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The most efficient LLM inputs

Bear-2 compression model processes your raw LLM inputs like documents, websites, and transcripts to pass in maximum context with minimum tokens.

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Backed by the founders of

Strip filler text from raw LLM inputs

Bear-2 compresses long documents, websites and transcripts before they enter the LLM context window.

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Featurednew

Pax Historia · 193B tokens/mo

Compressed prompts outperformed uncompressed in a 268K-vote blind arena across all models.

+4.9%

Sonnet 4.5

+15%

Gemini 3 Flash

+5%

Purchase lift

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Helonic (YC F25) · Construction AI

Long-running agents analyzing construction drawings at near million-token prompts.

~1M

Token prompts

Hours

Agent run time

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Process raw LLM inputs

We build proprietary compression models to process raw text. Below 50ms inference with full determinism and cache safety.

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In its most fundamental sense, compression is the process of encoding

information using fewer bits or resources than the original representation

by identifying and eliminating statistical redundancies or irrelevant data

within a dataset. Whether applied to digital media, text, or the high-

dimensional vector spaces of Large Language Models, compression relies on

the principle that most raw information contains noise or repeating patterns

that do not contribute new meaning. By applying an algorithm—or in your

case, an ML-based model—to map the input data into a more compact form,

you essentially distil the signal from the noise. In the context of ML

inputs, this means transforming long-form text into a dense, mathematically

efficient representation that preserves the original semantic intent and

logical relationships while significantly reducing the physical token count,

thereby allowing a system to process more information within the same fixed

computational window or budget.

Research

Compressing Conversational Context Without Losing the Thread

Bear-2 improved CoQA accuracy from 93.3% to 95.3% while cutting tokens by 8.2%.

Research

Introducing Bear-2-Safety

Cut safety classifier costs by 30% while preserving or improving F1.

Wrap your existing client

One line wraps your OpenAI or Anthropic client. Your existing code stays the same. Compression happens automatically.

pip install the-token-company

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Ready to compress?

Access the compression API.

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