정보에 대해서GLM-4.5
GLM-4.5 시리즈 모델은 지능형 에이전트 애플리케이션의 복잡한 요구를 충족하기 위해 추론, 코딩 및 지능형 에이전트 기능을 통합하여 설계된 기반 모델입니다. GLM-4.5는 3550억 개의 총 매개변수와 320억 개의 활성 매개변수를 제공하며, 두 가지 모드인 사고 모드와 비사고 모드를 제공합니다.
Explore how DeepSeek-V3's advanced reasoning and coding capabilities translate into real-world applications.
Automated Code Generation & Debugging
Generate, optimize, and debug complex code snippets across various programming languages. The model's strong reasoning helps identify logical errors and suggest efficient solutions.
Use Case Example:
"A software engineer used DeepSeek-V3 to refactor a legacy Python module, resulting in a 40% reduction in code complexity and a 25% improvement in execution speed."
Scientific & Mathematical Research
Assist researchers by solving complex mathematical problems, formulating hypotheses, and analyzing data. Its ability to reason through abstract concepts makes it a powerful tool for scientific discovery.
Use Case Example:
"A physicist modeled a complex quantum mechanics problem, and the model provided a step-by-step derivation that led to a novel insight, which was later verified experimentally."
Intelligent Agent & Tool Integration
Build sophisticated AI agents that can understand user requests, select the appropriate tools (e.g., APIs, databases), and execute multi-step tasks autonomously.
Use Case Example:
"An automated travel assistant powered by DeepSeek-V3 booked a complete itinerary by interacting with flight, hotel, and car rental APIs based on a single natural language request from the user."
Advanced Conversational AI
Create highly engaging and context-aware chatbots, virtual assistants, or role-playing characters for gaming and entertainment. The model excels at maintaining coherent and natural-sounding dialogue.
Use Case Example:
"A gaming company implemented an NPC (Non-Player Character) using the model, which provided dynamic, unscripted interactions that significantly enhanced player immersion."
메타데이터
사양
주
Deprecated
건축
교정된
아니요
전문가의 혼합
네
총 매개변수
335B
활성화된 매개변수
32B
추론
아니요
Precision
FP8
콘텍스트 길이
131K
Max Tokens
다른 모델과 비교
이 Model이 다른 것들과 어떻게 비교되는지 보세요.
Z.ai
chat
GLM-4.7
출시일: 2025. 12. 23.
Total Context:
205K
Max output:
205K
Input:
$
0.42
/ M Tokens
Output:
$
2.2
/ M Tokens
Z.ai
chat
GLM-4.6V
출시일: 2025. 12. 8.
Total Context:
131K
Max output:
131K
Input:
$
0.3
/ M Tokens
Output:
$
0.9
/ M Tokens
Z.ai
chat
GLM-4.6
출시일: 2025. 10. 4.
Total Context:
205K
Max output:
205K
Input:
$
0.39
/ M Tokens
Output:
$
1.9
/ M Tokens
Z.ai
chat
GLM-4.5-Air
출시일: 2025. 7. 28.
Total Context:
131K
Max output:
131K
Input:
$
0.14
/ M Tokens
Output:
$
0.86
/ M Tokens
Z.ai
chat
GLM-4.5V
출시일: 2025. 8. 13.
Total Context:
66K
Max output:
66K
Input:
$
0.14
/ M Tokens
Output:
$
0.86
/ M Tokens
Z.ai
chat
GLM-4.1V-9B-Thinking
출시일: 2025. 7. 4.
Total Context:
66K
Max output:
66K
Input:
$
0.035
/ M Tokens
Output:
$
0.14
/ M Tokens
Z.ai
chat
GLM-Z1-32B-0414
출시일: 2025. 4. 18.
Total Context:
131K
Max output:
131K
Input:
$
0.14
/ M Tokens
Output:
$
0.57
/ M Tokens
Z.ai
chat
GLM-4-32B-0414
출시일: 2025. 4. 18.
Total Context:
33K
Max output:
33K
Input:
$
0.27
/ M Tokens
Output:
$
0.27
/ M Tokens
Z.ai
chat
GLM-Z1-9B-0414
출시일: 2025. 4. 18.
Total Context:
131K
Max output:
131K
Input:
$
0.086
/ M Tokens
Output:
$
0.086
/ M Tokens
