We benchmarked Codex alone against Codex routed through Edgee's compression gateway on the same repo, with the same model, under the same workflow. The result: Codex + Edgee used 49.5% fewer input tokens, improved cache hit rate from 76.1% to 85.4%, and reduced total session cost by 35.6%. This post breaks down why context compression makes Codex more efficient, more frugal, and materially cheaper to run without sacrificing useful output.
That's it! And it works the same with Claude Code.
The results:
As a gateway, Edgee can optimize the requests that are sent to OpenAI, remove noise and waste, and cut input token usage almost in half.
We ran a controlled benchmark (see the video): same repo, same model (gpt-5.4), same task sequence. One session with plain Codex, one with Codex routed through Edgee.
Input tokens: −49.5%
Total cost: −35.6%
Cache hit rate: from 76.1% to 85.4%
The cache hit rate improvement is the part I find most interesting. By sending leaner prompts, @OpenAI cache is hit more often, so the savings compound beyond just the compression ratio.
Here's what makes this different from other token compression tools: we pull token counts directly from the OpenAI API usage fields. No character-based estimates. The numbers are what you're actually billed for.
fmerian
Hey PH 👋
We're launching the Codex Compressor today.
But first, what is Edgee?
Edgee is an AI Gateway for Coding Agents, and it helps you save tokens. It's really simple to use, you only need two command lines:
A first one to install Edgee CLI with curl or brew
And a simple edgee launch codex
That's it! And it works the same with Claude Code.
The results:
As a gateway, Edgee can optimize the requests that are sent to OpenAI, remove noise and waste, and cut input token usage almost in half.
We ran a controlled benchmark (see the video): same repo, same model (gpt-5.4), same task sequence.
One session with plain Codex, one with Codex routed through Edgee.
Input tokens: −49.5%
Total cost: −35.6%
Cache hit rate: from 76.1% to 85.4%
The cache hit rate improvement is the part I find most interesting. By sending leaner prompts, @OpenAI cache is hit more often, so the savings compound beyond just the compression ratio.
Here's what makes this different from other token compression tools: we pull token counts directly from the OpenAI API usage fields. No character-based estimates. The numbers are what you're actually billed for.
⭐️ Please, give a star to our brand new OSS repository, we need support ;)
And don't hesitate to try, it's free!
Happy to answer any questions here all day. 🙏