OpenCompressOpenCompress

Real numbers. Real savings.

Verified on 12318 real AI agent conversations across GPT-5.3, Claude, Gemini, DeepSeek, Grok, MiniMax, and Kimi. No synthetic data.

43.7%
Token Savings
Avg across 12,318 real conversations
4.9/5
Accuracy Score
LLM-as-judge evaluation
$825
Annual Savings
GPT-5.2 Pro at 10k req/day
350+
Models Tested
Across all major LLM providers
Cost Impact

Savings across every major LLM

Same compression, applied to 29 models from 10 providers. Annual savings projected at 10,000 requests per day.

o1-pro
OpenAI
$825
per year
Save 38.2%5.0 → 4.5/5$150/1M in
GPT-4
OpenAI
$152
per year
Save 46.8%5.0 → 4.6/5$30/1M in
o3 Pro
OpenAI
$108
per year
Save 37.5%5.0 → 4.3/5$20/1M in
Claude Opus 4.1
Anthropic
$107
per year
Save 44.1%5.0 → 3.8/5$15/1M in
Claude Opus 4
Anthropic
$106
per year
Save 43.6%5.0 → 4.0/5$15/1M in
Quality Guarantee

4.9/5 accuracy — zero factual errors

Every compressed response evaluated by LLM-as-judge against the original. Compression removes redundancy, not meaning. 12318 cases tested.

Accuracy4.9/5
Compressed responses are factually consistent with originals
Usefulness4.3/5
Responses remain equally helpful to the user
Completeness4.3/5
Key points and critical context preserved
Unique Innovation

Output tokens cost 3-5x more. We compress them too.

Every other compression service only touches input. Our output alias system instructs the model to use shorthand in its response — reducing the most expensive tokens in the pipeline.

output_alias.json
// Alias dictionary injected into prompt
{ "@HA": "handleUserAuthentication",
"@DB": "the database",
"@AT": "authentication token" }
// Without aliases (24 tokens)
The function handleUserAuthentication checks the user's credentials against the database and returns an authentication token.
// With aliases (18 tokens → 25% fewer)
The function @HA checks the user's credentials against @DB and returns an @AT.
14% output token reduction with aliases
Verified on 12318 real conversations — aliases cut the most expensive tokens in the pipeline. No other service does this.
How Output Aliases Work
1
Analyze conversation
Identify repeated identifiers, function names, and technical terms in the context.
2
Build alias dictionary
Map long, repeated terms to short @-prefixed aliases (e.g., @HA → handleUserAuthentication).
3
Inject into prompt
Include alias dictionary in the system prompt so the model uses shorthand in its response.
4
Expand on client
Replace aliases back to full terms before showing the user — completely transparent.
Under the Hood

Five-stage pipeline

Each stage targets a different source of token waste. They compound — the output of one feeds into the next.

Stage Activation Rate
Semantic Prune100%avg 820 tok saved
Code Minify84.7%avg 420 tok saved
Output Alias50%
Relevance Filter23.7%avg 319 tok saved
Dict Compress12.7%avg 244 tok saved
Savings by Conversation Length
43.4%
3-10 msgs
n=43
45.3%
11-20 msgs
n=25
46.2%
21-40 msgs
n=25
46.6%
40+ msgs
n=25
ROI Calculator

Calculate your savings

Pick a model or enter your own token usage. See how much OpenCompress saves you.

$5,000
$100$100k

Savings rate based on benchmark data for this model. We compress input tokens before they reach the LLM — your existing API keys and models stay the same.

Monthly Net Savings
$1,528
38% compression on $5,000/mo · you keep 80%
Annual Net Savings
$18,336
Token Reduction
38%
Cost After
$3,472/mo
Monthly LLM cost (before)$5,000.00
Gross savings (38%)-$1,910.00
Our fee (20% of savings)+$382.00
Your net savings$1,528.00/mo
Monthly cost (after)$3,472.00
Get Started

Ready to cut your LLM costs by 40%?

Two lines of code. Every model. Automatic compression. You only pay for savings we actually deliver.

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