Compare
OpenAI Codex vs GLM (Z.ai)
Codex comes inside a paid ChatGPT plan and delegates cloud coding tasks; GLM sells a flat-rate plan feeding open models into the coding client you run.
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.
Both tools chosen. Compare is enabled.
Side by side
- Summary
OpenAI Codex is the agentic coding assistant included with paid ChatGPT plans rather than sold separately. It plans, writes and reviews code across a CLI, an IDE extension, the web and delegated cloud tasks, and since July 2026 its standalone desktop app lives on inside the ChatGPT desktop app.
Delegation is the distinctive move. Hand it a task and it works in an isolated cloud environment, several tasks in parallel, returning diffs and pull requests for review, and it reviews GitHub pull requests in turn. Successive GPT-5-class Codex models do the work.
MoreLess
Usage is credit-metered following the April 2026 repricing, with local and cloud runs sharing five-hour rolling windows and weekly limits possible, so heavy delegation argues for a larger plan. The Free tier permits only quick exploration; real agentic use needs a paid plan.
- 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
- One agent across CLI, IDE extension, web and desktop
- Technical founders shipping product without engineering headcount
- 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 both as a change to what powers the client you already use and through ZCode, Z.ai's own first-party client on the same Coding Plan quota. The GLM Coding Plan runs open-weight models inside clients such as Claude Code: you keep the client, the workflow and the keybindings, and change what feeds them with a configuration edit rather than a migration. For a team already settled into its tooling, that is the cheapest structural change on offer.
The honest working pattern is a daily driver plus a retained frontier seat. GLM carries the routine coding volume, while ambiguous specs and sustained multi-agent work still go to the closed frontier models, which remain ahead on exactly that kind of task. No tool is best for everyone; this one is unusually clear about which half of the work it wants.
- Best for
- Flat-rate coding subscription
- MIT-licensed GLM-5.2 open weights you can self-host
- A 1M-token context for large-codebase work
- Pay-per-token API access alongside the subscription
- A coding-first family, not a general-purpose assistant
- Cost
- Freemium (Free tier + paid plans)
- Ease
- Openness
- Runs privately (self-hostable)
- Data
- GLM's hosted terms point to Singapore while its developer is Chinese-founded, 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.
Pricing
- OpenAI Codex
Free·$8·$20·from $100$20/user·Custom
Prices as of August 2026.
- Free
- Free
- Go
- $8per month
- Plus
- $20per month
- Pro
- from $100per month
- Business
- $20per user, per month2+ users minimum; billed annually; $25 per user per month if billed monthly
- Enterprise & Edu
- Price on applicationno list price published
- GLM (Z.ai)
$12.60·$56·$117.60$79.20/user·$169.20/user
Prices as of August 2026.
- Lite
- $12.60per monthbilled annually; $18 per month if billed monthly
- Pro
- $56per monthbilled annually; $80 per month if billed monthly
- Max
- $117.60per monthbilled annually; $168 per month if billed monthly
- Standard Seat
- $79.20per user, per monthbilled annually; $88 per user per month if billed monthly
- Premium Seat
- $169.20per user, per monthbilled annually; $188 per user per month if billed monthly
By area
Where each one pulls ahead, area by area.
| Area | OpenAI Codex | GLM (Z.ai) |
|---|---|---|
| By job | ||
| Founders & entrepreneurs | OpenAI Codex has its pull-request review stand in for the second engineer an early company does not have, giving each change one systematic pass before merge | GLM makes the AI coding line item a fixed monthly number rather than a bill that scales with how hard everyone works |
| Software development | OpenAI Codex raises test coverage on a module while you stay on feature work, which is what having more well-scoped tasks than hands actually looks like | GLM's flat rate suits the shape of daily coding, where the volume is high and most of the tasks are routine |
| By task | ||
| Coding & software development | OpenAI Codex arrives inside paid ChatGPT plans, so finding out whether delegated coding suits a team requires no new purchase | GLM's flat rate makes the cost independent of usage, which is what matters when an agentic client is consuming tokens all day |
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 low-cost coding subscription, 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.
Related comparisons
Read the full guides
Where to start
Not sure what to adopt first?
Five quick questions about your job, task and constraints. We'll suggest your top three tools, plus the one to try first.
Tool facts last checked August 2026