GPT-5.5 Codex: More Reasoning Tokens, Worse Code
GPT-5.5 Codex's reasoning-token clustering may be degrading output quality — a warning sign for anyone building on chain-of-thought scaling.
The signal: An HN thread (327 points and climbing) is dissecting reports that GPT-5.5 Codex’s reasoning tokens are clustering — the model burns more “thinking” tokens without producing better code, and in some cases the output gets worse.
Why it matters: If you’ve wired Codex into a production coding pipeline, this is the gap between a model that fixes your bug and one that quietly eats your token budget spinning in circles. Reasoning-token bloat without a quality payoff is a cost problem wearing a capability costume.
The pattern I’m watching: Labs have spent two years selling “more reasoning = better output” as the next scaling law, and we’re now seeing the diminishing-returns wall show up in real dev tools instead of benchmark charts. This is the same curve we watched with raw parameter scaling — impressive until it isn’t.
What I’d do with this: Don’t upgrade your Codex integration on vibes — run your own repo and task set before/after, because HN anecdotes aren’t your production data. If you’re shipping agentic coding tools, add reasoning-token caps and output quality gates now, because this won’t be the last model where longer thinking quietly means worse answers.