GLM-5.3 for the work behind the answer
Bring GLM-5.3 a difficult code path, a collection of source documents or a task that needs tools. Give it the evidence and define what a useful answer must contain. $1.12 / 1M input and $3.52 / 1M output.
- 1M-token context
- Always-on reasoning
- Text input
- Function calling
- Streaming
Try GLM-5.3
Try a focused task with GLM-5.3. Choose your request format, inspect its fields and compare the answer with the evidence you supplied.
glm-5.3Per 1M tokens: $1.12 input · $3.52 output.Blank fields are omitted. Use JSON for nested values.
Estimated from returned usage and live rates. A dash means a required rate or usage is unavailable.
GLM-5.3 in practice
GLM-5.3 reviews a payment retry: the crash window, the external charge and a stable idempotency key.
Review this Python payment handler: def pay(order): charge(order.card, order.total); db.mark_paid(order.id). A worker may retry after crashing between charge and mark_paid. Provide a concise failure timeline, a minimal idempotent design using a stable order key, and three executable-style regression cases. Do not claim a database transaction alone makes the external charge exactly-once.
Nothing ties the two steps together. charge is an external, non-transactional effect; the DB update is a separate operation. A crash (or worker timeout, or process kill, or deployment) between them leaves the order charged but unpaid — a retried worker charges again.
Note: wrapping both lines in a DB transaction doesn't fix it. The charge is external; a rollback at t2 cannot "un-charge" the card, and retry still re-executes the charge. Exactly-once for an external effect requires idempotency, not atomicity.
The key must be stable across attempts and derived from the order, never per-attempt or random, or both dedupe layers are useless.
Recorded from GLM-5.3 on October 29, 2026. Selected original English excerpts; other sections are omitted. Review generated designs and code before using them.
GLM-5.3 pricing
GLM-5.3 pricing follows the tokens used by your request. The table reads current input, output and cache rates from the billing settings.
Cached input is part of total input. Reasoning is part of output. Example token counts illustrate the arithmetic; your returned usage determines the charge.
- Input
- $1.12Price · 1M tokens
- Output
- $3.52Price · 1M tokens
- Cached input
- $0.208Price · 1M tokens
- Cache write
- $1.12Price · 1M tokens
What is GLM-5.3?
GLM-5.3 is a Z.ai text reasoning model for coding and tasks that need a large working context. Its million-token window gives you space for source files, specifications and a continuing conversation. Select relevant material and ask for references to the evidence.
GLM-5.3 always reasons. Choose low, high or max effort, with max as the default. Both reasoning and the visible answer consume the output budget, which can reach 131,072 tokens. Start with a bounded task and enough room to finish, then compare the result against an explicit acceptance check.
Model: GLM-5.3 · Input: text · Output: text and tool calls · Formats: Chat Completions, Responses and Messages.
Use GLM-5.3 from your application or coding assistant
Keep a familiar request format while testing your own workload.
Connect GLM-5.3
Connect GLM-5.3 using the format your application expects. The documentation includes complete requests, protocol-specific parameter tables and examples of how to preserve a tool conversation.
- 1Create an API key.
- 2Copy a request from the docs.
- 3Check the result with your own data.
Build with GLM-5.3
Ask your coding assistant to connect GLM-5.3 with an environment variable for the key. Specify the expected result and approve a spending limit before running it.
- 1Copy the setup instructions.
- 2Keep your key in an environment variable.
- 3Review before running.
What GLM-5.3 can help you do
A useful GLM-5.3 prompt contains a concrete job, enough evidence to do it and a result you can check.
Trace a failure across files
Give GLM-5.3 the handler, the relevant types and the failing input. Ask it to explain where the assumption breaks and propose a minimal change, with a regression case that would have caught the failure.
Compare a long source set
Use GLM-5.3 to compare requirements across policies, notes or specifications. Require a supporting passage for each conclusion and an explicit list of unanswered questions before turning the analysis into a decision.
Extract useful JSON
Ask GLM-5.3 for a JSON object with fields defined in your instructions. State how missing values should appear. Validate the result in your application; JSON object mode does not enforce a supplied JSON Schema.
Build a tool conversation
Let GLM-5.3 propose a function call, then validate the arguments and run your function. Return its result with the original call identifier. The model can reason over that result while your application controls execution.
Practical uses for GLM-5.3
Find a duplicate-action bug
Show GLM-5.3 the code that performs an action and records completion. Ask which crash points can repeat the action, what makes the operation idempotent and which tests demonstrate the fix.
