Your prompts carry far more tokens than the model actually reads. Compresr drops the rest — up to 90% fewer tokens, so you cut cost and latency. At light compression it matches or beats full-context accuracy on public benchmarks.
pip install compresr
Use compresr to show me live cost savings on my own file. 1. pip install compresr 2. Introspect the SDK to discover the API, don't guess. 3. Ask me for COMPRESR_API_KEY (open https://compresr.ai/signup if I don't have one: $10 free, no card). 4. Ask me for (a) a path to a long document (PDF/.md/.txt) and (b) a question about it. 5. Compress the document with the question, then print a receipt: tokens in/out, ratio, GPT-5.2 cost full vs compressed at $1.75/M input tokens, % saved. 6. Ask GPT-5.2 the question against the compressed context and print the answer. Don't skip the cost receipt; that's the point.
Works in Claude Code, Cursor, or any agent harness. Open full demo →
How it works
Feed us the query and the context. We return only the tokens that actually move the answer.
What most teams are doing today
Cuts off the end of the context — the answer was often in what you dropped. Accuracy collapses on long docs.
Nuance and exact wording are gone. Costs an extra LLM call and adds latency, for a worse context.
Keeps irrelevant tokens, drops important ones. Rarely gets past 5× without tanking accuracy.
With Compresr
Question-aware compression. Runs in front of any LLM stack — replaces nothing.
Send the query and the context — we keep the spans that carry the answer and drop the rest.
Turn 100K-token prompts into a few hundred tokens. Cut cost and latency without losing the answer.
At light ~2× compression, accuracy matches or beats full context on public benchmarks.
Python and TypeScript clients. Wrap any prompt or document with a single client.compress(...) call.
LangChain, LlamaIndex, LiteLLM, and agent harnesses. Compress tool outputs, RAG chunks, or full prompts.
Runs inside your VPC. Your data never leaves your network — tuned for regulated workloads.
From prompt to answer
pip install compresr — or use the TypeScript client, or hit the REST API directly.
POST your long document and the question. We support text, PDFs, and tool outputs.
Tokens that carry the answer, nothing else. Typical 10× — up to 226× on long, sparse docs.
Send the compressed context to GPT-5.2, Claude, Gemini, or your own model. Pay less, respond faster.
Independent benchmark
At light ~2× compression, accuracy holds. Push to ~10× when cost matters more than peak accuracy.
| Baseline | Compresr | |
|---|---|---|
| Model | GPT-5.2 | latte_v2 + GPT-5.2 |
| Compression | None | ~2× |
| Average context | ~106K tokens | ~56K tokens |
| Accuracy | 73% | 77% |
| Cost per query | Full price | ~47% cheaper |
Two ways to deploy
Install, grab a key, compress any prompt or document before it hits your LLM. Pay per million tokens, no surprise bills.
Sign up, get $10 of compression free, no card needed.
Your data never leaves your network. We deploy Compresr to your infrastructure, tune it for your workload, and support you directly.
Enterprise, finance, healthcare, regulated workloads.
FAQ
Summarization rewrites the text into something new and lossy — nuance and exact wording are lost, and you pay an extra LLM call. Compresr selects spans of your original text that carry the answer to your query and drops the rest. No rewrite, no hallucinated words.
Any of them. Compresr sits in front of your LLM stack — you send our compressed context to GPT-5.2, Claude, Gemini, Llama, or your own model. We ship first-party integrations for LangChain, LlamaIndex, LiteLLM, and the OpenAI/Anthropic SDKs.
On the hosted API, requests are processed in-memory and never stored or used for training. For regulated workloads, deploy Compresr on-prem inside your VPC — your data never leaves your network.
$0.10 per 1M input tokens. First $10 of credits are free on sign-up — no credit card required. Enterprise on-prem pricing is custom.
At light ~2× compression, our benchmarks show accuracy that matches or slightly beats full-context. Push to ~10× when cost matters more than peak accuracy. You control the ratio per request.
Install the SDK, grab a key, and see the cost receipt on your own file.
$10 free credits · no credit card required
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