(I'm from OpenAI.)
Accuracy can decreases at large context sizes. OpenAI's compaction handles this better than anyone else, but it's still an issue.
If you are seeing this kind of thing start a new chat and re-run the same query. You'll usually see an improvement.
The following are true:
- In our API, we don't change model weights or model behavior over time (e.g., by time of day, or weeks/months after release)
- Tiny caveats include: there is a bit of non-determinism in batched non-associative math that can vary by batch / hardware, bugs or API downtime can obviously change behavior, heavy load can slow down speeds, and this of course doesn't apply to the 'unpinned' models that are clearly supposed to change over time (e.g., xxx-latest). But we don't do any quantization or routing gimmicks that would change model weights.
- In ChatGPT and Codex CLI, model behavior can change over time (e.g., we might change a tool, update a system prompt, tweak default thinking time, run an A/B test, or ship other updates); we try to be transparent with our changelogs (listed below) but to be honest not every small change gets logged here. But even here we're not doing any gimmicks to cut quality by time of day or intentionally dumb down models after launch. Model behavior can change though, as can the product / prompt / harness.
ChatGPT release notes: https://help.openai.com/en/articles/6825453-chatgpt-release-...
Codex changelog: https://developers.openai.com/codex/changelog/
Codex CLI commit history: https://github.com/openai/codex/commits/main/
I've had this perceived experience so many times, and while of course it's almost impossible to be objective about this, it just seem so in your face.
I don't discard being novelty plus getting used to it, plus psychological factors, do you have any takes on this?
https://www.reddit.com/r/OpenAI/comments/1qv77lq/chatgpt_low...
The intention was purely making the product experience better, based on common feedback from people (including myself) that wait times were too long. Cost was not a goal here.
If you still want the higher reliability of longer thinking times, that option is not gone. You can manually select Extended (or Heavy, if you're a Pro user). It's the same as at launch (though we did inadvertently drop it last month and restored it yesterday after Tibor and others pointed it out).
It's worth checking different versions of Claude Code, and updating your tools if you don't do it automatically. Also run the same prompts through VS Code, Cursor, Claude Code in terminal, etc. You can get very different model responses based on the system prompt, what context is passed via the harness, how the rules are loaded and all sorts of minor tweaks.
If you make raw API calls and see behavioural changes over time, that would be another concern.
PS - I appreciate you coming here and commenting!
However it's possible that consumers without a sufficiently tiered plan aren't getting optimal performance, or that the benchmark is overfit and the results won't generalize well to the real tasks you're trying to do.
I think a lot of people are concerned due to 1) significant variance in performance being reported by a large number of users, and 2) We have specific examples of OpenAI and other labs benchmaxxing in the recent past (https://grok.com/share/c2hhcmQtMw_66c34055-740f-43a3-a63c-4b...).
It's tricky because there are so many subtle ways in which "the numbers are all real" could be technically true in some sense, yet still not reflect what a customer will experience (eg harnesses, etc). And any of those ways can benefit the cost structures of companies currently subsidizing models well below their actual costs with limited investor capital. All with billions of dollars in potential personal wealth at stake for company employees and dozens of hidden cost/performance levers at their disposal.
And it doesn't even require overt deception on anyone's part. For example, the teams doing benchmark testing of unreleased new models aren't the same people as the ops teams managing global deployment/load balancing at scale day-to-day. If there aren't significant ongoing resources devoted to specifically validating those two things remain in sync - they'll almost certainly drift apart. And it won't be anyone's job to even know it's happening until a meaningful number of important customers complain or sales start to fall. Of course, if an unplanned deviation causes costs to rise over budget, it's a high-priority bug to be addressed. But if the deviation goes the other way and costs are little lower than expected, no one's getting a late night incident alert. This isn't even a dig at OpenAI in particular, it's just the default state of how large orgs work.
Curious to see how things will be with 5.3 and 4.6
ChatGPT-5.2-Codex follows directions to ensure a task [bead](https://github.com/steveyegge/beads) is opened before starting a task and to keep it updated almost to a fault. Claude-Opus-4.5 with the exact same directions, forgets about it within a round or two. Similarly, I had a project that required very specific behaviour from a couple functions, it was documented in a few places including comments at the top and bottom of the function. Codex was very careful in ensuring the function worked as was documented. Claude decided it was easier to do the exact opposite, rewrote the function, the comments, and the documentation to saynit now did the opposite of what was previously there.
If I believed a LLM could be spiteful, I would've believed it on that second one. I certainly felt some after I realised what it had done. The comment literally said:
// Invariant regardless of the value of X, this function cannot return Y
// Returns Y if X is true[1]: https://github.com/Vibecodelicious/llm-conductor/blob/main/O...
They can both write fairly good idiomatic code but in my experience opus 4.5 is better at understanding overall project structure etc. without prompting. It just does things correctly first time more often than codex. I still don't trust it obviously but out of all LLMs it's the closest to actually starting to earn my trust
I definitely suspect all these models are being degraded during heavy loads.
You always have to question these benchmarks, especially when the in-house researchers can potentially game them if they wanted to.
Which is why it must be independent.
[0] https://gizmodo.com/meta-cheated-on-ai-benchmarks-and-its-a-...