Which is probably why they will merge it. You can get a lot done in chat mode, especially if you connect custom MCPs to it. I can't see that continuing.
Pro messages is like 50 per week for pro 100 and 200 for pro 200.
Regular chat, even on extra high thinking mode, is sort of unlimited. Idk at what point you hit the abuse gaurdrail but its really high whatever it is.
For a company with Open in the name, OpenAI is very opaque about things, there are all kinds of limits and quotas that randomly come and go, but they only show you the meters for a couple.
E.g. codex reviews come from codex quota but also has its separate quota they don’t tell you about. Same with security reviews which has another quota still.
So the chat messages can be sent at several effort levels and from various models including 5.6 and 6 pro. The 6 pro messages are limited and they don’t tell you how many you’ve sent or how many are left. But the 5.6 messages seem to be unlimited, however there could just be a much higher unstated quota.
Its "pure capabilities" are definitely worse than Fable, but I find codex has a much more pleasant style and is in comparison much more generous with its limits.
Ugh, people are still saying the Codex limits are more generous. They're not, Claude's are over 2x higher, have been for months! [1] It's just that Claude uses far more tokens, 2-3x is common. Except sometimes GPT will use just as many or even go into a compact loop and then your quota is gone, little headroom for hard tasks.
That website seems to suggest that Opus 5 spends ~57 cents per task, while GPT 5.6 Sol spends ~49 cents per task? That ratio doesn't feel quite right to me. Artificial Analysis says Opus 5 High costs nearly ~3x as much as GPT 5.6 Sol High for a given task: https://artificialanalysis.ai/models/comparisons/claude-opus...
Yeah I subscribe to both and watch the numbers too, and it drives me nuts
Who wants to actually watch anyways, rather than worry about it my team just created our own harness that prioritize usage + intelligence and assigns work out (and records token usage..)
These exist and are used. The issue is that because they're so much smaller, they're also much worse, so they tend to have lots of false positives while still being easy to circumvent.
This is not what the bitter lesson is about. It's not "don't develop better methods, just scale", it's that those methods which scale best win. LeCun's work is fundamentally about devising a method that scales better with data than LLMs. Agree with him or not about the feasibility of it, but this is fundamentally still a bitter lesson-pilled mindset.
The fundamental issue is that for these systems bigger is essentially always better (if affordable). So if we can squash something like Kimi K3 down to run on a 'normal' system, that just incentivizes devs to increase the model size until once again we're at the limit of what can be run.
This is some bizarre victim inversion. The providers of closed models are the ones who are trying to use regulation to stop their open model competition, not the other way around.
