GLM (Z.ai)
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.
- Cost
- Freemium (Free tier + paid plans)
- Ease
- Model
- Runs privately (self-hostable)
- Checked
- August 2026
Prices, plans and model versions change fast: this is a mid-2026 snapshot; check the tool's official site for the latest.
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
Less suited to
Developers who want an agent client of their own have two routes: Z.ai's own ZCode agent, or Kimi Code, which ships an MIT-licensed client for terminal and IDEs. Anyone wanting the cheapest possible entry into coding assistance starts at GitHub Copilot, whose free tier and in-editor integrations cover the first mile. And a generalist who codes only occasionally is better served by Claude or ChatGPT than by a coding-first subscription.
The hosted API's terms point to Singapore while the developer is Chinese-founded, a jurisdiction question some organisations must answer before routing source code through it. Self-hosting the open weights removes that question entirely, because the model runs on infrastructure you control, but it also converts a subscription into an infrastructure project, and that operational work is the buyer's to own.
Costs & data, in short
The GLM Coding Plan runs as a flat subscription that feeds tools such as Claude Code, with pay-per-token API access alongside; the open-weight models cost nothing to download and run yourself, with spend shifting to your own hardware.
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.
Plans
| 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 |
Prices as of August 2026. Prices and plans change regularly. Check with the provider before you buy.
In practice
How GLM (Z.ai) is used, area by area.
Jobs
Founders & entrepreneurs
For a founder
For a founder, the appeal is a cost line that stops moving: the Coding Plan, a flat monthly subscription, changes what powers the tools the team already uses, so the spend becomes predictable while velocity stays intact. The pay-per-token API sits alongside for anything better metered. The candid caveat is the pairing: the hardest problems still sit with a closed frontier model, so the realistic budget keeps one frontier seat and pushes the volume through the flat plan. Founders funding high coding volume from a tight budget gain the most.
Example tasks
- Cap the team's monthly AI coding spend at a known figure
- Prototype and iterate at high volume without metered bills
- Keep one frontier seat and route routine work to the flat plan
- Call the same models by pay-per-token API where metering suits
- Change the model behind the team's tooling
Limits
If the venture's hard problems are constant rather than occasional, a flat plan on open weights will not replace the frontier seat, and paying for both only makes sense once the routine volume justifies it.
Compares
| vs | Pick GLM (Z.ai) when | Pick the other when |
|---|---|---|
| ChatGPT | GLM is a narrow bet on coding economics, feeding open-weight models into the coding client a technical founder already uses | you need one versatile assistant across writing, research, analysis and coding rather than a restructuring of coding costs |
| Claude CodeFull comparison → | GLM changes what powers the tools the team already uses, though a venture whose hard problems are constant rather than occasional will not replace the frontier seat with a flat plan | founder time is the scarcest resource and whole features can be delegated against the repo |
| Kimi CodeFull comparison → | GLM caps the team's monthly AI coding spend at a known figure through the flat Coding Plan, with a pay-per-token API alongside for anything better metered | access should scale from a solo subscription through pay-as-you-go, and the option to run behind an existing Claude Code setup matters as much as the price |
| CursorFull comparison → | GLM changes the model behind the team's tooling for a team that keeps its existing client, so that decision costs little learning time at a stage when there is none to spare | the product is past prototype, the codebase has weight, and you are still a hands-on builder |
| OpenAI CodexFull comparison → | GLM makes the AI coding line item a fixed monthly number rather than a bill that scales with how hard everyone works | you are the engineering department and the roadmap outruns your hands |
| ReplitFull comparison → | GLM changes the cost and not the workflow, for founders whose developers already work in a client such as Claude Code | you need a working product to sell and the infrastructure should be someone else's problem |
| GitHub CopilotFull comparison → | GLM keeps one frontier seat and routes the routine work to the flat plan, which is what a realistic early budget can actually carry | the founder is already shipping on GitHub and wants low-friction AI in the existing workflow rather than a new editor |
| Base44Full comparison → | GLM is aimed at founders funding high coding volume from a tight budget, which presumes there is coding volume to fund | you are non-technical and want a working app rather than a codebase to maintain |
| Bolt (StackBlitz)Full comparison → | GLM lets a team prototype and iterate at high volume without metered bills, so the cost of trying things stops tracking how often you try them | the work is early validation, throwaway prototypes and rapid experimentation, before a concept is proven enough to justify a production stack |
Software development
GLM earns its place in a developer's stack through the mechanics of adoption: the Coding Plan runs open-weight models inside the client you already use, so the trial costs a configuration edit and walking away costs only reverting it. The flat rate suits the shape of daily coding, where volume is high and most tasks are routine, and the 1M-token context gives it the reach for the large codebases where that routine work actually lives. What it does not claim is the frontier crown; the sensible setup pairs it with a retained frontier seat. Developers with heavy daily coding volume who are already settled inside Claude Code or a similar client gain the most.
