Thread

GPT 6.1 Sol: Near-Astra intelligence for a fifth of the price

1066 points · 953 comments · crorella

minimaxir · 1790702170 · focus · HN ↗
> Cached input costs just $0.10 per million tokens—95% less than standard input pricing and 50% less than GPT‑6 Sol’s cached input pricing
This is the actual big announcement. 50% cheaper cache than GPT-6 Sol will get you far more mileage on Codex.
1. TuxSH · 1790702497 · focus · HN ↗
  Exactly half as expensive as Opus 5.5 in every API pricing metric
  1. bigwheels · 1790703039 · focus · HN ↗
    And half as good. I didn't have great experiences with Anthropic models in the past, but Opus 5.5 seems to have turned a major corner. It is churning through tasks significantly more quickly and efficiently.
    Suggest trying it out yourself: Ask for something difficult from GPT-6 Sol and Opus 5.5 and watch what each one does. The difference is stark.
    Edit: Defining "difficult" as a complex coding or systems task (or even series of them in a single prompt).
    Infinity315 · 1790703186 · focus · HN ↗
    I'm not an OpenAI simp, but how anyone can have any opinion on the performance of these models in less than a day - let alone a few hours - is beyond me.
    toasty228 · 1790703298 · focus · HN ↗
    Try it, it's that good compared to openai current offering.
    I get better results and usage our of my $20 claude sub than my $100 openai sub... it's that ridiculous
    copperx · 1790703714 · focus · HN ↗
    [delayed]
    AndrewKemendo · 1790703338 · focus · HN ↗
    Only takes 5-10 minutes to test your favorite one shot comparison prompt.
    squidbeak · 1790703980 · focus · HN ↗
    If 5-10 minutes is enough, you need a more ambitious one-shot goal.
    edgyquant · 1790704201 · focus · HN ↗
    Can you give an example? For me I find that one shot prompts are pretty good it’s only when working with large codebases and complex, multi prompt workflows, that I find the real limitations of models
    AndrewKemendo · 1790708580 · focus · HN ↗
    Yeah the whole Pelican riding the bike is the best obvious one
    [deleted] · 1790703344 · focus · HN ↗
    [deleted]
    colinhb · 1790704025 · focus · HN ↗
    Yeah totally agree, people keep jumping in w/ strong views hours after release, eg: <a href="https://news.ycombinator.com/item?id=49045430">https://news.ycombinator.com/item?id=49045430
    rspeele · 1790704573 · focus · HN ↗
    While I have no experience comparing this brand-new model, OpenAI themselves call it "near-Astra" intelligence. I set Astra and Opus 5.5 independently working on the same large research/coding task in an experimental project (doing NURBS surface modeling stuff). They had the same starting repo state, same task packet, same test suite to try to meet. I have the $100 plan in both.
    Astra used 215% of a week's budget (I burned 2 free resets) and took 13 hours. Opus used 20% of a week's budget and took 20 hours. Both were asked to use lesser sub-agents for implementation grunt work at their discretion (Luna, Sonnet) as long as they manage and review the output.
    The timing comparison is not that interesting because the wall-clock speed mostly reflects how often they ran the (large, slow) test suite, not their coding speed. Although in the past my gut feeling is that OpenAI models do generally respond faster.
    The quality of their implementation was more interesting. There turned out to be a bug in one of the unit tests the agents were trying to pass. Opus interpreted the natural-language requirements from the task packet, found the test bug, and fixed it. Astra tried hard to solve the problem without altering the test suite. In practical terms Opus got much, much farther into a useful implementation. Astra was still stubbing out and faking critical parts of the implementation (B-splines) and since it ultimately couldn't pass the full test suite, finally gave up on its implementation. Astra wrote some useful tooling in the process of its efforts which I ended up integrating into Opus's version of the code, but otherwise its approach was behind.
