framework for deploying large language models on edge devices.

Models FinGPT

Open-source financial large language model project for finance sentiment, analysis, and domain adaptation.

Models
FAQ

Common questions about GLM-5

What should I check before using GLM-5?

Run GLM-5 on a fixed prompt set from your own workflow. Compare quality, latency, context handling, retry behavior, deployment path, and license fit against nearby open models before adopting it.

Is GLM-5 open source?

GLM-5 is listed with MIT based on the official source links in this profile. Re-check the repository, model card, or docs before production use.

Who should evaluate GLM-5?

GLM-5 is most worth evaluating for developers comparing open coding models for agentic engineering tasks.