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.
You're mid-task. Claude is in flow. Then the plan limit hits and everything stops. You know the feeling — the session cuts out, the context is gone, and you're starting over. For heavy Claude Code users, this isn't an occasional annoyance. It's a regular ceiling on what you can get done in a day.
We built Edgee's Claude Code Compressor to push that ceiling back.
Over the last few months, we've been working on a problem we kept seeing in production AI systems:
LLM costs don't scale linearly with usage, they scale with context. As teams add RAG, tool calls, long chat histories, memory, and guardrails, prompts become huge and token spend quickly becomes the main bottleneck.
So we built a token compression layer designed to run before inference.