    Now, this is just one comparison in one domain, and arguably Astra's strict adherence to the tests as-given is a good thing. But Opus wasn't merely loosening the rules / moving the goalposts to pass, it spotted an actual bug, and was more successful at doing what I actually wanted. And the cost difference was Astra-nomical.
    Out of curiosity for an interpretation free from my personal bias, I gave Astra a hint from Opus and permission to change the test in question, which it did, and got a bit farther, but still ultimately didn't produce a working implementation (to be fair, Opus's was not completely working either, but was closer). I then fired up fresh agents to review the two repos. Predictably, an Opus agent thought the Opus-written repo was the better basis to build on, and an Astra agent thought the Astra-written repo was the one to keep. They were not explicitly told which was which nor did the commit trailers say, but I assume they can tell. However, after doing this twice each, I saved the 4 review reports into another folder and did yet another meta-review of the 4 reports, so each would see the arguments and critiques both directions. In this meta-review both Astra and Opus converged on preferring the Opus implementation.
    agar · 1790707976 · focus · HN ↗
    This was a very interesting, informative, and well-written comment (and experiment). Thank you.
    this_user · 1790719544 · focus · HN ↗
    Astra doesn't just burn token at an insane rate, it is also strangely high maintenance when using it. Occasionally, you have to keep prodding it to keep working. Then at other times, it will disappear down some rabbit hole, trying to resolve increasingly hypothetical issues. It feels like you constantly have to keep it on track, while Opus is just churning through tasks.
    rrvsh · 1790728240 · focus · HN ↗
    Yes, I really don't like Astra - 5.6 models seemed to perform at literally the same level with less opaque prose; I guess Astra is great if you're working on insanely hard mathematical problems (or are fooled by its masked sycophancy) but for coding 5.6 seems to have better taste. I hope that they course correct or at least offer models that do better for coding, or even better that this oligopoly ends
    chaostheory · 1790720955 · focus · HN ↗
    [delayed]
    rspeele · 1790722059 · focus · HN ↗
    I strongly agree!
    My biggest conclusion from this test was: the most efficient use of my weekly Astra budget is as a reviewer/consultant for work done by Opus. I don't have Astra write much code right now, but I do have it reading a lot of what Opus writes. Of course with the way the AI landscape shifts the balance could be the exact opposite 2 weeks from now.
    Seeing how each model preferred its own flavor of code shows that, even from a "blind" fresh context, a same-model reviewer will still often look at the work of another incarnation of itself and go "yep that's how I woulda done it" and not be as likely to realize that there was an alternative path or implicit assumption/mistake in the work.
    phoghed · 1790705158 · focus · HN ↗
    I think it’s one of the reasons why you often see people decrying the lessening capabilities of the models a few weeks later, despite there being 0 proof of any changes, and evidence of the models staying the same from sites that track it.
    They form these super strong opinions after a few prompts, then face reality over time.
    People have been talking about how good whatever model is at “complex” tasks since the beginning, never mind that all of those models are now outperformed by Luna which many people consider unusable for complex work.
    beering · 1790705532 · focus · HN ↗
    They’re comparing against the previous model, not the newly released one (6.1). Why do that on a thread about the new model, I don’t know.
    ex1fm3ta · 1790705842 · focus · HN ↗
    benchmarks.
    mmis1000 · 1790703598 · focus · HN ↗
    For my personal experience, antropic model have better use experience except for 4.7 and 4.8 though. 4.7 and 4.8 feels like expensive downgrade of 4.6 to me (I didn't know why these two should even exist)
    krzyk · 1790705866 · focus · HN ↗
    For me Anthropic models from 4.7 to 5 including where bad and ate tokens like crazy. Task delivery was worse than GPT 5.6 and token usage was 2-3x higher.
    Looks like 5.5 is the new 4.6
    jauntywundrkind · 1790703782 · focus · HN ↗
    A pity I have to use claude code to try this, that I can't use the tools I know and love and have built around (opencode).
    dotancohen · 1790704383 · focus · HN ↗
    > Ask for something difficult from GPT-6 Sol and Opus 5.5 and watch what each one does.
