Zhipu AI Software engineering & agents
GLM 5.3
Keep a difficult text-only engineering task measurable from plan to patch.
GLM 5.3 is a Z.ai route for complex software engineering and long-horizon agent tasks. TokenHot lists text-only input, a 1M context value, always-on reasoning with low, high and max effort levels, function calling and structured output. Use it when the work is code, terminal operations or vulnerability analysis rather than visual understanding.
- Text input only
- 1M context
- Always-on reasoning
GLM 5.3 is currently cataloged for text input only. Do not route screenshots or image understanding tasks here simply because the model has a large context window.
AI Chat
Send a prompt and see the response here.
glm-5.3Start a conversation with this model and ask follow-up questions.
USD /1M tokens
Pricing
| Group | Input | Output | Cache Read |
|---|---|---|---|
| default | $1.19 | $4.150006 | $0.299999 |
| glm-0.85 | $1.0115 | $3.527505 | $0.254999 |
Public list prices are shown in USD. Final charges may vary by account group and usage tier.
Overview
What is GLM 5.3?
A good GLM 5.3 request is explicit about repository state, command boundaries and acceptance tests. Ask for a plan before allowing terminal tools, then require actual logs and diffs for any execution claim.
Reasoning effort changes the trade-off between depth, latency and cost. Compare low, high and max on the same engineering fixtures instead of treating max as a universal default.
Bound the command surface
Define allowed paths, commands and stop conditions. Ask the model to return arguments before execution.
Find the risky boundary
Require file, symbol and data-flow references. Separate static findings from tests or runtime evidence.
Make the result mergeable
Use a change schema with tests, risk and rollback fields so review does not depend on prose alone.
From brief to deliverable
Put GLM 5.3 to work
Developer-tool teams
Prepare a safe terminal operation
Turn an issue into a bounded command plan with expected outputs and a stop state for missing evidence.
- Tools you need
- A terminal gateway, permission policy and command logs.
- Keep in mind
- The model cannot report a command as passed before your system returns its output.
View a task brief
Use the supplied issue context, repository rules, allowed commands and target artifact. to plan prepare a safe terminal operation. Return a validated command plan and execution checklist., state the evidence limits and list the checks required before accepting the result. Do not claim an output or interaction was verified without direct evidence.
Security and reliability teams
Audit code for a vulnerability pattern
Search supplied files for a defined pattern, rank findings and give a safe reproducer or a reason it needs more evidence.
- Tools you need
- Repository search, static analysis and a human security reviewer.
- Keep in mind
- Static findings do not establish runtime exploitability without confirmation.
View a task brief
Use the supplied a pattern definition, source tree, severity rubric and exceptions. to plan audit code for a vulnerability pattern. Return a source-linked findings report and remediation plan., state the evidence limits and list the checks required before accepting the result. Do not claim an output or interaction was verified without direct evidence.
Engineering teams
Return a structured patch brief
Draft a patch plan and regression matrix from a bug report, keeping unverified commands and assumptions separate.
- Tools you need
- Repository access and a test runner for execution.
- Keep in mind
- A structured proposal is not a merged or tested change.
View a task brief
Use the supplied a bug report, relevant modules, tests and style constraints. to plan return a structured patch brief. Return a machine-readable patch brief for review., state the evidence limits and list the checks required before accepting the result. Do not claim an output or interaction was verified without direct evidence.
Capabilities in context
Key features
| Feature | What you get | Why it matters |
|---|---|---|
| Context capacity | 1M tokens shown in the catalog | Keep project context available while controlling retrieval and output budgets. |
| Input boundary | Text input only is listed | Use a separate multimodal route when the task requires visual or audio evidence. |
| Reasoning levels | Always-on reasoning with low, high and max effort options | Select an effort policy and compare accepted results, latency and cost on the same fixtures. |
| Engineering tools | Function calling and structured output are listed | Validate arguments and keep terminal access, permissions and execution outside the model. |
| Version identity | TokenHot model ID: glm-5.3 | Pin the route and reasoning level in logs for reproducible engineering runs. |
The page reflects TokenHot catalog capabilities and input limits. GLM 5.3 is text-only on this route; verify effort parameters and tool behavior before integration.
Audience & fit
Where GLM 5.3 fits
Software engineering teams
Use it for repository planning, code review and structured patch proposals where a human or CI system can verify the result.
Security and reliability teams
Pair source-linked findings with static and runtime checks. Keep exploitability and impact claims evidence-based.
Developer-tool builders
Expose a bounded command surface and record every tool result. Use the reasoning level that matches the task risk.
Cost context
Plan the cost of a useful result
Count validation time
Terminal calls, tests, static analysis and human review are part of the accepted result cost.
Tune reasoning by task
High or max effort may help hard bugs but increase latency and cost. Measure against a fixture set rather than guessing.
Keep context relevant
A 1M window does not mean every repository file should be sent. Retrieve the symbols and evidence the task needs.
Use the current pricing table for available rates and account groups. Model IDs and prices come from TokenHot’s catalog; provider pricing and subscriptions are separate.
API
Code examples
curl --location --request POST 'https://api.tokenhot.ai/v1/chat/completions' \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/json' \
--data-raw '{
"model": "glm-5.3",
"messages": [
{
"role": "user",
"content": "What model are you"
}
]
}'FAQ
Frequently asked questions
What is GLM 5.3 best suited for?
GLM 5.3 is suited to the input, output, and capability types listed on this page. Test production workloads before choosing it for a critical system.
How is GLM 5.3 priced?
Pricing is shown in the pricing section above. Actual cost depends on usage volume, account group, and the request payload.
How can I call GLM 5.3?
Use the model ID shown above with one of the supported API protocols. Code examples are provided when usage data is available.
Can GLM 5.3 analyze images?
The current catalog lists text input only. Use a model with image input for screenshots, diagrams or visual evidence.
Does always-on reasoning mean max effort?
No. The catalog lists low, high and max effort options. Choose and record the selected level for each evaluation.
Can it operate a terminal?
Only through tools your application exposes and authorizes. Require actual command results before claiming execution.
What should a code prompt contain?
Include the failing behavior, relevant files, constraints, tests and the expected acceptance condition. Ask for assumptions and unknowns explicitly.
Get started
Build with GLM 5.3
API