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Ranks LLMs by real production token usage, not benchmarks. Aggregates traffic from millions of users hitting 400+ models through one API — sliced by model, lab market share, tool-call frequency, and image volume, updated weekly.
Public leaderboard ranking LLMs and multimodal models across 70+ datasets — reasoning, knowledge, coding, math, and long-context. Blends open-source and proprietary benchmarks into one comparative view spanning GPT-4, Claude, Qwen, and InternLM.
Blind side-by-side voting site where users send one prompt to two anonymous chat models, pick the winner, and millions of votes become Elo rankings across text, coding, vision, image, and video. Crowd preference, not static benchmarks, decides the order.
Runs one-command evaluation of vision-language models across 80+ multimodal benchmarks, handling data download, inference, and metric scoring in a single pass. Supports 220+ LMMs; adding a new model means writing one generate_inner() function.
A challenge repository for training the best language model that fits inside a 16,000,000‑byte (16MB) submission artifact; provides baseline training code, FineWeb bpb evaluation, a public leaderboard, and compute-grant instructions for short 8×H100 runs.
Provides 315,000 pairwise human-preference votes comparing 15 English TTS models over 300 operational prompts, with 4,500 high‑quality audio renders and structured vote/pair/prompt records for training or evaluating preference/reward models. Metadata under CC-BY-4.0; audio use governed by model providers' terms.