    That's far too vague. I found Opus to be terrific at coding, but human text just seems so robotic with it. OpenAI models used to be the prototype for robotic text, but lately I've been finding them much more natural. What is "something difficult" in your workflow?
    peterbell_nyc · 1790705087 · focus · HN ↗
    You HAVE to have a set of personal evals for each class of task you want to use models against at scale so you can test plausible candidates and compare output on your work against your evals.
    There is way too much subtlety in what does and doesn't work for a given problem, context/prompt, tool set and eval. I can tell you Fable is generally better than Haiku, but comparing similar tiers really does depend on your exact context.
    Starlevel004 · 1790705962 · focus · HN ↗
    > OpenAI models used to be the prototype for robotic text, but lately I've been finding them much more natural.
    This was the biggest thing I noticed in the 6 models; their conversational prose is dramatically less grating.
    notatoad · 1790718874 · focus · HN ↗
    My side by side evaluation this week was to build a tool for mounting my app’s UI components in a headless chrome and feeding mock data into them, for the purpose of taking screenshots for help docs. Not super complicated, but a real task I needed done.
    I have the task to codex first, it took a couple back and forth prompts to define the project and then it worked for a bit and to took a couple more prompts before I decided it was good enough - not perfect, but close. It re-implemented some wrapper components in a simplified way that lost some of the UI, but it would work.
    Opus 5.5 took the same prompt with no back and forth, it just went off a built a tool that takes pixel-perfect screenshots of exactly what my app looks like.
    TuxSH · 1790704880 · focus · HN ↗
    > Suggest trying it out yourself: Ask for something difficult from GPT-6 Sol and Opus 5.5 and watch what each one does. The difference is stark.
    Oh yes, I know GPT-6 Sol is ... quite not up to par. At least it's not as bad as GPT-5.6 Terra I suppose.
    sobiolite · 1790705440 · focus · HN ↗
    Are you comparing Opus 5.5 with GPT-6 Sol or GPT-6.1 Sol? Because they are different models.
    beering · 1790705601 · focus · HN ↗
    This news and thread is about 6.1 Sol, not 6 Sol. You haven’t even had time to do a fair comparison yet.
    rrvsh · 1790728076 · focus · HN ↗
    I found that 6 Sol is dogshit; have you tried o5.5 vs. 5.6 sol? curious to hear if your experience is still the same in that regard
    [deleted] · 1790741988 · focus · HN ↗
    [deleted]
  2. dom96 · 1790704720 · focus · HN ↗
    Based on my benchmark[1] it is the same price as Opus 5.5 and just as capable.
    1 - <a href="https://bench.killswitch-lang.org" rel="nofollow">https://bench.killswitch-lang.org
    zeroonetwothree · 1790709807 · focus · HN ↗
    Opus 5 scoring higher than 5.5 makes me question of the value of this benchmark to real world usage
    dom96 · 1790710300 · focus · HN ↗
    Well, it is genuine.
    Opus 5.5 fails the "understanding" tasks which Opus 5 passes. I feed it a script which takes two numbers and prints the max of the two numbers. Opus 5.5 thinks it prints 1/0 instead of the max numbers. Opus 5 gets it right.
    Here are the outputs from both: <a href="https://gist.github.com/dom96/b5bce82b6e6c1ebd5271ed70ad941b49" rel="nofollow">https://gist.github.com/dom96/b5bce82b6e6c1ebd5271ed70ad941b....
    Looking at that Opus 5.5 fails to deduce that the "hack statement" is actually an if statement in disguise, but Opus 5 gets this right. I feel like this is a pretty good test and shows Opus 5's greater intelligence.
2. joshstrange · 1790703602 · focus · HN ↗
  > 50% cheaper cache than GPT-6 Sol will get you far more mileage on Codex.
