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Looking at the chart here, it seems like Sonnet 4 was already better than GPT-5-codex in the SWE verified benchmark. However, my subjective personal experience was GPT-5-codex was far better at complex problems than Claude Code.
My subjective personal experience is the exact opposite of yours, GPT-5-codex is super slow and the results are mediocre at best. I would probably stop using AI for coding if I was forced to use GPT-5-codex.
Earlier quoted context omitted.
But where is the training set of good pelicans on bikes coming from? You think they have people jigging them up internally?
Assuming they updated the crawled training data, just having a bunch of examples of specifically pelicans on bicycles from other models is likely to make a difference.
Earlier quoted context omitted.
Opposite for me…5-codex high ran out of tokens extremely quickly and didn’t adhere as well to the agents.md as Claude did to the Claude.md, perhaps because it insists on writing extremely complicated bash scripts or whole python programs to execute what should be simple commands.
Codex was a miserable experience for me until I learned to compact after every feature. Now it is a cut above CC, although the latter still has an edge at TODO scaffolding and planning.
/compact is helping you by reducing crap in your context but you can go further. And try to watch % context remaining and not go below 50% if possible - learn to choose tasks that don't require an amount of context the models can't handle very well.
Looking at the chart here, it seems like Sonnet 4 was already better than GPT-5-codex in the SWE verified benchmark. However, my subjective personal experience was GPT-5-codex was far better at complex problems than Claude Code.
I always wonder how absolute in performance a given model is. Sometimes i ask for Claude-Opus and the responses i get back are worse than the lowest end models of other assistants. Other times it surprises me and is clearly best in class. Sometimes in between this variability of performance it pops up a little survey. "How's Claude doing this session from 1-5? 5 being great." and i suspect i'm in some experiment of e…
It would, but
> To state it plainly: We never reduce model quality due to demand, time of day, or server load.
https://www.anthropic.com/engineering/a-postmortem-of-three-...
If you believe them or not is another matter, but that's what they themselves say.
I just ran this through a simple change I’ve asked Sonnet 4 and Opus 4.1, and it fails too. It’s a simple substitution request where I provide a Lint error that suggests the correct change. All the models fail. I could ask someone with no development experience to do this change and they could. I worry everyone is chasing benchmarks to the detriment of general performance. Or the next token weight for the incorrect c…
I've been worried about this for a while. I feel like Claude in particular took a step back in my own subjective performance evaluation in the switch from 3.7 to 4, while the benchmark scores leaped substantially.
To be fair, benchmarking has always been the most difficult problem to solve in this space, so it's not surprising that benchmark development isn't exactly keeping pace with all of the modeling/training development happening.
Looking at the chart here, it seems like Sonnet 4 was already better than GPT-5-codex in the SWE verified benchmark. However, my subjective personal experience was GPT-5-codex was far better at complex problems than Claude Code.
GPT-5 is like the guy on the baseball team that's really good at hitting home runs but can't do basic shit in the outfield. It also consistently gets into drama with the other agents e.g. the other day when I told it we were switching to claude code for executing changes, after badmouthing claude's entirely reasonable and measured analysis it went ahead and decided to `git reset --hard` even after I twice pushed back…
Oh wow, a lot of focus on code from the big labs recently. In hindsight it makes sense that the domain the people building it know best is the one getting the most attention, and it's also the one the models have seen the most undeniable usefulness in so far. Though personally, the unpredictability of the future where all of this goes is a bit unsettling at the same time...
Why is this? Does Anthropic have just higher infrastructure costs compared to OpenAI/xAI?
Earlier quoted context omitted.
Codex was a miserable experience for me until I learned to compact after every feature. Now it is a cut above CC, although the latter still has an edge at TODO scaffolding and planning.
Compact?
Looking at the chart here, it seems like Sonnet 4 was already better than GPT-5-codex in the SWE verified benchmark. However, my subjective personal experience was GPT-5-codex was far better at complex problems than Claude Code.
GPT-5 is like the guy on the baseball team that's really good at hitting home runs but can't do basic shit in the outfield. It also consistently gets into drama with the other agents e.g. the other day when I told it we were switching to claude code for executing changes, after badmouthing claude's entirely reasonable and measured analysis it went ahead and decided to `git reset --hard` even after I twice pushed back…