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GLM-5.3-Flash

Z.ai · OpenAI-compatible API

z-ai/glm-5.3-flash

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USD / 1M tokens

Input$0.08

Cache Read$0.023

Cache Write—

Output$0.28

Context: 1M
Max Output: 128K
Tool Calling: ✓
Reasoning: ✓
Vision: ✓
Streaming: ✓

OpenAI-compatible API

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.iteracompute.com/v1",
    api_key=os.environ["ITERACOMPUTE_API_KEY"],
)
response = client.chat.completions.create(
    model="z-ai/glm-5.3-flash",
    messages=[{"role": "user", "content": "Hello!"}],
)
print(response.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api.iteracompute.com/v1",
  apiKey: process.env.ITERACOMPUTE_API_KEY,
});
const response = await client.chat.completions.create({
  model: "z-ai/glm-5.3-flash",
  messages: [{ role: "user", content: "Hello!" }],
});
console.log(response.choices[0].message.content);

curl https://api.iteracompute.com/v1/chat/completions \
  -H "Authorization: Bearer $ITERACOMPUTE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "z-ai/glm-5.3-flash",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

z-ai/glm-5.3-flashView Docs →

Catalog updated: 2026-10-19.

Overview

Text and image input, streaming, reasoning, tool calling and structured outputs; up to 131,072 output tokens.

Tasks and selection tradeoffs

Batch classification

Classify support tickets into an explicit set of labels and check a held-out sample.

Screenshot triage experiments

Evaluate screenshot descriptions against a manually labeled set before automating triage.

Task suggestions based on catalog features and price, not model quality benchmarks.

Choose your tool

OpenAI SDKCall from Python or JavaScript Vercel AI SDKBuild a TypeScript application

Choose a model and tool for your task →

Pricing

Prices in USD per 1M tokens.

Input$0.08

Cache Read$0.023

Cache Write—

Output$0.28

Example task: Classify a batch of support tickets

Assume 12,000 aggregate input tokens and 1,000 billable output tokens across all calls. No cache hit.

12,000 × $0.08 / 1M + 1,000 × $0.28 / 1M = $0.001240

If usage actually reports 9,000 of those input tokens as cached, replace that portion at the cached-input rate ($0.023/1M): $0.000727. Cached tokens are part of total input, never added on top.

Illustrative calculation using the 2026-10-19 catalog snapshot. Include every agent call and retry, repeated history and billable reasoning output; settle against actual usage and current Console prices.

Estimate your monthly cost

Uses the 2026-10-19 catalog snapshot, independently of the live prices above. USD per 1M tokens; an estimate, not a bill.

Estimate includes standard text input, cache reads and text output only. Long-context premiums, service tiers, cache writes, image output and tool fees are excluded.

Model

Input tokens / request

Billable output tokens / request

Cached share of input (%)

Requests / month

Input includes cached tokens; output includes billable reasoning. Count every API request, including agent steps and retries.

Estimated monthly cost

—USD / month

GLM-5.3-Flash

Uncached input: —
Cache read: —
Billable output: —

Connect this model →

Calculation and billing basis

Monthly cost = requests × [input × (1 − cached share) × input rate + input × cached share × cache-read rate + output × output rate] / 1,000,000.

Cached tokens are part of total input, never added on top. Use actual cache-read hits from usage; repeating a prompt does not guarantee a hit. Missing rates are unconfirmed, not $0. Separate cache-write, storage and other additional fees are excluded. Settle against actual usage and current Console prices.

Enable JavaScript to use the calculator. The worked example above remains available.

Usage limits

The snapshot has lower input/output prices than GLM-5.3, and lists image input. Flash is not a measured latency promise; compare correctness and response time on the same sample.
See the current context and maximum output in the model specifications above. Input and requested output share the context budget.
The context and capability flags describe the public catalog. They do not establish a particular tool workflow or a quality score.
Start with one representative task. Check correctness, actual usage and response time before increasing traffic.
Use the integration examples to connect your application and handle tool results.

API parameters

Parameter / protocol field	Usage
`model / messages`	Use the exact model ID and chat message list
`tools / tool_calls / tool messages`	Use tools to define functions and role=tool to return their results
`stream / stream_options.include_usage`	Use SSE streaming with final usage
`max_tokens`	Set an output budget within the model and account limits

Parameters vary by model. Keep model-specific controls separate from shared OpenAI-compatible fields.

API

Base URL: https://api.iteracompute.com/v1
Endpoint: POST /v1/chat/completions
Model ID: z-ai/glm-5.3-flash
Authentication: Authorization: Bearer ITERACOMPUTE_API_KEY

View Docs

FAQ

How do I use GLM-5.3-Flash with the OpenAI SDK?

Set base_url to https://api.iteracompute.com/v1 and model to z-ai/glm-5.3-flash. Authenticate with your IteraCompute API key.

What does GLM-5.3-Flash cost?

See the pricing section for current input, cached-input and output rates in USD per 1M tokens. Check the refresh time before use.

What context length is available?

See the model specifications above for the current context and maximum output. Input and requested maximum output share the context budget.

Ready for your first request?

Create your key, choose a tool and get a non-empty first reply.

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