Z.ai
GLM-5.3 FlashOpen
High-speed, low-cost GLM 5.3 Flash model by Z.ai with 180+ tokens/sec throughput.
Intelligence
64.2 #282
AAQI 58.0
Output Speed
182 t/s
TTFT 220 ms · ITL 5.5 ms/tok
Blended price (3:1)
$0.15 /1M
In $0.1/M · Out $0.3/M
Context
262K
≈ 655 A4 pages · max out 8,192
Quality Domain Radar
6-Axis Frontier Evaluation vs Category Average & Flagship
GLM-5.3 Flash
Claude Mythos Preview (#1)
Average (68)
Radar axes are approximated from category scores where direct benchmark measurements are missing — see Methodology.
Strengths & Trade-offs
Where it wins
- Open weights — self-host, fine-tune and audit with no vendor lock-in
- 182 tok/s output — fast enough for real-time agents
- Sub-400ms time-to-first-token
- Very low blended price — $0.15/1M mixed tokens
Killer Use Cases
Real-time agents
Low-latency loops for voice, gaming and interactive copilots.
High-volume batch jobs
Classification, extraction and summarization at minimal cost per call.
SWE-bench agents
Issue-to-PR resolution across multi-file repos — refactor, test and verify autonomously.
Developer Quickstart & API
Set
ZAI_API_KEY first — get an API keycurl https://api.z.ai/api/paas/v4/chat/completions \
-H "Authorization: Bearer $ZAI_API_KEY" \
-H "content-type: application/json" \
-d '{
"model": "glm-5-3-flash",
"messages": [{"role": "user", "content": "Refactor this function for O(n) time and explain the change."}]
}'import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.z.ai/api/paas/v4",
api_key=os.environ["ZAI_API_KEY"],
)
resp = client.chat.completions.create(
model="glm-5-3-flash",
messages=[{"role": "user", "content": "Refactor this function for O(n) time and explain the change."}],
)
print(resp.choices[0].message.content)const res = await fetch("https://api.z.ai/api/paas/v4/chat/completions", {
method: "POST",
headers: {
Authorization: `Bearer ${process.env.ZAI_API_KEY}`,
"content-type": "application/json",
},
body: JSON.stringify({
model: "glm-5-3-flash",
messages: [{ role: "user", content: "Refactor this function for O(n) time and explain the change." }],
}),
});
console.log((await res.json()).choices[0].message.content);Official Resources & Links
API deployment
Compare hosts & latency
Throughput, TTFT and cache pricing across Groq, Cerebras, Together, DeepInfra, Fireworks, Bedrock and Azure.
GroqCerebrasTogether AIDeepInfraFireworks AIAWS BedrockAzure AI
Scores by Source
Category Scores
Benchmark Results
Live Specs & Architecture
Under the hood
- Prompt caching supported — cached input reads at $0.02/1M tokens.
Release date
Aug 1, 2026
Parameters
Not disclosed
License
Open Weights
Context Window
262,000 tokens
Max Output
8,192 tokens
Modalities
text + image
Cache Pricing
read $0.02 · write $0.15 /1M
Reasoning
—
Input Price
$0.1/M
Output Price
$0.3/M
Output Speed
182 t/s
Time to First Token
220 ms
Data Updated
Aug 28, 2026
Estimated monthly bill
Estimated monthly spend
$0.00
Calculating savings vs GPT-4o...
Cite & embed
GitHub Markdown badge
Loading badge markdown...BibTeX
@misc{llmpodium2026glm53flash,
title = {Benchmark Analysis and Intelligence Evaluation of GLM-5.3 Flash},
author = {LLMPodium Research},
year = {2026},
url = {https://llmpodium.com/models/glm-5-3-flash}
}Embed Score Badge
Add a live Podium Score badge to your GitHub or Hugging Face README — it links back to the live ranking.
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