GLM 5.3 FlashX overview
GLM 5.3 FlashX by Zhipu AI
GLM-5.3 FlashX is the high-speed variant of Zhipu AI GLM-5.3 Flash, a native multimodal model reaching up to 200 tokens/s on the same hybrid sparse and linear attention architecture, with a 1M-token context window and text, image and video understanding.
GLM 5.3 FlashX specs
- Model ID
z-ai/glm-5.3-flashx- Developer
- Zhipu AI
- Context window
- 1,048,576 tokens
- Max output
- 131,072 tokens
- Input
- Image, Text
- Output
- Text
- API
- OpenAI-compatible, base URL
https://api.aimlapi.com/v1
What Is GLM 5.3 FlashX API?
GLM-5.3 FlashX is the high-speed variant of Zhipu AI's GLM-5.3 Flash. It keeps the same hybrid sparse-plus-linear attention architecture and the same 1,048,576-token context window, but is tuned for throughput, Zhipu reports up to 200 tokens per second.
It sits in the GLM-5.3 family alongside GLM-5.3 and GLM-5.3-Flash, trading some of the flagship's depth for speed and a lower price.
Technical Reference
- Aliases: glm-5.3-flashx, zhipu/glm-5.3-flashx
- Release date: September 18, 2026
- Context window: 1,048,576 tokens
- Max output tokens: 131,072 tokens
- Modalities: Image, Text → Text
- Capabilities: reasoning, parallel tool calls, structured output, streaming, vision, file input
- Endpoint: POST /v1/chat/completions
What GLM 5.3 FlashX Brings to the Table
Throughput first
Up to 200 tokens per second, vendor-reported, on the same architecture as GLM-5.3 Flash. Built for paths where latency is the constraint.
A million tokens of context
1,048,576 tokens in, up to 131,072 back, enough for whole repositories or long transcripts without chunking.
Agentic plumbing
Parallel tool calling, structured (JSON schema) output and streaming, which is what agent loops need to plan, call tools and iterate.
Where GLM 5.3 FlashX Makes the Most Sense
Latency-bound chat
Interactive assistants where time-to-answer matters more than the last increment of quality.
High-volume pipelines
Classification, extraction and summarisation run continuously rather than occasionally.
Long-context work
The 1M window removes most of the need for retrieval scaffolding.
Agents with tools
Parallel tool calling and structured output for multi-step workflows.
Use cases
- Latency-sensitive chat and assistants
- High-volume classification, extraction and summarisation
- Long-context analysis over 1M-token inputs
- Agentic workflows with parallel tool calling and structured output
- Document and screenshot understanding via vision and file input
Capabilities
- Reasoning
- Streaming
- Structured outputs
- Function calling
- Vision
- File input
GLM 5.3 FlashX API pricing
| Usage | Price | Unit |
|---|---|---|
| Input | $0.37 | 1M tokens |
| Output | $1.25 | 1M tokens |
| Cached input | $0.075 | 1M tokens |
Prices from the AI/ML API price list, checked on Nov 4, 2026.
GLM 5.3 FlashX vs other models: token prices
AI/ML API input and output rates for 5 models, per million tokens.
| Model | Input | Output |
|---|---|---|
 GLM 5.3 FlashX | $0.37 | $1.25 |
| GLM-5.3-Flash | $0.15 | $0.5 |
| GLM-5.3 | $1.4 | $4.4 |
| GLM 5.2 | $1.4 | $4.4 |
| GLM 5.2 Fast Preview | $3.5 | $11 |
How to use the GLM 5.3 FlashX API
Build with your agent
Give your agent the model details, code and documentation.
1Get your API key
Create API keyexport AIMLAPI_KEY="8f2b4c7d9a1e6b3f…"2Make your first request
curl 'https://api.aimlapi.com/v1/chat/completions' \
-H "Authorization: Bearer $AIMLAPI_KEY" \
-H 'Content-Type: application/json' \
-d '{
"model": "z-ai/glm-5.3-flashx",
"messages": [
{
"role": "user",
"content": "Write a haiku about the ocean."
}
]
}'import os
import requests
headers = {
"Authorization": f"Bearer {os.environ['AIMLAPI_KEY']}",
"Content-Type": "application/json",
}
payload = {
"model": "z-ai/glm-5.3-flashx",
"messages": [
{
"role": "user",
"content": "Write a haiku about the ocean."
}
]
}
response = requests.post("https://api.aimlapi.com/v1/chat/completions", headers=headers, json=payload, timeout=120)
response.raise_for_status()
print(response.json())const response = await fetch("https://api.aimlapi.com/v1/chat/completions", {
method: "POST",
headers: {
Authorization: `Bearer ${process.env.AIMLAPI_KEY}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"model": "z-ai/glm-5.3-flashx",
"messages": [
{
"role": "user",
"content": "Write a haiku about the ocean."
}
]
}),
});
if (!response.ok) throw new Error(`HTTP ${response.status}`);
console.log(await response.json());Models similar to GLM 5.3 FlashX
GLM 5.3 FlashX FAQ
What is the context window of GLM 5.3 FlashX?
GLM 5.3 FlashX has a 1,048,576 token context window and can return up to 131,072 tokens.
How is GLM 5.3 FlashX different from GLM-5.3 Flash?
FlashX is the high-speed variant. It runs on the same hybrid sparse-plus-linear attention architecture and the same 1M-token context window as GLM-5.3 Flash, but is tuned for throughput, Zhipu reports up to 200 tokens per second.
How much does GLM 5.3 FlashX cost?
GLM 5.3 FlashX costs $0.37 input and $1.25 output per 1M tokens on AI/ML API, billed as you go with no subscription.
What inputs and outputs does GLM 5.3 FlashX support?
It accepts images and text as input and returns text. File input is supported as well.
How fast is GLM 5.3 FlashX?
Zhipu AI reports up to 200 tokens per second. That figure is vendor-reported and has not been independently reproduced.
Who created GLM 5.3 FlashX?
GLM 5.3 FlashX was built by Zhipu AI as part of the GLM-5.3 family, alongside GLM-5.3 and GLM-5.3 Flash. It became available on September 18, 2026.
How do I call GLM 5.3 FlashX through AI/ML API?
It is served on the OpenAI-compatible chat completions endpoint. Point any OpenAI SDK at https://api.aimlapi.com/v1 and set the model to z-ai/glm-5.3-flashx. The aliases glm-5.3-flashx and zhipu/glm-5.3-flashx also resolve to it.
What is the GLM 5.3 FlashX model ID?
Use z-ai/glm-5.3-flashx as the model parameter. Requests go to the OpenAI-compatible endpoint with your AI/ML API key.