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The limits for the $20/month plan can be reached in 10-20 minutes when having it explore large codebases with directed. It’s also easy to blow right through the

by Aurornis 11mo ago

The limits for the $20/month plan can be reached in 10-20 minutes when having it explore large codebases with directed. It’s also easy to blow right through the quota if you’re not managing content well (waiting until it fills up and then auto-compacting, or even using /compact frequently instead of /clear or the equivalent in different tools). For most of my work I only need the LLM to perform a structured search of the codebase or to refactor something faster than I can type, so the $20/month plan is fine for me. But for someone trying to get the LLM to write code for them, I could see the $20/month plans being exhausted very quickly. My experience with trying “vibecoding” style app development, even with highly detailed design documents and even providing test case expected output, has felt like lighting tokens on fire at a phenomenal rate. If I don’t interrupt every couple of commands and point out some mistake or wrong direction it can spin seemingly for hours trying to deal with one little problem after another. This is less obvious when doing something basic like a simple React app, but becomes extremely obvious once you deviate from material that’s represented a lot in training materials.

sheepscreek 11mo ago
Not for Codex. Not even for Gemini/Antigravity! I am truly shocked by how much mileage I can get out of them. I recently bought the $200/mo OpenAI subscription but could barely use 10% of it. Now for over a month, I use codex for at least 2 hrs every day and have yet to reach the quota. With Gemini/Antigravity, there’s the added benefit of switching to Claude Code Opus 4.5 once you hit your Gemini quota, and Google is waaaay more generous than Claude. I can use Opus alone for the entire coding session. It is bonkers. So having subscribed to all three at their lowest subscriptions (for $60/mo) I get the best of each one and never run out of quota. I’ve also got a couple of open-source model subscriptions but I’ve barely had the chance to use them since Codex and Gemini got so good (and generous). The fact that OpenAI is only spending 30% of their revenue on servers and inference despite being so generous is just mind boggling to me. I think the good times are likely going to last. My advise - get Gemini + Codex lowest tier subscriptions. Add some credits to your codex subscription in case you hit the quota and can’t wait. You’ll never be spending over $100 even if you’re building complex apps like me.
- Aurornis 11mo ago
  > I recently bought the $200/mo OpenAI subscription but could barely use 10% of it This entire comment is confusing. Why are you buying the $200/month plan if you’re only using 10% of it? I rotate providers. My comment above applies to all of them. It really depends on the work you’re doing and the codebase. There are tasks where I can get decent results and barely make the usage bar move. There are other tasks where I’ve seen the usage bar jump over 20% for the session before I get any usable responses back. It really depends.
  - selcuka 11mo ago
    Not the same poster, but apparently they tried the $200/mo subscription, but after seeing they don't need it, they "subscribed to all three at their lowest subscriptions (for $60/mo)" instead.
    - Aurornis 11mo ago
      > but apparently they tried the $200/mo subscription, but after seeing they don't need it This is why it’s confusing, though. Why start with the highest plan as the starting point when it’s so easy to upgrade?
      - 1over137 11mo ago
        Because you’re rich?
        sheepscreek 11mo ago
        Not rich. I pay in Canadian dollars :( I’m just a simple dude trying to optimize his life.
  - sheepscreek 11mo ago
    I got it to try Atlas, their agentic browser, before it was open to Plus users. I convinced myself that I could use the additional capacity to multi-task and push through hard core problems without worrying about quota limits. For context, this was a few months ago when GPT 5 was new and I was used to constantly hitting o3 limits. It was an experiment to see if the higher plan could pay for itself. It most certainly can but I realized that I just don’t need it. My workflow has evolved into switching between different agents on the same project. So now I have much less of a need for any one.
deleted 11mo ago
[deleted]
stuaxo 11mo ago
You'd think local models could explore a codename and build up a knowledge graph of it they could use to query it. It could take longer, but save your subscription tokens.
JamesSwift 11mo ago
That has not been my experience with sonnet, and even so it is largely remedied by having better AI docs caching the results of that investigation for future use.

sheepscreek 11mo ago

Not for Codex. Not even for Gemini/Antigravity! I am truly shocked by how much mileage I can get out of them. I recently bought the $200/mo OpenAI subscription but could barely use 10% of it. Now for over a month, I use codex for at least 2 hrs every day and have yet to reach the quota. With Gemini/Antigravity, there’s the added benefit of switching to Claude Code Opus 4.5 once you hit your Gemini quota, and Google is waaaay more generous than Claude. I can use Opus alone for the entire coding session. It is bonkers. So having subscribed to all three at their lowest subscriptions (for $60/mo) I get the best of each one and never run out of quota. I’ve also got a couple of open-source model subscriptions but I’ve barely had the chance to use them since Codex and Gemini got so good (and generous). The fact that OpenAI is only spending 30% of their revenue on servers and inference despite being so generous is just mind boggling to me. I think the good times are likely going to last. My advise - get Gemini + Codex lowest tier subscriptions. Add some credits to your codex subscription in case you hit the quota and can’t wait. You’ll never be spending over $100 even if you’re building complex apps like me.

Aurornis 11mo ago
> I recently bought the $200/mo OpenAI subscription but could barely use 10% of it This entire comment is confusing. Why are you buying the $200/month plan if you’re only using 10% of it? I rotate providers. My comment above applies to all of them. It really depends on the work you’re doing and the codebase. There are tasks where I can get decent results and barely make the usage bar move. There are other tasks where I’ve seen the usage bar jump over 20% for the session before I get any usable responses back. It really depends.
- selcuka 11mo ago
  Not the same poster, but apparently they tried the $200/mo subscription, but after seeing they don't need it, they "subscribed to all three at their lowest subscriptions (for $60/mo)" instead.
  - Aurornis 11mo ago
    > but apparently they tried the $200/mo subscription, but after seeing they don't need it This is why it’s confusing, though. Why start with the highest plan as the starting point when it’s so easy to upgrade?
    - 1over137 11mo ago
      Because you’re rich?
      - sheepscreek 11mo ago
        Not rich. I pay in Canadian dollars :( I’m just a simple dude trying to optimize his life.
- sheepscreek 11mo ago
  I got it to try Atlas, their agentic browser, before it was open to Plus users. I convinced myself that I could use the additional capacity to multi-task and push through hard core problems without worrying about quota limits. For context, this was a few months ago when GPT 5 was new and I was used to constantly hitting o3 limits. It was an experiment to see if the higher plan could pay for itself. It most certainly can but I realized that I just don’t need it. My workflow has evolved into switching between different agents on the same project. So now I have much less of a need for any one.

deleted 11mo ago

[deleted]

stuaxo 11mo ago

You'd think local models could explore a codename and build up a knowledge graph of it they could use to query it. It could take longer, but save your subscription tokens.

JamesSwift 11mo ago

That has not been my experience with sonnet, and even so it is largely remedied by having better AI docs caching the results of that investigation for future use.