Example tasks
- Point Claude Code at GLM for routine implementation work
- Work across large repositories within the 1M-token context
- Handle refactoring and test-writing volume on a flat plan
- Reserve a frontier seat for ambiguous or architectural problems
- Switch models back and forth with a configuration edit
Limits
On ambiguous specs and sustained multi-agent work, the closed frontier models still win. Treat GLM as the daily driver and keep a frontier seat for the hard problems rather than expecting one subscription to cover both.
Compares
| vs | Pick GLM (Z.ai) when | Pick the other when |
|---|---|---|
| Claude CodeFull comparison → | GLM restructures the cost of daily coding onto a flat subscription, whether through the client you already use or Z.ai's own ZCode | the work is ambiguous, genuinely hard, or runs sustained multi-agent sessions |
| QwenFull comparison → | GLM makes the trial cost a configuration edit and walking away cost the same, which is the whole argument for trying it inside the client you already run | the team wants a coding model it controls end to end rather than a plan that feeds the one it has |
| Kimi CodeFull comparison → | GLM works across large repositories inside a 1M-token context and handles the refactoring and test-writing volume that make up most of a daily coding routine, all on the flat plan | the shape wanted is its own client that other tools can call into, rather than a plan that runs inside the one you already have |
| CursorFull comparison → | GLM disturbs nothing about the setup it arrives in, because the Coding Plan swaps what feeds the existing tools, and Z.ai's own ZCode client is there if you would rather adopt one | the editor is where you live and you want the strongest AI woven through it |
| OpenAI CodexFull comparison → | GLM's flat rate suits the shape of daily coding, where the volume is high and most of the tasks are routine | the backlog holds more well-scoped tasks than hands and delegation would genuinely clear it |
| ReplitFull comparison → | GLM restructures the running cost without changing how anybody works, which is a narrower promise than a new environment and a cheaper one to test | the idea should be running this afternoon rather than configured this week |
| GitHub CopilotFull comparison → | GLM is aimed at developers with heavy daily coding volume who are already settled inside a client such as Claude Code, rather than at anyone still choosing one | development runs through GitHub and you want AI in the editor, the issues and the pull requests |
| DeepSeekFull comparison → | GLM is pointed at Claude Code for routine implementation work and needs no wiring built around it first | you want strong models cheap at volume, or fully under your control |
| Devin CloudFull comparison → | GLM keeps a frontier seat reserved for the ambiguous and architectural problems and runs everything else on the flat plan | delegation with review fits the workflow better than another pair of hands |
Tasks
Coding & software development
In the coding category GLM occupies a lane of its own
In the coding category GLM occupies a lane of its own: the Coding Plan feeds the client already in daily use, and Z.ai's own ZCode agent draws on the same plan. The Coding Plan's flat rate makes the cost of GLM independent of usage, which matters when an agentic client is consuming tokens all day. The structure is honest about its ceiling: this is the daily driver in a two-model setup, with the closed frontier retained for the work that defeats open weights. Teams whose coding volume is steady enough for flat pricing to pay for itself gain the most.
Example tasks
- Feed open-weight models into Claude Code without changing clients
- Sustain long agentic sessions across the 1M-token context
- Cover routine implementation, refactoring and test work at flat cost
- Split work between a daily driver and a retained frontier seat
- Move between subscription and pay-per-token API as usage shifts
Limits
This is not the lane for a first coding assistant; the value assumes an existing client and enough sustained volume for flat pricing to matter. Nor is it where the hardest work should land; that remains frontier territory.
