Manim Generation Benchmark
Setting the standard for evaluating Large Language Models in generating mathematical animations with Manim
Mathematical Animation Benchmarking
Our state-of-the-art benchmark suite evaluates LLMs' capabilities in generating Manim code for mathematical animations, from basic visualizations to complex mathematical concepts.
Comprehensive Animation Testing
Our benchmark suite evaluates how well LLMs understand and generate Manim code for mathematical animations, from basic shapes to complex visualizations.
Manim-Specific Metrics
Specialized evaluation criteria designed for assessing code generation capabilities in mathematical animations and visual mathematics explanations.
Model Performance Analysis
Compare leading models like GPT-4, Claude, and Gemini across different Manim animation tasks and mathematical complexity levels.
Scene Complexity Testing
Each model undergoes testing with real-world mathematical visualization scenarios, from simple plots to complex geometric transformations.
Fairness & Consistency
Our benchmarks ensure fair comparison across different model architectures when generating Manim animations and mathematical visualizations.
Open Methodology
Full transparency in our benchmarking process, with all Manim test cases and evaluation criteria publicly available for community review.
Community-Driven
Join our growing community of mathematics educators and developers contributing to improving LLM benchmarking for Manim code generation.
Code Quality Focus
Special attention to code quality and best practices in generated Manim code, ensuring efficient and maintainable animations.
Performance Metrics
Detailed analysis of animation quality, render efficiency, and mathematical accuracy in generated Manim code.