AI InputLab

Compress context. Keep meaning.

Trim prompts and source documents to fit more inside model context windows — without losing the signal.

Strategies

Input~0 tok
AI-ready~0 tok
Savings0%
Your output will appear here.

Long prompts get truncated, slow down responses, and cost more per call. Context Compressor shrinks text using strategies tuned for LLMs — collapsing whitespace, removing low-signal phrases, and condensing repetition — while preserving the meaning your model actually needs.

How to use it

  1. 1.

    Paste your text

    Drop in a long prompt, document, or context block. The token counter shows the starting size.

  2. 2.

    Choose compression level

    Light removes obvious whitespace and filler. Aggressive condenses sentences and removes redundancy.

  3. 3.

    Compare and copy

    Side-by-side preview shows tokens saved. Copy the compressed version straight into your prompt.

Token economics, briefly

Most chat APIs price by tokens in and out. Cutting an 8,000-token system prompt to 4,500 tokens saves money on every request and frees up context for the user's actual question. For agents that loop, the savings compound dramatically.

Best for

  • Shrinking long system prompts
  • Fitting documents under model context limits
  • Reducing per-call API costs at scale
  • Speeding up agent loops

Token economy.

Every model has a context window — and you pay (in latency, money, and quality) for filling it with noise. This compressor applies three deterministic strategies: collapsing whitespace, dropping near-duplicate sentences, and lightly removing low-information words. The result is denser text that still reads naturally and feeds models with more usable signal per token.

Frequently asked

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