That does not match my experience. I switched away from Anthropic to OpenAI roughly a month ago, and it's almost comical how much more usage I'm getting out of this subscription.
I migrated from Anthropic's 5x plan to OpenAI's 5x plan, and eventually upgraded to 20x after I was able to statistically verify that OpenAI plans were almost exact multipliers of the Plus plan, exactly as advertised. Meanwhile, Anthropic has gotten caught playing "20x referred to the five hour limit" word games with their customers.
There was a period of time Codex was better, they slowly cut it back by my estimate 3x a few months ago.
I've verified this with the heaviest users I know, I've run the numbers myself over and over.
I have too many max accounts on each to not know this. My Claude accounts typically are doing 2x the number of sessions, and every single week the Codex accounts run out faster even with all these resets. I can easily burn a full weeks usage in a half day, it's closer to 1.5 with CC.
I've used the $200 dollar Anthropic plan @ Opus4/4.1, 4.5 and 4.8, and the $200 OAI plan from GPT5-6, and at every point in time my anecdotal experience is that the OAI limits are FAR more generous. I could consistently burn my weekly limits in ~36h on Opus, but it's hard to do it in less than ~72h with GPT.
It's really not a question, in every dimension I've confirmed it including socially across a lot of the heaviest users. There was a short period of time this was true it's not been true for months now.
If it was true, it would only be a very recent phenomenon, and it still doesn't match anecdotal reports from people I trust. If you have data to back up your assertions you should share it, otherwise you come across as very sus.
Lol @ sus though, I mean I am curious how this can be because I do see people saying Codex is more generous and wonder how it can be. I have too much usage for too long to have any doubts, but for all I know OpenAI black boxed me or Anthropic put me in some nice bucket, I wouldn't be surprised if they do that. I did see some mention that they can limit your tokens if they suspect you of things, though I forget the source of that.
This is not true, the last few releases have had almost no breaking changes. There was a really bad period during all their big refactors but they've since corrected for that.
And as for outdated dependencies, breaking changes, and unfriendly companies, the reason I ever got into RN was after trying to build an iOS app using SwiftUI (and then bailing to UIKit to no avail). Talk about a borked ecosystem.
The frameworks, developer tools, languages, and iteration speed of RN hands down beat any one platform, and you have one model for your data and state which is an unequivocal win. I agree it has plenty of warts, but the arc of progress is that they have been going down steadily.
If you're a 3k person developer team where 200ms can cost millions a year, then by all means go purely native. Though I bet if they'd just gone to the new architecture and spent 1/5 of the cost in agents optimizing things and building a few native bridges they'd have nicer apps and be shipping faster.
Cool stuff. I'm moving One[stack.dev] onto pure rust now, and Tamagui v3 compiler will also is moving from Babel to Yuku which is quite interesting - from my testing it's more flexible and quite a bit faster than OXC.
Insert the famous Louie CK phones on planes bit. Sand is thinking, working, coding and now looking for you, for pennies an hour... but "oof" it's not good enough.
> The question now is if GLP-1s have additional anti-inflammatory influence.
This is well established, not at all a question.
> they might reduce some inflammation markers slightly more than weigh loss alone
There's no "slightly," It's going to be a news story for years to come. It's a broad spectrum anti-inflammatory drug of a breadth and efficacy never seen before.
Can you please share a good reference on this? All I read about GLP-1 stuff is popular science style punlications. I'm genuinely interested in learning about their benefits over reducing caloric intake, i.e. diet.
Careful, you may have a bit of psychosis. They are very, very far from incomprehensible, and also very far from the top at least of my field. The best in my field are produce far higher quality results, and I think that's true for all fields. It's just an incredibly good 85% quality machine that experts all use because they can guide it to be up to their quality faster than doing it themselves.
You could take that even further, to the actual danger of reliance of these tools when you lack the expert knowledge. That is, when you assume it took you 100%, but missed the 15% it got very wrong, or perhaps even worse: subtly wrong. This compounds with the next similar task, and either you've made the actual experts quit their job as it has become to babysit LLM output, or you end up with an unusable mess, deleted production databases, etc.
I've been using this DeepSeek model the whole day today after building with 5.6 Luna extensively over the last week and I would disagree, at least for Rust + OpenGL.
DeepSeek just spend almost 2 hours trying to figure out why terrain textures were not working. It tried everything over and over again, it even had reference code for meshes on how to setup the rendering with materials, and it could just not do it.
I finally gave up and gave it to GPT-5.6 Luna instead, and figure out in a single prompt after 20 seconds, that the terrain mesh was being initialized with None in the material slot.
Other tasks it has managed to figure out at least, but it is significantly slower than GPT-5.6 Luna and it requires a lot more iterations.
That's roughly my experience. Luna is extremely efficient and at higher levels of reasoning and longer running tasks more capable.
Reading the DS reasoning is wild, it's constantly going in circles. The most minor lack of clarity in your prompt and it will spend ages going back and forth on what you meant. It reasons 5x longer than the preview which makes it really slow now as well. We did a lot of work to nudge it to be decisive and improve our evaluation setup to there's more clarity, and it helped but only marginally.
Ours is a full-stack app one shot test so it includes backend, frontend, design, and QA/testing. It's graded by Opus xhigh and Sol xhigh and the grades are averaged.
DS4 preview would finish in 20 minutes flat on high reasoning and grades 6/10. Luna high gets 9/10 in about 30 minutes. DS4-final is crazy - at high thinking it's taking over an hour and getting ~8 but only had one successful run as I got tired of waiting so long after many early abort/retries trying to debug why thinking was so long. The lowest thinking still takes over 45 minutes, and with thinking off it actually is finally closer to preview in time but actually get's a much more varying result anywhere from incomplete to 6 it seems.
Costs per run DS4 is best but not actually by a lot as it's spending 10x the tokens with all the reasoning and mistakes. It's a very brute force model and I really preferred preview in many ways for how predictably fast it was.
Side note, Spark 1.2 is a nice model for this test, best in frontend design and fastest to get results together, though not nearly as efficient as Luna. Grok scores similarly to Spark but at like $50/run vs the contributor Spark costing $1.50.
Edit 2: btw it tests a team of agents working together in a special harness and stack. So 20 minutes is for 8 agents essentially. That said everything was built around DS as it was the cheapest to iterate against so even with that advantage the new one struggles.
Yep, and the v4 flash final is about 2.5x slower than preview making it no longer a fast model, in fact slower than Luna and bigger models in many cases.
Spark is actually the interesting one imo. It's significantly better, also significantly faster. If you are ok with letting Meta soak up your data (which DS does too) it's also the same price.
There was only a brief window of time that the opposite was true.
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