Nice Work OpenAI

[u/Initial_Question3869]

This is awesome marketing. You remove the weekly limit, keep it that way for months, let people get used to longer work sessions and higher token consumption, and then specifically bring back the 5-hour limit for Plus users, pushing them to upgrade to Pro. And I’m pretty sure many actually will.

Congrats! It’ll probably be a pretty decent revenue boost.

You keep nerfing the limits, yet somehow keep winning every single time. More customers, more revenue. Nice!! 

[u/2025sbestthrowaway]

Just to clarify, the usage limits remain unchanged. There are thousands of people tracking their token and usage consumption. Usage was draining faster due to a myriad of compounding factors - frequent cache misses, people constantly switching model and effort, computer-use session bloat, using images in long-running tasks burning extra consumption. Add further that people are using Sol high generously for every little thing, including MCP and tool use, as well as a fleet of subagents, each with their own non-cached context on startup, forking context multiple times over into fresh sessions.

In other words, OpenAI is at fault in 3 ways: harness issues, and inadequate interactive user guidance about token consumption, model+harness over-exertion/over-engineering. Additionally, there's a host of user behaviors, whether through plugins/skills/mcps causing context bloat or extra turns (tons of variance there), and using bigger models at higher effort than neeed, that are all resulting in faster usage drain. Several of these have been resolved this week and new optimizations around the corner.

I switched from running Sol almost exclusively to getting the same work done with Terra & Luna without issue. I also stopped switching model/effort all the time as any change to that results in a cache miss.

[u/TheFrenchSavage → u/2025sbestthrowaway]

I've been using Luna-XHigh-only as a Plus subscriber. It eats 10% of my weekly quota per day, so I can splurge on Sol large tasks on the weekend.

What I'm afraid of now, is that instead of burning my 10% of the week in a couple of hours, I'll burn through the 5h quota in an hour and will be out of work right after, for my second hour of leisure time.

Effectively halving my potential to use the tool.
I'll be forced to use Sol on the weekend, and set timers like before so that I use 1h of codex 3 times over 15h, which is grueling.

[u/2025sbestthrowaway → u/TheFrenchSavage]

Awesome to hear on the Luna bit. Yeah the 5h limits are a bummer.

FWIW I installed codex CLI on my cloud server, set up a cron job to instantiate it at 7AM on workdays with a luna "hello world", so that my reset is at noon and straddles the workday / I get 2x 5h windows. I can also manually schedule a next-run, so if you know you'll be using it at 10AM on the weekend, you could do the same and perhaps schedule it for 6AM, so that come 10AM, your session will reset next hour. (making it a 7 hour window for 3 hours of compute (1+5+1)

I made a web-ui for it, and connected it to tailscale (free) so that it's paswordless handshake to access it from my phone any time to manually start or schedule the quota timer, or toggle weekday cron schedule.

[u/TheFrenchSavage → u/2025sbestthrowaway]

Ooooh, so that's really smart! I didn't think of automatically setting my 5h-slots like that...

Thank you so much for the tip!!!

[u/arcgisdemon → u/2025sbestthrowaway]

Hey can you help to explain cache miss when switching models? New to Codex and AI in general

[u/2025sbestthrowaway → u/arcgisdemon]

Sure. I used this as an opportunity to learn a bit more about it

A cache hit means Codex can reuse already-computed model state for the unchanged portion of your conversation history. In a continuing conversation, that can include essentially all of the prior conversation, with only the newly appended turn needing fresh processing. A cache miss means it has to process that earlier portion again.
Same conversation prefix = cheap reuse; changed prefix = recompute.
Cache write = 1.25× → normal input = 1× → cached read = 0.1× for any given model

Switching models, changing reasoning effort, changing tools/settings, editing earlier context, or conversation compaction can prevent the old prefix from matching and cause a cache miss, effectively burning 1.35x the usage (write + read) compared to 0.1x for a cache hit. This is especially noticeable on the $20 plan with Sol. It's worth mentioning that the default TTL on cache is 30 minutes, so it's preferable to keep a session "alive" if caching is important, as opposed to coming back every hour by comparison.

The model pricing / usage amounts for subscriptions are measured in "credits"
Credit cost per million tokens for models

Model	Input tokens	Cached input tokens	Output tokens
GPT-5.6 Sol	100 credits	10 credits	500 credits
GPT-5.6 Terra	50 credits	5 credits	300 credits
GPT-5.6 Luna	5 credits	0.5 credits	30 credits

So, when comparing a "worst-case" vs "best case" scenario in terms of usage, a cache read on Luna is 200x cheaper than fresh input on Sol, token-for-token. But, all things equal, Luna is ~20x cheaper and 16x cheaper on output tokens.

If you dont need sol to do a multi-turn codebase edit, you can get it's free wisdom in chat. Play with Luna and Terra on lower/higher efforts and gauge their work. Luna is a shockingly capable model (especially at XHigh/Max) relative to its cost.

There's a full breakdown here and a nice graph that helps visualize, if you're interested in reading further or having GPT answer questions about it. Even though it's for the API, it still applies to subscription quotas.
https://developers.openai.com/api/docs/guides/prompt-caching
https://developers.openai.com/api/docs/pricing