GLM-5.3-Flash (glm-5.3-flash) Model Details

Overview

Multimodal1M ContextVisionCode generationAgentic tasks

GLM-5.3-Flash is the first natively multimodal model in the GLM-5 series. Built on a 320B-A18B hybrid of sparse and linear attention, it is the world's first open frontier model to adopt that architecture. It scores 57 on the Artificial Analysis Intelligence Index, matching Claude Opus 4.8 and surpassing GLM-5.2 across the board. Vision is woven natively into the coding loop, so the model watches interfaces, rendered output and interaction feedback and keeps improving, working across code, browser and GUI to finish a task. It also reaches into Office work, financial research and professional documents, decomposing goals on its own, calling tools and delivering finished PPTX, PDF, DOCX and XLSX files. At one tenth the price of GLM-5.3, frontier intelligence no longer has to be rationed.

Radically cheaper architecture

A sparse-plus-linear attention hybrid cuts attention compute by 3.01× and KV cache by 4.44× versus GLM-5.3.

Natively multimodal visual coding

Vision is built into the coding loop: the model watches the interface, the rendered result and the interaction feedback, then keeps improving.

A work partner beyond coding

Extends to Office work, financial research and professional documents, delivering finished PPTX / PDF / DOCX / XLSX files.

Million-token context

A 1M-token context window with 128K max output loads project-scale codebases and very long documents in a single pass.

Features

VisionImages / video / files✓

Reasoninglow / high / max, max recommended; always on and cannot be disabled✓

Streaming✓

Tool callingIntegrates with a range of external tools✓

Context cachingImproves performance on long conversations✓

Structured outputJSON and other formats✓

Web search✕

Batch✕

Prefix completion✕

Fine-tuning✕

Pricing

Input$0.15/M tokens

Input (cache hit)$0.03/M tokens

Output$0.5/M tokens

List prices are one tenth of GLM-5.3, matching the vendor's official pricing. A limited-time 50% discount runs until 2026-09-09 24:00 (input ¥0.4 / cached input ¥0.115 / output ¥1.4). Final billing follows the console and actual token usage.

Rate Limits & Context

Context Window1M

Max Output128K

RPM (requests/min)200

TPM (tokens/min)3M

API Reference

POSThttps://api.tokenfab.com/v1/chat/completions

Request Example

curl https://api.tokenfab.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $TOKENFAB_API_KEY" \
  -d '{
    "model": "glm-5.3-flash",
    "thinking": { "type": "enabled", "clear_thinking": false },
    "reasoning_effort": "max",
    "temperature": 1,
    "top_p": 0.95,
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": [
        {"type": "text", "text": "Analyze this UI screenshot, point out layout and interaction issues, and suggest improvements."},
        {"type": "image_url", "image_url": {"url": "https://cdn.example.com/ui.png"}}
      ]}
    ]
  }'

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("TOKENFAB_API_KEY"),
    base_url="https://api.tokenfab.com/v1"
)

response = client.chat.completions.create(
    model="glm-5.3-flash",
    thinking={"type": "enabled", "clear_thinking": False},
    reasoning_effort="max",
    temperature=1,
    top_p=0.95,
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": [
            {"type": "text", "text": "Analyze this UI screenshot, point out layout and interaction issues, and suggest improvements."},
            {"type": "image_url", "image_url": {"url": "https://cdn.example.com/ui.png"}}
        ]}
    ]
)

print(response.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.TOKENFAB_API_KEY,
  baseURL: "https://api.tokenfab.com/v1"
});

const response = await client.chat.completions.create({
  model: "glm-5.3-flash",
  thinking: { type: "enabled", clear_thinking: false },
  reasoning_effort: "max",
  temperature: 1,
  top_p: 0.95,
  messages: [
    { role: "system", content: "You are a helpful assistant." },
    { role: "user", content: [
      { type: "text", text: "Analyze this UI screenshot, point out layout and interaction issues, and suggest improvements." },
      { type: "image_url", image_url: { url: "https://cdn.example.com/ui.png" } }
    ]}
  ]
});

console.log(response.choices[0].message.content);

Response Example

{
  "id": "chatcmpl-glm53f-8e3j9a2",
  "object": "chat.completion",
  "created": 1784950000,
  "model": "glm-5.3-flash",
  "choices": [{
    "index": 0,
    "message": {
      "role": "assistant",
      "content": "The UI has three main issues: 1) the top navigation bar is misaligned with the content area, with uneven spacing; 2) the primary button's color contrast is too low, weakening the call to action; 3) form validation messages appear in inconsistent positions. Use a consistent grid, give the primary button more visual weight, and place all validation messages below their input fields."
    },
    "finish_reason": "stop"
  }],
  "usage": {
    "prompt_tokens": 512,
    "completion_tokens": 186,
    "total_tokens": 698
  }
}

Request Parameters

Parameter	Type	Required	Description
`model`	string	✓	Model ID; use glm-5.3-flash
`messages`	array	✓	List of chat messages; content may be a plain string or multimodal blocks containing image_url
`thinking`	object	—	Thinking config, {"type":"enabled"} (enabling only — disabling is not supported); clear_thinking:false is recommended
`reasoning_effort`	enum	—	Reasoning effort: low / high / max; max recommended
`temperature`	float	—	Sampling temperature, 0–2; 1 recommended
`top_p`	float	—	Nucleus sampling, 0–1; 0.95 recommended
`max_tokens`	int	—	Maximum output tokens
`stream`	bool	—	Whether to stream the response, default false; enabling tool_stream alongside it is recommended
`tools`	array	—	Tool definitions for function calling
`tool_choice`	string	—	Tool choice policy: auto / none / a named function