LLM Stats

Product Introduction

1. LLM Stats is a comprehensive analytics platform designed for comparing and evaluating artificial intelligence models through standardized benchmarks, pricing structures, and capability assessments. The platform aggregates performance metrics, cost data, and technical specifications from leading AI organizations into a unified interface.
2. The core value of LLM Stats lies in its ability to simplify decision-making for AI developers and researchers by providing real-time, objective comparisons of hundreds of models across critical evaluation parameters. It eliminates the need to manually compile data from disparate sources, offering centralized access to verified benchmarks and pricing details.

Main Features

1. The platform features a dynamic leaderboard updated daily with performance rankings across benchmarks such as MMLU, GSM8K, HumanEval, and GPQA, incorporating both proprietary and open-source models. Users can filter results by parameters like context length, licensing, and multimodal capabilities.
2. LLM Stats provides a unified API that enables programmatic access to 100+ AI models through a single OpenAI-compatible endpoint, supporting seamless integration into existing workflows. The API includes standardized pricing per million tokens for input/output operations, with 99.9% uptime guarantees.
3. A browser-based playground allows free testing of AI models in real time, offering side-by-side comparisons of outputs for tasks like code generation, mathematical reasoning, and long-context processing. The playground supports tokenization statistics and context window visualizations for technical validation.

Problems Solved

1. The platform addresses the fragmentation of AI model evaluation by consolidating benchmark data, pricing, and technical specifications from sources like research papers, official documentation, and provider blogs. This resolves inefficiencies in cross-referencing performance metrics across isolated datasets.
2. LLM Stats primarily serves AI researchers, machine learning engineers, and product teams requiring objective comparisons for model selection. Secondary users include enterprise architects optimizing cost-performance ratios and open-source developers benchmarking against proprietary models.
3. Typical use cases include selecting the optimal model for research projects based on GPQA reasoning scores, comparing API costs per million tokens across providers like Groq and DeepSeek, and validating long-context capabilities using the 1M-token playground environment.

Unique Advantages

1. Unlike static leaderboards, LLM Stats incorporates real-time updates for emerging models like Gemini 3.0 and Grok-4 Heavy, with verified benchmarks sourced directly from official releases. The platform cross-references 25+ metrics per model, including context window lengths and knowledge cutoff dates.
2. The API uniquely normalizes pricing across providers by converting all costs to USD per million tokens, accounting for variations in tokenization methods. Advanced filters enable comparisons by hardware acceleration types (e.g., Groq LPU vs. Cerebras CS-3) and quantization levels (4-bit/8-bit).
3. Competitive advantages include proprietary visualization tools for token-to-text ratios (1M tokens ≈ 30 podcast hours or 1,000 book pages) and hardware-performance trade-off analyses. The platform’s issue tracking system allows community-driven data corrections, ensuring auditability.

Frequently Asked Questions (FAQ)

1. How does LLM Stats ensure benchmark accuracy? Benchmarks are extracted from peer-reviewed research papers, official technical reports, and provider documentation, with version-controlled updates. Users can flag discrepancies through GitHub-style issue discussions for community verification.
2. What distinguishes the API from direct provider access? The API abstracts provider-specific SDKs into a unified endpoint with automatic fallover between models, reducing integration complexity. Rate limits and costs are calculated in real time across 100+ models using standardized token counters.
3. Can the playground handle multimodal model testing? While current focus is on text-based benchmarks, multimodal evaluation for image/audio processing is roadmap-planned for Q1 2026. Existing users can test multimodal capabilities through API integrations with model-specific endpoints.