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GLM 5.3 is Z.ai's full-size long-context chat model behind an OpenAI-compatible call. It reads up to 1,048,576 tokens, replies up to 131,072, with reasoning effort up to max, 128 tool schemas per request and JSON output.

Input $1.05Output $3.30per 1 million tokens25% below the publisher's list priceCached input $0.195Z.ai official priceInput $1.40Output $4.40

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GLM 5.3

What can GLM 5.3 help with?

Related models and next tasks

Missing the model you need?

Tell us what to add and where you would use it. Requests go to the people who decide what gets onboarded next.

Build stable message flows for long reasoning with a 131,072-token reply ceiling

GLM 5.3 reads up to 1,048,576 tokens and returns up to 131,072 — the highest reply ceiling in this catalogue — with a reasoning_effort dial that goes low, high or max. Its Flash sibling has the same shape at a much lower rate, which makes the comparison between them the first thing worth running.

Open the chat playground
Build stable message flows for long reasoning with a 131,072-token reply ceiling
Model
GLM 5.3
Good for
long reasoning with a 131,072-token reply ceiling
Message shape
System rules → evidence and state → current task → output contract
Fact boundary
Capabilities, controls, and price come from the live schema

Integration principle

Validate with the smallest message set, then add necessary context. Separate facts, assumptions, and constraints, and design interruption, retry, settlement, and review paths.

Build repeatable workflows for long reasoning with a 131,072-token reply ceiling

GLM 5.3 reads up to 1,048,576 tokens and returns up to 131,072 — the highest reply ceiling in this catalogue — with a reasoning_effort dial that goes low, high or max. Its Flash sibling has the same shape at a much lower rate, which makes the comparison between them the first thing worth running.

  • The highest reply ceiling here

    131,072 tokens in one turn

    More room in a single reply than any other route in this catalogue, including the frontier ones.

  • A max effort level

    low, high or max, default max

    One step beyond the usual high. The default is max, so a call inherits the most expensive setting unless you say otherwise.

  • Reasoning is billed as output

    Effort has a price per call

    At max on every request, the deliberation cost is paid even on turns that never needed it.

Where GLM 5.3 wins on task cost and interaction shape

Test one real task for quality, verifiability, latency, and sale-price fit instead of relying on a general leaderboard.

  1. Choose GLM 5.3

    Hard cases, long answers

    When the reply itself is long and the reasoning has to hold together across it.

  2. Choose 5.3 Flash

    Same shape, a fraction of the rate

    Identical window, ceiling and dial. Run both on your own difficult cases before assuming the rate difference reflects the quality difference for your task.

  3. Compare a frontier route

    The decision carries consequences

    On work where a wrong answer is expensive, judge on your own evidence pack rather than on any single vendor's framing.

Turn GLM 5.3 into concrete work, not generic labels

Every scenario needs input boundaries, verification, and recovery; official capabilities, benchmarks, and tool support are never invented.

  • Reasoning that must survive its own length

    Long analytical writing

    Reasoning that must survive its own length

    A 131,072-token ceiling lets the argument finish rather than being cut where the budget ran out.

  • A million tokens in, a full report out

    Whole-codebase review

    A million tokens in, a full report out

    Both ends are large here, which is unusual and is what this route is for.

  • JSON over long inputs

    Structured extraction at depth

    JSON over long inputs

    response_format keeps a long answer machine-readable when the input is a document set rather than a paragraph.

  • Adult characters, clear boundaries

    Unrestricted narrative work

    Adult characters, clear boundaries

    Long arcs where continuity matters more than turn latency. We add no content filter; what the model itself declines is its own behaviour.

Organize context into an inspectable GLM 5.3 input

Objective, evidence, state, constraints, and output contract need distinct jobs so requests can be compressed, debugged, and reproduced.

  1. Effort

    max is the default, not the recommendation

    Set it per call. low is enough for a large share of traffic, and the difference shows up directly on the bill.

  2. Structure

    Ask for sections when the reply is long

    A 131,072-token answer without a requested structure is hard to review and harder to diff between runs.

  3. Caching

    Stable prefix, variable tail

    Cached input is rated well below fresh input; on million-token calls that is the saving worth designing for.

  4. Output

    Name the format before the length

    response_format plus a section list is what keeps a very long reply usable.

Prove a repeatable GLM 5.3 case before product integration

Confirm live schema, quote, streaming, and error behavior before reusing the normalized request.

  1. 01 · Baseline

    Validate the smallest request in Playground

    Check message layers, output shape, streaming, and current controls.

  2. 02 · Boundaries

    Exercise failure, cancellation, and retry

    Verify timeouts, interrupted streams, oversized input, regeneration, and duplicate submission.

  3. 03 · Integrate

    Copy the normalized API request

    Use the public model id and messages contract; read model controls from the live schema.

  4. 04 · Operate

    Meter usage and keep evaluating

    Store state, final usage, failure reasons, and quality feedback; quote expensive calls first.

Bring GLM 5.3 into a clear API contract

Playground, API examples, request states, and usage records stay aligned from evaluation through production integration.

  • Unified contract

    One shape for messages, streams, and errors

    Keep request structure, state handling, and error recovery consistent when changing models.

  • Live schema

    Use the current endpoint controls

    Playground and API examples expose available fields, limits, and default behavior together.

  • Observable lifecycle

    Every request has a clear state

    Submission, streaming, failure, cancellation, and usage use inspectable public semantics.

  • Portable integration

    Reduce work when models evolve

    A clear contract keeps product code focused on inputs, results, and user experience.

GLM 5.3 API boundaries to confirm before integration