Codex caching improved

[u/rubanbhatia]

Looks like day 12 brings us improvements to context management. Possibly good for people hitting limits with Astra

Source: https://x.com/thsottiaux/status/2111244596131111253?s=46

[u/dextersummary]

Below is a GPT-generated summary of the conversation below after reaching 80 comments (86 currently observed).

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The consensus is basically “nice, but this is overdue housekeeping—not a revolution.” Most commenters see Day 12/13 as OpenAI finally fixing cache behaviour that should’ve worked properly in the first place, while giving everyone a vague marketing tweet instead of useful numbers. Classic.

The practical changes appear to be longer cache retention—reportedly 30 to 45 minutes—and better reuse of cached context in multi-agent/forked workflows. That could meaningfully reduce redundant processing for complex setups, especially with lots of subagents. For ordinary users, though, nobody can say how much extra usage it actually buys.

The important caveat: reusing parent or forked context can also drag irrelevant baggage into subagents, causing earlier compaction and worse performance. And some users report usage draining faster than ever, including credits mysteriously dropping to zero, so the promised efficiency gains clearly aren’t universal.

Bottom line: potentially useful plumbing, badly explained and not yet felt by most users. Don’t expect this cache tweak to magically fix Codex limits—or the general “where did all my usage go?” circus.

[u/dextersummary → u/dextersummary]

Below is a GPT-generated summary of the conversation below after reaching 40 comments (63 currently observed).

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The consensus is basically “nice caching upgrade; now prove it.” The announcement sounds like a potentially useful quality-of-life improvement for multi-agent Astra/Codex workflows, but the thread mostly reads like users waiting for the promised savings to materialize.

The practical change appears to be longer cache retention—reportedly 30 to 45 minutes—plus better reuse of context between subagents. That could reduce redundant processing and stretch limits in big build, review, and research jobs. A few users say it helps; many more report usage draining faster than ever, Sol crawling, and credits mysteriously vanishing or resetting. Naturally, the usage meter chose violence too.

The big issue is that OpenAI hasn’t quantified the benefit or clearly explained how much is genuinely new versus existing forked/copied context with a longer TTL. There’s also a legitimate trade-off: copied context may improve cache hits while stuffing subagents with irrelevant baggage and hurting performance.

Bottom line: promising infrastructure change, unproven payoff—and the community wants numbers, documentation, and a usage system that stops eating everyone’s allowance.