GLM-4.1V-9B-Thinking
About GLM-4.1V-9B-Thinking
GLM-4.1V-9B-Thinking is an open-source Vision-Language Model (VLM) jointly released by Zhipu AI and Tsinghua University's KEG lab, designed to advance general-purpose multimodal reasoning. Built upon the GLM-4-9B-0414 foundation model, it introduces a 'thinking paradigm' and leverages Reinforcement Learning with Curriculum Sampling (RLCS) to significantly enhance its capabilities in complex tasks. As a 9B-parameter model, it achieves state-of-the-art performance among models of a similar size, and its performance is comparable to or even surpasses the much larger 72B-parameter Qwen-2.5-VL-72B on 18 different benchmarks. The model excels in a diverse range of tasks, including STEM problem-solving, video understanding, and long document understanding, and it can handle images with resolutions up to 4K and arbitrary aspect ratios
Explore how GLM-4.1V-9B-Thinking's advanced multimodal reasoning can be applied to solve complex, real-world problems across various domains.
Advanced STEM Problem Solving
Leverage GLM-4.1V-9B-Thinking's multimodal reasoning to solve complex STEM challenges, analyzing diagrams, equations, and data to derive insights and verify hypotheses.
Use Case Example:
"Assisted a quantum physics researcher by analyzing complex experimental data plots and theoretical equations to validate a new particle interaction model, reducing validation time by weeks."
Multimodal Code & System Debugging
Analyze code, error logs, UI screenshots, and architectural diagrams to pinpoint subtle bugs, optimize performance, and suggest robust solutions across diverse tech stacks.
Use Case Example:
"Identified a critical deadlock in a real-time embedded C++ system by reasoning through its execution trace, memory dumps, and a video of the system's failure state, providing an immediate fix."
Intelligent Financial & Market Analysis
Perform deep quantitative and qualitative analysis on financial reports, market charts, and news feeds, identifying trends, inferring market dynamics, and generating comprehensive strategies.
Use Case Example:
"Analyzed a company's quarterly earnings reports, investor call transcripts, and real-time stock market charts to predict a significant market shift, advising on optimal portfolio adjustments."
Comprehensive Visual & Document Auditing
Automate auditing of complex systems by reasoning through legal documents, engineering blueprints, operational logs, and video feeds to detect inconsistencies and vulnerabilities.
Use Case Example:
"Reviewed a set of smart contracts, their associated architectural diagrams, and a video simulation of potential attack vectors, identifying a critical reentrancy vulnerability and proposing a secure refactor."
Metadata
Specification
State
Deprecated
Architecture
Vision-Language Model
Calibrated
No
Mixture of Experts
No
Total Parameters
9B
Activated Parameters
9B
Reasoning
No
Precision
FP8
Context length
66K
Max Tokens
66K
Z.ai
GLM-4.7
Total Context:
205K
Max output:
205K
Input:
$
0.42
/ M Tokens
Output:
$
2.2
/ M Tokens
Z.ai
chat
GLM-4.6V
Total Context:
131K
Max output:
131K
Input:
$
0.3
/ M Tokens
Output:
$
0.9
/ M Tokens
Z.ai
chat
GLM-4.6
Total Context:
205K
Max output:
205K
Input:
$
0.39
/ M Tokens
Output:
$
1.9
/ M Tokens
Z.ai
chat
GLM-4.5-Air
Total Context:
131K
Max output:
131K
Input:
$
0.14
/ M Tokens
Output:
$
0.86
/ M Tokens
Z.ai
chat
GLM-4.5V
Total Context:
66K
Max output:
66K
Input:
$
0.14
/ M Tokens
Output:
$
0.86
/ M Tokens
Z.ai
chat
GLM-4.1V-9B-Thinking
Total Context:
66K
Max output:
66K
Input:
$
0.035
/ M Tokens
Output:
$
0.14
/ M Tokens
Z.ai
chat
GLM-Z1-32B-0414
Total Context:
131K
Max output:
131K
Input:
$
0.14
/ M Tokens
Output:
$
0.57
/ M Tokens
Z.ai
chat
GLM-4-32B-0414
Total Context:
33K
Max output:
33K
Input:
$
0.27
/ M Tokens
Output:
$
0.27
/ M Tokens
Z.ai
chat
GLM-Z1-9B-0414
Total Context:
131K
Max output:
131K
Input:
$
0.086
/ M Tokens
Output:
$
0.086
/ M Tokens
