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OpenAI Codex vs GLM (Z.ai)
A plain-English comparison to help you choose between them.
Both restructure what serious agentic coding costs, from different directions: Codex arrives inside the paid ChatGPT plan a team may already hold, while GLM sells a flat-rate plan that feeds open-weight models into the coding client you already run. Pick OpenAI Codex when ChatGPT is already the company subscription and you want delegated cloud tasks, parallel runs and pull-request review without a second purchase. Pick GLM when delegation has become a daily habit and credit metering is the pain, because the flat plan absorbs volume that plan limits would otherwise interrupt. On genuinely hard, ambiguous work the closed frontier models keep the edge, which favours Codex's GPT-5-class line over open weights.
Side by side
- Summary
OpenAI Codex is the agentic coding assistant included with paid ChatGPT plans rather than sold separately.
- Best for
- Delegating coding tasks to parallel cloud environments
- Agentic coding bundled into an existing ChatGPT plan
- Automated first-pass review of GitHub pull requests
- Cost
- Paid only
- Ease
- Openness
- Hosted service
- Data
- Codex is closed and cloud-run. Prompts, code and repository context are processed on OpenAI's services, so check the training and retention terms of your plan tier and the workspace-level data controls it carries before connecting private repositories. Treat agent output as untrusted until reviewed: agent-written code and auto-approved pull-request reviews need a human pass before production, especially around payments, auth and regulated data flows.
- Summary
GLM is Z.ai's coding-first model family, sold less as a new tool than as a change to what powers the one you already have.
- Best for
- Flat-rate coding subscription from around $18 a month
- MIT-licensed GLM-5.2 open weights you can self-host
- A 1M-token context for large-codebase work
- Cost
- Freemium (Free tier + paid plans)
- Ease
- Openness
- Runs privately (self-hostable)
- Data
- GLM's hosted API is served from China, a jurisdiction point to weigh before routing real code or prompts through it. Self-hosting the open weights removes that question entirely: the model runs on infrastructure you control, and nothing leaves it.
Common questions
Does either require a new subscription?
Codex does not, if the team already pays for ChatGPT: real agentic use needs a paid plan, but it is bundled rather than sold separately, and the free tier permits only quick exploration. GLM is its own purchase, a coding-first subscription from around $18 a month, with pay-per-token API access alongside for anything better metered.
What does GLM actually change in a setup?
Only the model layer. The Coding Plan runs open-weight models inside clients such as Claude Code, so adopting it is a configuration edit and abandoning it is reverting one. Codex is the opposite shape: its own agent across CLI, IDE extension, web and the ChatGPT desktop app, with delegated tasks running in isolated cloud environments, several in parallel.
Where does each hit its ceiling?
GLM's ceiling is capability: on ambiguous specifications and sustained multi-agent work, closed frontier models still win, so the flat plan works best as a daily driver beside a retained frontier seat. Codex's ceiling is the meter: usage is credit-metered under plan limits, and a heavy delegation habit is the signal to size the plan up rather than push through.
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Tool facts last checked July 2026