Z.ai
Text Generation
GLM-4.7
GLM-4.7은 Zhipu의 차세대 플래그십 Model로, 총 355B 파라미터와 32B 활성화 파라미터를 가지고 있으며, 일반 대화, 추론 및 에이전트 기능에서 종합적인 업그레이드를 제공합니다. 응답은 더 간결하고 자연스러워졌으며, 글쓰기에서는 더욱 몰입감을 느낄 수 있습니다. 도구 호출 지침도 더 신뢰할 수 있게 따르며, 인공물의 프론트엔드 마감 처리와 에이전트 코드의 효과성, 장기간 과제 완료 효율성도 더욱 개선되었습니다....
Total Context:
205K
Max output:
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 시리즈 모델은 지능형 에이전트를 위해 설계된 기본 Model입니다. GLM-4.5-Air는 총 1060억 매개변수와 120억 활성 매개변수를 갖춘 더 컴팩트한 디자인을 채택하고 있습니다. 또한, 사고 모드와 비사고 모드를 모두 제공하는 하이브리드 추론 모델입니다....
Total Context:
131K
Max output:
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....
Total Context:
1049K
Max output:
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....
Total Context:
205K
Max output:
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....
Total Context:
205K
Max output:
131K
Input:
$
1.2
/ M Tokens
Input:
$
text
/ M Tokens
Output:
$
4.0
/ M Tokens
Z.ai
Text Generation
GLM-5
GLM-5 is a next-generation open-source model for complex systems engineering and long-horizon agentic tasks, scaled to ~744B sparse parameters (~40B active) with ~28.5T pretraining tokens. It integrates DeepSeek Sparse Attention (DSA) to retain long-context capacity while reducing inference cost, and leverages the “slime” asynchronous RL stack to deliver strong performance in reasoning, coding, and agentic benchmarks....
Total Context:
205K
Max output:
131K
Input:
$
0.95
/ M Tokens
Input:
$
text
/ M Tokens
Output:
$
2.55
/ M Tokens
