OpenAI’s Secret: Grug-Speak Reasoning Slashes Token Costs

OpenAI’s GPT-5.5 models score higher on coding benchmarks with dramatically fewer tokens than Gemini or Opus—20K vs 270K for comparable tasks. This efficiency stems from deliberate RL training to compress internal reasoning into terse, ungrammatical language, as seen in leaked traces like “Try period.”

The implication is a massive cost and speed advantage per task, because fewer reasoning tokens reduce both generation and re-ingestion overhead across multi-step tool chains. For engineering teams, this means total task cost should be the metric, not per-token price.

The tradeoff is complete opacity: users can’t debug model decision paths, and the terse histories may degrade performance in very long contexts. Competitors may resist similar optimizations because verbosity increases per-token revenue, creating a market tension between profit and user value.

Teams evaluating AI coding tools must benchmark cost per completed story, demand transparency on reasoning token spend, and weigh the operational risk of losing visibility into model logic against the savings.