About llmbenchmark.org
Building the future of AI evaluation with scientific rigor and open-source collaboration
Our Mission
We built llmbenchmark.org to address a critical gap in the AI ecosystem: the lack of scientifically rigorous, transparent, and reproducible benchmarks for Large Language Models.
As LLMs become increasingly integrated into critical applications, the need for reliable evaluation frameworks has never been more important. Our platform provides researchers, developers, and organizations with research-grade tools to test and compare models across multiple dimensions.
Our Vision
We envision a future where LLM evaluation is:
- •Scientific: Built on rigorous methodology with peer-reviewable results
- •Transparent: Open source code, public datasets, and clear evaluation criteria
- •Reproducible: Deterministic tests with version-controlled prompts and rubrics
- •Comprehensive: Evaluation across performance, quality, cost, and emerging capabilities
Our Approach
Research-First
Every benchmark we develop starts with foundational research. We analyze cutting-edge papers, implement novel approaches, and validate our methodology through rigorous testing.
Open Source
All our code, datasets, and evaluation frameworks are publicly available. We believe transparency is essential for trust and collaboration in AI evaluation.
Community-Driven
We actively encourage contributions from researchers and developers worldwide. Our platform grows stronger through diverse perspectives and collaborative improvement.
Real-World Focus
Our benchmarks test capabilities that matter in production environments, from long-context understanding to computational efficiency and cost optimization.
Built With
llmbenchmark.org was designed and developed in collaboration with Claude Code, Anthropic's AI-powered development assistant. This partnership represents our commitment to leveraging cutting-edge AI tools while maintaining human oversight and research integrity.
Every line of code, design decision, and documentation entry has been carefully reviewed and validated by our team to ensure quality and accuracy.
Technology Stack
Frontend
- Next.js 15 with React 19
- TypeScript in strict mode
- Tailwind CSS 4
- Framer Motion for animations
Backend
- Node.js 22 with Hono framework
- PostgreSQL 16 with pgvector
- Redis for job queues (BullMQ)
- Prisma ORM
Testing
- Python 3.13 test executors
- LangChain integration
- Elasticsearch for analytics
- Docker containerization
Infrastructure
- Docker Compose orchestration
- Traefik reverse proxy
- pnpm workspace monorepo
- GitHub Actions CI/CD
Get Involved
We welcome contributions from the community! Whether you're a researcher with new benchmark ideas, a developer who wants to improve our platform, or someone who found a bug to report, your input is valuable.