LegalOn halves Codex costs while maintaining development speed
65% cost cut. LegalOn slashed Codex expenses while keeping development velocity—here's how model matching and budget strategy changed the game.
Why it matters
LegalOn demonstrated concrete cost optimization across OpenAI's Codex tier by strategically routing tasks to Astra, Sol, and Luna models and managing inference budgets. This is actionable for enterprises running high-volume code-generation workloads at scale.
The key facts
10 to knowDaily Codex costs cut by 65%
Development speed maintained during cost reduction
Strategy: matched Astra, Sol, and Luna models to specific tasks
Budget managed strategically across model tier selection
Published as case study on OpenAI index (Oct 8, 2026)
LegalOn reduced estimated daily Codex costs by 65%
Maintained development speed while cutting costs
Used model matching strategy: Astra, Sol, and Luna assigned to different task categories
Strategic budget management enabled the optimization
Published by OpenAI (vendor case study, not independent validation)
Go to the source
OpenAI Blogopenai.com
Publisher excerpt: LegalOn cut estimated daily Codex costs by 65% while maintaining development speed. It matched Astra, Sol, and Luna to tasks and managed budgets strategically.