
GLM-5.3
glm-5.3Open sourceGLM series
Overview
1M ContextCode generationAgentic tasksReasoning
GLM-5.3 is the strongest open-source model for coding released by Zhipu. It shares the base model and parameter scale of GLM-5.2 (743B total parameters), with every capability gain coming from extreme post-training scaling. It breaks new ground on complex software engineering, terminal operations and long-horizon agent tasks, with coding capability 50% higher than GLM-5.2; it also fills in cybersecurity, performing strongly on defensive security tasks such as white-box code review and vulnerability discovery. Combined with a million-token context, tiered thinking effort, function calling and MCP tools, it can drive project-scale software development, security audits and complex automation pipelines.
Million-token context
A 1M-token context window loads project-scale codebases and very long documents in a single pass.
Coding leap
Coding capability is 50% higher than GLM-5.2, ranking first among open-source models on complex software engineering and terminal operations.
Cybersecurity
White-box code review and vulnerability discovery, with strong results on defensive security tasks.
Post-training scaling
The IndexShare / SAO / Slime frameworks keep raising the intelligence ceiling on the same base model.
Features
Reasoning✓
Streaming✓
Tool calling✓
Context caching✓
Structured output✓
Web search✓
Batch✕
Prefix completion✕
Fine-tuning✕
Pricing
Input¥8/M tokens
Input (cache hit)¥2/M tokens
Output¥28/M tokens
Rate Limits & Context
Context Window1M
Max Output128K
RPM (requests/min)200
TPM (tokens/min)3M
Built-in Tools
web_search
Responses API
code_interpreter
Responses API
web_extractor
Responses API
API Reference
ENDPOINT
POST https://api.tokenfab.cn/v1/chat/completions
Request Example
curl https://api.tokenfab.cn/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $TOKENFAB_API_KEY" \
-d '{
"model": "glm-5.3",
"thinking": { "type": "enabled" },
"reasoning_effort": "max",
"messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "请审查以下 Python 代码中的安全漏洞,并给出修复建议。"}
]
}'Response Example
JSON
{
"id": "chatcmpl-glm53-9d2k7f1",
"object": "chat.completion",
"created": 1784950000,
"model": "glm-5.3",
"choices": [{
"index": 0,
"message": {
"role": "assistant",
"content": "审查发现两处风险:1) 用户输入未做参数化处理,存在 SQL 注入隐患;2) 敏感信息以明文写入日志。建议改用预编译语句并脱敏后落盘。"
},
"finish_reason": "stop"
}],
"usage": {
"prompt_tokens": 42,
"completion_tokens": 156,
"total_tokens": 198
}
}Request Parameters
| Parameter | Type | Required | Description |
|---|---|---|---|
| model | string | ✓ | Model ID, set to glm-5.3 |
| messages | array | ✓ | List of chat messages, each with role and content |
| thinking | object | — | Thinking configuration, {"type":"enabled"} (enabling only; disabling is not supported) |
| reasoning_effort | enum | — | Thinking effort: low / high / max, default max |
| temperature | float | — | Sampling temperature, 0–2, default 0.7 |
| top_p | float | — | Nucleus sampling, 0–1, default 0.9 |
| max_tokens | int | — | Maximum output tokens |
| stream | bool | — | Whether to stream the response, default false |
| tools | array | — | Tool definitions for function calling |
| tool_choice | string | — | Tool choice policy: auto / none / a named function |