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Models profile

GLM-5

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

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.

Best for Developers comparing open coding models for agentic engineering tasks
First test Run a real workload set, then compare output quality, latency, serving path, context limits, and license fit.
Open repository View sources
Decision signals
Open source
Tags
workflow orchestrationtool callinglocal inferenceopen sourceopen weightsdeveloper workflow

Model profile

What is GLM-5 good for?

Model profiles should make capability, deployment, license, and evaluation tradeoffs concrete before readers choose a model for an agent or local workflow.

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.

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.

Fit check

Workloads where this model makes sense

Good fit if

  • 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 a fit if

  • Users who only need a hosted chat assistant
  • Teams that require a mature managed SLA around the model runtime

Model evaluation

What to test before adopting it

Capability

Reasoning, coding, multimodal, OCR, local assistant, or tool-planning behavior.

The workload should drive the model choice.
Deployment

Local runtime, open weights, hosted API, self-hosted inference, or hybrid routing.

A strong model can still fail your constraints.
Evaluation

Real prompt sets, latency, cost, context handling, license fit, and retry behavior.

Benchmarks alone are not enough.

First test

How to evaluate it before committing

Run a real workload set, then compare output quality, latency, serving path, context limits, and license fit.

Keep the first test small enough that you can inspect the source, understand the permissions, and compare the result with nearby OpenAgent resources.

Workflows

Best model workloads to evaluate

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

Compare by workload and serving path

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.

Resource Category License Stars
DeepSeek V4 Models MIT n/a
DeepSeek-R1 Models MIT 91,963
GLM-OCR Models MIT model / Apache-2.0 code n/a

FAQ

Model adoption 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.

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