Z.AI: GLM 5.3 Flash
z-ai/glm-5.3-flash
Access GLM 5.3 Flash from Z.AI using Puter.js AI API.
Get Started// npm install @heyputer/puter.js
import { puter } from '@heyputer/puter.js';
puter.ai.chat("Explain quantum computing in simple terms", {
model: "z-ai/glm-5.3-flash"
}).then(response => {
document.body.innerHTML = response.message.content;
});
<html>
<body>
<script src="https://js.puter.com/v2/"></script>
<script>
puter.ai.chat("Explain quantum computing in simple terms", {
model: "z-ai/glm-5.3-flash"
}).then(response => {
document.body.innerHTML = response.message.content;
});
</script>
</body>
</html>
# pip install openai
from openai import OpenAI
client = OpenAI(
base_url="https://api.puter.com/puterai/openai/v1/",
api_key="YOUR_PUTER_AUTH_TOKEN",
)
response = client.chat.completions.create(
model="z-ai/glm-5.3-flash",
messages=[
{"role": "user", "content": "Explain quantum computing in simple terms"}
],
)
print(response.choices[0].message.content)
curl https://api.puter.com/puterai/openai/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_PUTER_AUTH_TOKEN" \
-d '{
"model": "z-ai/glm-5.3-flash",
"messages": [
{"role": "user", "content": "Explain quantum computing in simple terms"}
]
}'
Model Card
GLM 5.3 Flash is a mixture-of-experts model from Z.ai (Zhipu AI), the first natively multimodal release in the GLM-5 family, accepting text and image input. It has 320 billion total parameters with 18 billion active per token, and a hybrid sparse-and-linear attention architecture that Z.ai says cuts attention compute about 3x and KV cache size about 4.4x compared with GLM-5.3.
Through this API it offers a 1,310,720 token context window and up to 131,072 output tokens, priced at $0.08 per million input tokens and $0.25 per million output tokens. It supports function calling, and thinking is always on and cannot be disabled.
GLM 5.3 Flash beats GLM-5.2 on Z.ai's reported benchmarks (DeepSWE v1.1, 63.4 vs 46.2; AutomationBench, 48.8 vs 26.2) and approaches Claude Opus 4.8 on coding and agentic tasks. Model weights are released under the MIT license.
Context Window 1M
tokens
Max Output 131K
tokens
Input Cost $0.08
per million tokens
Output Cost $0.25
per million tokens
Input text, image
modalities
Tool Use Yes
Release Date Aug 26, 2026
Output Speed 42
tokens / sec
Latency 1.16s
time to first token
Model Playground
Try GLM 5.3 Flash instantly in your browser.
This playground uses the Puter.js AI API — no API keys or setup required.
Benchmarks
How GLM 5.3 Flash performs on standard evaluations.
| Benchmark | Score |
|---|---|
| GPQA Diamond Graduate-level science Q&A | 91.2% |
| Humanity's Last Exam Cross-domain reasoning | 39.9% |
| SciCode Scientific programming | 46.1% |
| LCR Long-context reasoning | 78.0% |
Scores sourced from Artificial Analysis.
Find other Z.AI models →
GLM 5.3
GLM 5.3 is Z.ai's latest open-weight foundation model, a post-training refresh of GLM-5.2 released August 14, 2026. It keeps the same 744B-parameter Mixture-of-Experts architecture (40B active) and 1M-token context window; all reported gains come from expanded reinforcement-learning post-training rather than a new base model. On Z.ai's internal Code Bench, GLM 5.3 scores 50% higher than GLM-5.2. It also improves on Terminal-Bench 3.0 (4.6 to 28.3) and DeepSWE v1.1 (46.2 to 66.9). Cybersecurity ability grew alongside coding: CyberGym rose from 77.2% to 84.5%, and ExploitBench more than doubled, from 24.4% to 54.4%. The model supports three reasoning-effort levels (low, high, max) via the API, and thinking can no longer be disabled. It is best suited to long-horizon coding agents, repository-scale engineering tasks, and vulnerability research.
ChatGLM 5.2 Fast
GLM 5.2 Fast is the high-speed serving tier of Z.ai's GLM-5.2, running the same model weights on inference infrastructure tuned for higher throughput. Output quality matches the standard GLM-5.2 endpoint; serving speed and price are the differences. It keeps the full 1M-token context window and 128K max output, along with tool calling, structured output, streaming, optional thinking mode with adjustable reasoning effort, and implicit prompt caching. Providers report roughly 2x the throughput of their standard GLM-5.2 endpoints, with peaks measured at 446 tokens per second. Because the weights are identical, it inherits GLM-5.2's coding results, including 62.1 on SWE-bench Pro. The tradeoff is a higher per-token price than standard GLM-5.2. A fit for agent loops that chain many model calls, real-time coding assistants, and other latency-sensitive workloads.
ChatGLM 5.2
GLM 5.2 is Z.ai's sixth-generation open-weight foundation model, built around a 1M-token context window that can hold entire mid-sized codebases in a single prompt. It uses a 744B-parameter Mixture-of-Experts architecture with an "IndexShare" attention optimization that cuts per-token FLOPs by 2.9x at 1M context, keeping long-context inference practical. A new MTP speculative decoding layer raises acceptance length by up to 20%, reducing latency. Dual reasoning modes (High/Max) let you trade speed for depth on complex tasks. GLM 5.2 scored 62.1 on SWE-bench Pro, outperforming GPT-5.5 (58.6) and its predecessor GLM-5.1 (58.4). It is the top-ranked open-weight model on long-horizon coding benchmarks. Best suited for repository-scale refactoring, multi-step agentic coding, full-codebase analysis, and any workflow that previously required chunking large inputs.
Frequently Asked Questions
You can access GLM 5.3 Flash by Z.AI through Puter.js AI API. Include the library in your web app or Node.js project and start making calls with just a few lines of JavaScript — no backend and no configuration required. You can also use it with Python or cURL via Puter's OpenAI-compatible API.
Yes, it is free if you're using it through Puter.js. With the User-Pays Model, you can add GLM 5.3 Flash to your app at no cost — your users pay for their own AI usage directly, making it completely free for you as a developer.
| Price per 1M tokens | |
|---|---|
| Input | $0.08 |
| Output | $0.25 |
GLM 5.3 Flash was created by Z.AI and released on Aug 26, 2026.
GLM 5.3 Flash supports a context window of 1M tokens. For reference, that is roughly equivalent to 2,621 pages of text.
GLM 5.3 Flash can generate up to 131K tokens in a single response.
GLM 5.3 Flash accepts the following input types: text, image. It produces: text.
Yes, GLM 5.3 Flash supports tool use (function calling), allowing it to interact with external tools, APIs, and data sources as part of its response flow.
GLM 5.3 Flash scores 57.5 on the Artificial Analysis Intelligence Index, outperforming 97% of tracked models. On coding, it scores 71.5 (outperforms 87% of models).
Yes — the GLM 5.3 Flash API works with any JavaScript framework, Node.js, or plain HTML through Puter.js. Just include the library and start building. See the documentation for more details.
Get started with Puter.js
Add GLM 5.3 Flash to your app without worrying about API keys or setup.
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