Distilla — cut your LLM token bill 20–40%

Distilla compresses your prompts into dense language: fewer tokens, same or better output. The cost of tokens is the new cloud bill. Distilla measures it and cuts it — honestly, with verifiable before/after numbers. Works with any model: OpenAI, Anthropic (Claude), Kimi, Gemini, Llama, local models — anything you send text to.

If you pay for AI, you're overpaying for filler tokens. Distilla removes them.

Install

pip install distilla          # core
pip install distilla[accurate] # optional: exact token counts via tiktoken

Use (2 lines)

from distilla import compress, savings

prompt = "Please, I would like you to act as a very helpful assistant and kindly help me write a well-structured professional email."
dense = compress(prompt)

print(dense)             # "role: assistant help me write professional email."
print(savings(prompt, dense))
# {'tokens_before': 27, 'tokens_after': 12, 'saved_pct': 55.6, 'method': 'tiktoken', ...}

Compress a full system+user call on the way to your LLM:

from distilla import compress_call

system, user, saved = compress_call(system_prompt, user_prompt)
resp = client.chat.completions.create(model="...", messages=[
    {"role": "system", "content": system},
    {"role": "user", "content": user},
])
print("saved", saved["saved_pct"], "%")

Why it works

• Removes filler, keeps meaning. "please / I would like you to / very / kindly / well-structured" add tokens, not information. Distilla strips them and densifies phrasing.
• Protects what matters. Code blocks, quoted strings and URLs are never touched.
• Honest, measured savings. Real token counts (tiktoken when installed), not promises.
• Model-agnostic. One call before your LLM. Drop-in with the OpenAI/Anthropic/any SDK.
• Dense = clearer. Less noise often means the model follows instructions better, not just cheaper.

What people search for (and find here)

reduce OpenAI cost · lower LLM token bill · prompt compression · cheaper GPT / Claude / Kimi calls · save tokens · AI cost optimization · token cost reduction · shrink prompts · optimize prompt length.

Typical savings

FAQ

Does it change the meaning? No — it removes filler and densifies, and protects code/quotes/URLs. On terse prompts it barely touches them. You always see the exact before/after.

Which models? All. It operates on the text before you send it, so it's independent of the provider.

Is the saving real? Yes. savings() returns true token counts (via tiktoken if installed).

MIT licensed. Built by Andreax. Fewer tokens, same result — measured, not promised.