Stop paying for context your model doesn’t need.
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.
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.
How it works
We keep the signal and drop the noise.
Keep the tokens that matter to your query.
What most teams are losing
Stop overpaying.
If you’re paying full price for your tokens, you’re leaving real money on the table.
Trimming / Truncation
- Cuts off the tail: the answer was often in what you dropped.
- Accuracy collapses on long docs.
Summarization
- Lossy rewrite: nuance and exact wording are gone.
- Costs extra LLM calls and latency for a worse context.
Question-agnostic compression
- Compresses blindly: keeps irrelevant tokens, drops important ones.
- Rarely gets past 5× without tanking accuracy.
Question-aware compression.
Feed us the query and the context. We return only the tokens that actually move the answer. You pay less, the LLM responds faster, and answers get sharper.
- Per query
- $0.263$0.037
- Tokens in
- 112,552498
GPT-5.2 + latte_v1
226× fewer tokens
- Question-aware: we compress for the task.
- At light ~2× compression, accuracy matches or beats full context.
- SDK or on-prem. Your call.
Independent benchmark
FinanceBench.
| BaselineGPT-5.2 | latte_v1 API+ GPT-5.2 | |
|---|---|---|
| Compression | None | ~2x |
| Average Context | ~106Ktokens | ~56Ktokens |
| Accuracy | 73% | 77% |
| Savings | None | ~47%cheaper |
FinanceBench, 128 questions over SEC filings. At light ~2x compression accuracy holds; push to ~10x when cost matters more than peak accuracy
Two ways to deploy
Pick the one that fits your stack.
Drop-in SDK. One API key.
Install, grab a key, compress any prompt or document before it hits your LLM. Pay per million tokens, no surprise bills.
- $10 in free credits on sign-up, no credit card required
- TypeScript & Python clients
- Question-aware compression
- Transparent per-million-token pricing
Sign up, get $10 of compression free, no card needed.
Runs inside your VPC.
Your data never leaves your network. We deploy Compresr to your infrastructure, tune it for your workload, and support you directly.
- Private deployment in your cloud or data center
- Custom throughput & latency SLAs
- Tailored to your business needs
- Dedicated support
Enterprise, finance, healthcare, regulated workloads.
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