GLM-5.3 is Zhipu AI's newest flagship large language model, built for advanced text generation and deep reasoning. It carries forward the GLM series' full 1M-token context window and adds native, parallel tool/function calling with strict JSON schema output, making it a solid base for agentic applications that need to hold a lot of context and act on it reliably.
| Developer | Zhipu AI |
|---|---|
| Model ID | zhipu/glm-5.3 |
| Release Date | August 17, 2026 |
| Context Window | 1,024,000 tokens (~1M) |
| Max Output Tokens | 131,072 |
| Modalities | Text (input and output) |
| Capabilities | Reasoning, parallel tool/function calling, streaming, structured (JSON schema) output |
The headline spec is the context window: at just over 1,000,000 tokens, GLM-5.3 can hold large documents, long conversation histories, or sizeable codebases in a single request without needing to chunk or summarize first.
GLM-5.3 supports chain-of-thought style reasoning for complex, multi-step problems, alongside faster inference for simpler queries — letting you balance latency and depth depending on the task.
The model can call multiple tools/functions in a single turn and return responses that strictly conform to a provided JSON schema, which is useful for building reliable agents and structured data pipelines rather than free-form text you have to parse yourself.
Responses can be streamed token-by-token, keeping latency low for chat-style interfaces even on long-context requests.
With a 1M-token window, GLM-5.3 can process long reports, transcripts, or multi-file context in one pass instead of splitting the input across multiple calls.
Parallel function calling plus structured JSON output make GLM-5.3 a practical choice for agents that need to call several tools per turn and return machine-readable results.
The strict JSON schema output mode is well suited to extracting structured fields from unstructured text at scale, without post-processing free-form responses.
Measured on the AI/ML API gateway across live traffic. Figures are medians unless stated. Last updated 2026-10-12.
Software engineering and coding workflows are a documented fit for GLM 5.3, including complex software engineering, repo-scale coding, and agentic development tasks.
Long-horizon agent tasks are another stated use case, with the model described as suited to multi-step, tool-driven workflows that need to stay coherent over long contexts.
Cybersecurity work is also explicitly mentioned as a target use case, including “emergent cyber capabilities” and cybersecurity scenarios.
Structured generation and analysis are documented uses as well, including structured outputs, analysis, and long-form text handling in development workflows.
import requests
r = requests.post(
"https://api.aimlapi.com/v1/chat/completions",
headers={"Authorization": "Bearer " + AIMLAPI_KEY},
json={
"model": "zhipu/glm-5.3",
"messages": [
{
"role": "user",
"content": "Hello!"
}
]
},
)
print(r.json())
const r = 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": "zhipu/glm-5.3", "messages": [ { "role": "user", "content": "Hello!" } ] }), }); console.log(await r.json());
curl -X POST https://api.aimlapi.com/v1/chat/completions \ -H "Authorization: Bearer $AIMLAPI_KEY" \ -H "Content-Type: application/json" \ -d '{"model":"zhipu/glm-5.3","messages":[{"role":"user","content":"Hello!"}]}'
OpenAI-compatible — swap the base URL and it works with your existing SDK.
| Type | Price |
|---|---|
| Input | |
| Output | |
| Cached input |
| Benchmark | Score | What it measures | Source | Retrieved |
|---|---|---|---|---|
| Intelligence | 44.9 | Composite score across standardised reasoning, knowledge and problem-solving evaluations, measured independently by Artificial Analysis | Source | September 12, 2026 |
| Coding | 74.8 | Composite score across standardised coding evaluations, measured independently by Artificial Analysis | Source | September 12, 2026 |
| Model | Input | Output | Context | Best for |
|---|---|---|---|---|
GLM-5.3 This page | Reasoning + agents | |||
| Reasoning + agents | ||||
| Balanced coding + agents | ||||
| Long-context, multimodal & agentic workflows | ||||
| Reasoning + agents |
GLM-5.3 has a 1,024,000 tokens context window and can return up to 131,072 tokens.
GLM-5.3 takes text as input and returns text.
Use zhipu/glm-5.3 as the model id. Requests go to https://api.aimlapi.com/v1/chat/completions.
GLM-5.3 became available on August 17, 2026.
GLM-5.3 is priced at input $1.82 / 1M tokens, output $5.72 / 1M tokens, cached input $0.338 / 1M tokens.
Yes, GLM-5.3 can stream responses as they are generated.
GLM-5.3 was built by Zhipu AI.
