AI Model Leaderboard

How We Rank AI Models

Our leaderboard uses a composite quality index that combines three key benchmarks: MMLU Pro (measuring knowledge and reasoning across 57 subjects), HumanEval (measuring code generation ability), and MATH (measuring mathematical problem-solving). Scores are normalized to a 0-100 scale and cross-referenced against LMSYS Chatbot Arena ELO ratings for real-world validation.

We track speed (tokens per second), time-to-first-token (TTFT), pricing, and context window size to give you a complete picture. The Value Score divides quality by cost, showing you which models deliver the most capability per dollar.

Key Trends in AI Model Performance

• Open-source catching up: DeepSeek R1 and V3 now compete with top closed-source models on reasoning and coding benchmarks
• Reasoning specialization: Models like o3 and R1 trade speed for dramatically better performance on complex tasks
• Context windows expanding: 1M+ tokens is now standard for flagship models, with Llama 4 Scout supporting 10M
• Speed improving: Flash-tier models now exceed 200 tokens/second while maintaining strong quality

Choosing the Right Model

There is no single "best" model — it depends on your use case. For most applications, a model routing approach works best: route simple queries to fast, cheap models and complex queries to frontier models. This gives you the best of both worlds — low cost and high quality.