Compares
| vs | Pick GLM (Z.ai) when | Pick the other when |
|---|---|---|
| Kimi CodeFull comparison → | GLM feeds its open-weight models into the client you already use, and Z.ai also ships ZCode, its own agent drawing on the same Coding Plan | you would rather adopt its open-source agent for terminal and IDE work than reconfigure an existing tool |
| QwenFull comparison → | GLM is not the lane for a first coding assistant: the value assumes a client already in daily use and enough sustained volume for flat pricing to matter | the model layer itself is what you want, open weights served and wired into your own agentic workflows |
| Claude CodeFull comparison → | GLM splits the work rather than replacing it: routine implementation, refactoring and test volume run at flat cost while the hardest problems stay on a frontier seat | whole tasks should be handed over in a real codebase rather than autocompleted inside one |
| CursorFull comparison → | GLM changes the model and leaves the client alone, so nothing about the way a team already works has to move | you live in an editor all day and want the strongest AI woven into it |
| OpenAI CodexFull comparison → | GLM's flat rate makes the cost independent of usage, which is what matters when an agentic client is consuming tokens all day | delegation should become the default way routine code gets written, dispatched to parallel cloud environments and returned as pull requests |
| GitHub CopilotFull comparison → | GLM keeps the switch reversible, because open-weight economics arrive inside the coding client already in use and leaving costs what arriving did | you want capable AI across the whole GitHub-centred workflow rather than the sharpest tool in one lane |
| Sourcegraph CodyFull comparison → | GLM brings a general-purpose model family with a flat-rate coding subscription, and Z.ai's own ZCode client alongside it | the codebase is vast, spread across repos, and context is what assistants lack |
| DeepSeekFull comparison → | GLM moves between the subscription and the pay-per-token API as usage shifts, so the commercial shape follows the work instead of fixing it | coding AI spend matters, or you want strong open weights you control |
| Devin DesktopFull comparison → | GLM sustains long agentic sessions across the 1M-token context, which is where an agent that keeps working actually spends its time | you want an agentic editor that anticipates rather than waits for instructions |
Private, local & self-hosted
GLM belongs in this category because the MIT licence converts a jurisdiction question into an infrastructure decision. The hosted API's terms point to Singapore while the developer is Chinese-founded, a combination some organisations cannot accept for source code; the same weights, self-hosted, remove that question entirely because nothing leaves the infrastructure you run. And the weights are worth the deployment effort here precisely because they are coding-first with a 1M-token context: capability aimed at the private-codebase work that motivates self-hosting in the first place. Organisations that want a strong coding model but cannot send code to anyone's hosted service gain the most.
Example tasks
- Self-host the MIT-licensed GLM-5.2 weights on infrastructure you control
- Keep proprietary source code inside your own network end to end
- Serve the 1M-token context internally for large-codebase work
- Answer the hosted-API jurisdiction question by running the model yourself
- Deploy coding capability under a permissive MIT licence
Limits
Self-hosting is real infrastructure work, and a coding model with a 1M-token context is a serious deployment rather than a laptop experiment. If the goal is a quick local model for one developer, Ollama's two commands to a running model fit the prototyping job better.
Compares
| vs | Pick GLM (Z.ai) when | Pick the other when |
|---|---|---|
| QwenFull comparison → | GLM brings a coding-first family with a 1M-token context and MIT weights to a self-hosted stack | you want the current default for new self-hosted coding work and its actively maintained Apache-2.0 line |
| TabnineFull comparison → | GLM's MIT licence converts a jurisdiction question into an infrastructure decision, which is a problem an organisation can solve with machines rather than with paperwork | the organisation cannot let source code leave the network and needs on-premises or air-gapped assistance with zero data retention |
| DeepSeekFull comparison → | GLM is for the case where your rules about where source code may travel exclude hosted APIs, GLM's own included, and you want its coding capability anyway | you want top-tier open weights on your own hardware rather than the hosted app |
| AnythingLLMFull comparison → | GLM serves the 1M-token context internally for large-codebase work, which is capability aimed at the private-codebase job rather than at documents | the job is asking questions of your own documents, with everything kept local |
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.
Alternatives
Same category, different strengths.
Appears in these stacks
Curated combinations this tool is part of.
Common questions
Is GLM (Z.ai) free?
There's a free tier to start; paid plans add capacity and features.
Where does GLM (Z.ai) fit best?
GLM (Z.ai) fits best in Software development and Founders & entrepreneurs; see its practice notes for how.
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Last checked: August 2026