https://help.openai.com/en/articles/20001625-understanding-chatgpt-and-codex-usage-limits
Starting in December, Plus, Pro, and Business plans’ usage allowance will be shared across ChatGPT and Codex.
OpenAI confirms that:
- ChatGPT and Codex will share a token-based allowance.
- Instant chat will not consume this allowance.
- More complex tasks and advanced models will consume more tokens.
- Users can check their remaining allowance under Settings → Usage.
- Users can purchase extra credits if they exhaust their allowance.
TL;DR - ChatGPT conversations that use advanced reasoning will compete with Codex for the same allowance. And it will be token based, rather then message / turn based. How many tokens? Fuck you, that’s how many. Want a coder and chat with a non brain dead LLM? Pick one.
So, if you’re thinking of re-upping your OpenCode Go or OpenRouter access (or you’re fortunate enough to have a rig that can run Qwen3.8 27B, Flash or the soon to be arriving Qwen 4’s), now’s the time to fuck off jippity.
Personally, I’m going to ditch it for OR credits and selective use of GLM, Kimi etc for code review. I don’t have the rig for a local GLM :(
Ride was always going to end but fortunately, open weight models are a thing.
Now if I could only run Qwen3.8-27b at not minutes-per-token…
It is, however it’s still pretty slow and not many models find the sweet spot that Qwen did.
I feel most models would be better on a faster GPU as MoE sizes are too big right now. I can get comparable tok/s on large models compared to discrete GPUs but much slower on small sizes.
The curse and blessing of unified memory!
I was considering getting a BC-250 (a cut down PS5) with 16GB unified memory, but at 400GB/s bandwidth.
When they were cheap, it almost made sense. But realistically, I can get similar speeds with a MoE thanks to cpu and system ram, and 16GB is just not enough for anything top shelf (no, Prism MLs Bonsai models don’t count. I don’t care what the benchmarks say - they’re lobotomised).
Still, given the chance to get a Strix on the cheap, I’d go for it. There’s something very nice about a low power box that can run a 120B model at speed.
I am definitely happy with it.
One of the best things is I run some local services and I can have embedding models, decision models, and small LLMs always in memory for my hosted apps. Then it’s only a few seconds to load up a large LLM like qwen 125b or similar.
Kinda wish I’d gone in one those BC250s as well, there are good guides on them now. 400gb/s is pretty good.
Oh, your running some JEVs? How are they? I just can’t figure out a use for em.
Yes, concurrency is amazing. I can pull some tricks with CPU (OCR, TinyBERT, Klein etc) while the GPU runs inference…but it’s not a Strix.
Yeah, the BC-250 had / has real promise, esp with the core unlocks…but like P40s and P100s (server cards) people twigged on “this is cheap and fast”. I can’t get one locally for under $600 now. They use to be $150. By the time you fit it out properly, it’s nearly $1K. Not worth it.
I’m running liquid d1 and clef.
Clef is good, but like 30gb. I mostly use d1 as a reranker type model for search and it’s good for that. I haven’t found many reliable use cases yet for them.
Meanwhile you made me look at prices. JFC.
Yeah, I could sell it for well over twice what I paid.
It’s insane.
I just don’t get it. Like…who’s buying these things in quantities vast enough to charge that much? Oh, I’ve heard Open AI snapped them and Mac Minis up in bulk for “testing” (whatever that means)… but given the cost now… why / who is buying them in quantities that warrant these sort of prices? It’s not as if they’re rare per se. Hell, there might even be a glut relative to actual demand. Yet, those prices…
Human psychology is weird.
I think it’s just the RAM prices? If you’re a reseller or training professionally renting or buying a b100 or something is way more powerful (but 100x the cost)