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Labs waste your tokens.
We don’t.
Every filler token in your context is revenue for a lab. With bear‑2 in front,
filler never wastes the context window. Same answers, a fraction of the cost.
Backed by the founders of
Featured
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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Long-running agents analyzing construction drawings with long prompts.
~47K
Tokens saved / prompt
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.
Read the docsIn 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
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-companyStop paying for filler.
Get an API key and see the savings on your own prompts.