  Cache doesn't help you much when you are compacting every 5 minutes...
  I was shocked at how quickly I ran out my $100/mo subscription with a single agent (sol medium).
  1. codewithcheese · 1790705772 · focus · HN ↗
    you can config codex to compact at a higher context limit
  2. redox99 · 1790706261 · focus · HN ↗
    If you run out of sol medium with $100 you're doing something wrong. Astra destroys your usage, I get 1 day of usage with Astra, but 6 sol is almost unlimited and I only use xhigh.
    shimman · 1790709072 · focus · HN ↗
    "You're holding it wrong." Is hardly a retort from a real paying customer having problems with their paid services.
    This is why these companies are struggling to make money, they're chastising their customers just like they've been chastising the human race.
    trio8453 · 1790718639 · focus · HN ↗
    > "You're holding it wrong." Is hardly a retort from a real paying customer having problems with their paid services.
    It's very appropriate in the cases when you're holding it wrong. The fact that you're paying doesn't mean that you can't make mistakes or waste resources.
    shimman · 1790780715 · focus · HN ↗
    I don't find it appropriate at all, especially regarding technology that workers deeply hate and are skeptical of.
    If this is how you want to get people on your side, I can understand why the entire country/human race are against these companies.
    trio8453 · 1790800039 · focus · HN ↗
    Sides? Hate? This is all very emotional. Try to put the facts down plainly and see how ridiculous it is --
    It's a product and if you're using it incorrectly, we can either
    1. say so
    2. pretend that you don't so to get/keep you on "our side"? or not say is because you're skeptical or hate it? (how does that last bit even follow logically?)
    How is 2 better in any way for anyone involved?
    crossroadsguy · 1790832995 · focus · HN ↗
    [delayed]
    Anonasty · 1790759784 · focus · HN ↗
    Literally the prompting and task definition is main variable how LLM's performs. There are literally millions of examples of vibe coders and new AI adopters who run out of tokens since they don't know how the LLM's work.
    jorblumesea · 1790713369 · focus · HN ↗
    yeah I use sol constantly and have done maybe $15 of spend in the past week. it's solid and cheaper. this is at least 4-5 investigations, prs, whatever per day.
    Aeolun · 1790718129 · focus · HN ↗
    It’s only nearly unlimited if you haven’t just used a banked reset. After a banked reset your weekly usage gets cut by about 80% (not the week you need to wait to get your normal limits back though). ChatGPT has given me a really good reason to cancel.
    threecheese · 1790722893 · focus · HN ↗
    Can you elaborate? I've been getting great usage out of my $200/mo plan, and thought I'd try a reset (first time) which was expiring just for giggles. Am I going to get only 20% of it effectively?
    I overused Astra in order to drain my weekly, figuring I'd have the reset. (not wastefully, I did get more work done)
    Aeolun · 1790763407 · focus · HN ↗
    I can’t say what will happen to you, but yes, that has been my experience. It is better to wait for your normal full limit to return, because if you use a banked reset you get only 1/5th of the tokens but you still have to wait the full week afterwards for it to reset. 20% would be fine if it didn’t also reset the date your normal reset fires.
    seunosewa · 1790784586 · focus · HN ↗
    Banked resets do expire if you don't use them, so use them anyway.
    nkmnz · 1790786738 · focus · HN ↗
    Did you “earn” that reset on a lower tier?
    edot · 1790834998 · focus · HN ↗
    Proof? Like, do you have logs or something? Not calling you a liar but this seems not correct based on my usage.
    mattkenefick · 1790774037 · focus · HN ↗
    How do you get 1 day of usage with Astra?
    I create a lot, but I can make a full month with Astra on the current Pro plan. What are you doing to spend that much?
    redox99 · 1790774753 · focus · HN ↗
    Currently spending a lot of tokens programming the AI for my videogame.
    1 day is kind of generous, it probably lasts like 12 hours of running non stop.
