
Z.ai
Text Generation
GLM-4.7
GLM-4.7 是智谱的新一代旗舰模型,拥有 355B 的总参数和 32B 的激活参数,在一般对话、推理和代理能力方面提供了全面升级。响应更加简洁自然;写作更加身临其境;工具调用指令得到了更可靠的执行;并且工件和代理代码的前端润色以及长时间任务完成效率得到了进一步提升。...
上下文长度:
205K
最大输出长度:
205K
Input:
$
0.42
/ M Tokens
Input:
$
text
/ M Tokens
Output:
$
2.2
/ M Tokens

Z.ai
Text Generation
GLM-4.5-Air
GLM-4.5系列模型是为智能代理设计的基础模型。GLM-4.5-Air采用了更紧凑的设计,拥有1060亿个总参数和120亿个活动参数。它还是一种混合推理模型,提供思维模式和非思维模式。...
上下文长度:
131K
最大输出长度:
131K
Input:
$
0.14
/ M Tokens
Input:
$
text
/ M Tokens
Output:
$
0.86
/ M Tokens

Z.ai
Text Generation
GLM-5.2
GLM-5.2 is Z.ai’s most capable open-source model to date, built for long-horizon agentic engineering with a truly usable 1M-token context window. It keeps project state intact across ultra-long tasks, reducing the need to compress or discard context—the longer the task, the more it can remember and reason....
上下文长度:
1049K
最大输出长度:
262K
Input:
$
1.302
/ M Tokens
Input:
$
text
/ M Tokens
Output:
$
4.092
/ M Tokens

Z.ai
Text Generation
GLM-5.1
GLM-5.1 is Z.ai's next-generation flagship model built for agentic engineering. It is designed to run continuously for hours or even longer, refining its strategy as it works—the longer it runs, the better the results....
上下文长度:
205K
最大输出长度:
131K
Input:
$
1.19
/ M Tokens
Input:
$
text
/ M Tokens
Output:
$
3.74
/ M Tokens

Z.ai
Text Generation
GLM-5V-Turbo
GLM-5V-Turbo is Zhipu’s latest flagship multimodal foundation model, optimized for multimodal coding and agent capabilities. It supports up to 200K tokens of image, video, and text context, and, when integrated with frameworks such as Claude Code and OpenClaw, can handle complex long-horizon programming and assistant tasks....
上下文长度:
205K
最大输出长度:
131K
Input:
$
1.2
/ M Tokens
Input:
$
text
/ M Tokens
Output:
$
4.0
/ M Tokens

Z.ai
Text Generation
GLM-5
GLM-5 是一款面向复杂系统工程和长时间自主任务的下一代开源模型,扩展到约744B稀疏参数(约40B激活)和约28.5T预训练tokens。它集成了DeepSeek稀疏注意力(DSA),在减少推理成本的同时保留了长上下文能力,并利用“slime”异步RL堆栈,在推理、编码和自主基准测试中提供强大的性能。...
上下文长度:
205K
最大输出长度:
131K
Input:
$
0.95
/ M Tokens
Input:
$
text
/ M Tokens
Output:
$
2.55
/ M Tokens

