Z.AI: GLM 5 Turbo
z-ai/glm-5-turbo
Access GLM 5 Turbo from Z.AI using the 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-turbo"
}).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-turbo"
}).then(response => {
document.body.innerHTML = response.message.content;
});
</script>
</body>
</html>
Model Card
GLM-5 Turbo is a foundation model by Z.ai optimized for fast inference and agent-driven workflows, excelling at tool invocation, complex instruction decomposition, and long-chain task execution in OpenClaw scenarios. It is built on top of the GLM-5 architecture (744B parameters, 40B active) with DeepSeek Sparse Attention for reduced deployment cost and up to 205K token context. GLM-5 Turbo supports reasoning/thinking mode and is designed for real-world multi-step agentic tasks including scheduled, persistent, and high-throughput operations.
Context Window 200K
tokens
Max Output 128K
tokens
Input Cost $1.2
per million tokens
Output Cost $4
per million tokens
Input text
modalities
Tool Use Yes
Release Date Mar 15, 2026
Model Playground
Try GLM 5 Turbo instantly in your browser.
This playground uses the Puter.js AI API — no API keys or setup required.
Benchmarks
How GLM 5 Turbo performs on standard evaluations.
| Benchmark | Score |
|---|---|
| GPQA Diamond Graduate-level science Q&A | 84.7% |
| Humanity's Last Exam Cross-domain reasoning | 27.8% |
| IFBench Instruction following | 73.2% |
| LCR Long-context reasoning | 71.7% |
| Terminal-Bench Hard Agentic terminal tasks | 33.3% |
| τ²-Bench Tool use / agents | 98.5% |
Scores sourced from Artificial Analysis.
Find other Z.AI models →
GLM 5.3 Flash
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 1,000,000 token context window and up to 131,100 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.
ChatGLM 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.
Frequently Asked Questions
You can access GLM 5 Turbo 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.
GLM 5 Turbo is free to integrate using the Puter.js AI API. With the User-Pays Model, you can add AI to your app for $0, since users cover their own AI usage through their Puter account.
| Price per 1M tokens | |
|---|---|
| Input | $1.2 |
| Output | $4 |
GLM 5 Turbo was created by Z.AI and released on Mar 15, 2026.
GLM 5 Turbo supports a context window of 200K tokens. For reference, that is roughly equivalent to 400 pages of text.
GLM 5 Turbo can generate up to 128K tokens in a single response.
GLM 5 Turbo accepts the following input types: text. It produces: text.
Yes, GLM 5 Turbo supports tool use (function calling), allowing it to interact with external tools, APIs, and data sources as part of its response flow.
GLM 5 Turbo scores 26.6 on the Artificial Analysis Intelligence Index, outperforming 83% of tracked models.
Yes — the GLM 5 Turbo 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.
Add GLM 5 Turbo to your app for free
Developers can integrate GLM 5 Turbo for free using the Puter.js AI API.
With the User-Pays Model, each user covers their own AI usage instead of the developer.