Turn a specification into checks
Give GLM-5.3 a written contract and ask for test cases at its boundaries. Separate required behavior from assumptions, then review each case against the original requirement.
Plan a small migration
Provide GLM-5.3 the current interface, desired interface and compatibility constraints. Request a sequence of changes with a verification step and rollback condition for each stage.
Start with GLM-5.3
- 01
Add credit
Create an account and add the amount you need. Your credit does not expire.
- 02
Choose a format
Use glm-5.3 as the model ID. Copy a Chat Completions, Responses or Messages example from the documentation; each format has its own field names.
- 03
Try a small task
Give GLM-5.3 a question with an answer you can check. Set an output budget that allows for reasoning.
- 04
Inspect and continue
Review the GLM-5.3 output and usage. Preserve the complete assistant and tool history when continuing the conversation in your own application.
GLM-5.3 capabilities at a glance
Match the format and controls to the task before you integrate.
- Context window
- 1,048,576 tokens
- Reasoning effort
- low, high or max; always enabled
- Structured output
- JSON object mode in Chat and Responses
- Input modality
- Text only; no image, audio or video input
- Conversation history
- Keep reasoning, tool identifiers and results in order
These are documented model capabilities. Evaluate accuracy and response time on your own workload.
Plan a successful GLM-5.3 request
Allow room for reasoning
GLM-5.3 includes thinking in its output limit. Use a reasonable budget for the task and inspect the completion status. Reaching the token limit is different from receiving a complete answer.
Supply text evidence
GLM-5.3 accepts text, not screenshots or video. Extract the relevant text before requesting a review. Include file names or section labels so the answer can point back to a specific source.
Read actual cache usage
GLM-5.3 uses automatic context caching. Stable reusable prefixes can help, but a matching prompt does not guarantee a hit. Use the cached-token count in the response when calculating the cost.
Verify important conclusions
Ask GLM-5.3 for source passages or executable checks. Review generated code, extracted amounts and factual claims before using them to make an external change.
GLM-5.3 on SeedRouter
See the rates here
Compare GLM-5.3 input, output and cache rates before you send a request. The pricing table updates from current billing settings.
Pay for usage
Use GLM-5.3 from a prepaid balance. There is no monthly subscription, and failed requests are not charged.
Preserve the complete history
For GLM-5.3 Chat requests, clear_thinking=false preserves historical reasoning. Keep those reasoning fields and tool messages unchanged and in order; copying only the visible answer can remove information needed for the next turn.
Prefer a concrete output contract
Define field names, types and missing-value behavior in your prompt. Ask GLM-5.3 for JSON object output, parse it and validate it before saving. Handle validation failures explicitly instead of assuming that valid JSON is correct data.
Compare GLM-5.3 with other models
See live prices and task-specific controls before choosing.
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View modelTry Claude Haiku 5.5 for classification, extraction and summaries. See live token prices, test your prompts, and connect your app with the Messages API.
View modelGLM-5.3 guides
Understand token billing, cache behavior and practical integration choices before scaling your workload.
GLM-5.3 questions
What is GLM-5.3?+
GLM-5.3 is a Z.ai reasoning model for coding, long text analysis and tool conversations. It accepts text and returns text or tool calls. It is distinct from GLM-5.3-Flash.
Is GLM-5.3 free?+
GLM-5.3 requests on this service use prepaid credit at the live rates above. There is no subscription requirement. Failed requests are not charged.
Can GLM-5.3 turn thinking off?+
No. GLM-5.3 always reasons. Choose low, high or max effort; max is the default. Reasoning tokens count as output even when the final answer is short.
How much context can GLM-5.3 use?+
GLM-5.3 provides a million-token context window, with 1,048,576 tokens listed in the official client metadata. Its maximum output is 131,072 tokens. Actual input usage, not the maximum window size, determines the input charge.
Which request formats does GLM-5.3 support?+
GLM-5.3 documents Chat Completions, Responses and Anthropic-compatible Messages access. Their parameters differ. The API reference separates the published GLM contract from compatibility fields that need workload-specific verification.
How much does GLM-5.3 cost?+
GLM-5.3 currently starts at $1.12 / 1M input and $3.52 / 1M output. The table also shows cached input and any separately reported cache writes. Use actual token usage rather than the visible answer length to estimate a request.
Try GLM-5.3 on your own task
Give GLM-5.3 a question you can verify. Review the answer and usage.