The Token Company builds LLM input compression middleware using a fast machine learning model that removes useless tokens from prompts via a drop-in API. The compression model preprocesses LLM inputs by removing least-significant tokens to cut token counts, latency, and inference costs. It compresses 100k tokens in under 100ms and typically reduces input tokens by approximately 66% while improving model accuracy and enabling larger context windows. The company’s bear-1 and bear-1.1 compression models preserve semantic intent while stripping noise, with benchmarks showing accuracy gains (e.g., +2.7 percentage points on financial QA with up to 20% fewer tokens) and up to 37% faster end-to-end latency. Integration takes minutes via a simple API.