  3. apitman · 1790708235 · focus · HN ↗
    You have a lot of control over compaction, both directly by changing compaction settings, and indirectly by how you structure your codebase/docs so agents use less tokens.
  4. antonvs · 1790708384 · focus · HN ↗
    Try Gemini. It’s so cheap I often use my personal AI Pro account for corporate work, and most of the time it doesn’t matter.
    ChickeNES · 1790708936 · focus · HN ↗
    Gemini is dumb as hell though, it's not like for like
    Foobar8568 · 1790711842 · focus · HN ↗
    cheerleader hallucinating agent. That's Gemini.
    Marha01 · 1790712388 · focus · HN ↗
    Gemini 3.8 Flash is actually pretty good.
    antonvs · 1790882121 · focus · HN ↗
    I doubt you've tried it recently, or perhaps you confused the search engine version of Gemini for the frontier models.
    I've been using Gemini on development of a DNN training pipeline, and there's no way you can describe it as "dumb as hell". That description just makes it clear that you're not talking about the technical capabilities of the models, but about some sort of fanboy comparison from a parallel hype universe.
  5. _davide_ · 1790709669 · focus · HN ↗
    As a reference i burn 1% percent for every 40 minutes of sol on average
  6. onlyrealcuzzo · 1790710720 · focus · HN ↗
    If you're compacting every 5 minutes, you have a workflow problem - period.
    No LLM will be cost effective if it's compacting this often. You have to find a way around it.
    ngruhn · 1790711655 · focus · HN ↗
    Context window is only 275k or something. And honestly compaction is not that bad in Codex. I often don't even notice I went through 5 compactions in a session.
    sally_glance · 1790717045 · focus · HN ↗
    Same for me, I started wondering if maybe workflows using compaction instead of clear + markdown memory would be more efficient. Writing a plan or tasks to a file often has the next session repeat part of the exploration, compaction seems to keep most relevant context.
    SyneRyder · 1790717378 · focus · HN ↗
    Sounds like that's the problem then, 275k is a tiny context window. I regularly have sessions that go to 450k or even up to 700k for an unattended overnight Claude Opus session.
    Apparently OpenAI makes you manually setup their 1 Million context window, and it seems to be only documented on X:
    <a href="https://x.com/thsottiaux/status/2089082893804896524" rel="nofollow">https://x.com/thsottiaux/status/2089082893804896524
    There's at least a forum thread about it here:
    <a href="https://community.openai.com/t/why-does-codex-report-a-258-400-token-context-window-for-gpt-5-6-sol/1394346/5" rel="nofollow">https://community.openai.com/t/why-does-codex-report-a-258-4...
    gf000 · 1790721096 · focus · HN ↗
    But that 250k context worth way more than 1M in terms of how well it's utilized, so actually I do like codex trying to keep you at that sweet spot.
    rrvsh · 1790727974 · focus · HN ↗
    Its really not tiny; you can't compare Claude to GPT, they have honestly diverged enough that as the other reply said, 256k GPT is about equal to 1M Claude. The compaction is slightly annoying, and you can turn it up to 1M as you said if you truly need everything in context, but otherwise it's perfectly serviceable
    kaoD · 1790784071 · focus · HN ↗
    [delayed]
    bjord · 1790763006 · focus · HN ↗
    > unattended overnight claude opus session
    yes, exactly
    SyneRyder · 1790763872 · focus · HN ↗
    Not sure I understand if this was meant as a slight against Claude? Or agreement?
    These are often my best sessions - they're unattended overnight, because by then we have the specification figured out, and I can just leave Claude to build out the rest, making good choices if it does find gaps in the spec. I regularly go to sleep & wake up to an entirely new application completed. Claude never uses compacting in my sessions.
    I haven't used GPT as much as I should have, so I'm prepared to be incorrect & out of date. It just intuitively feels like I wouldn't get the same from a 275K context window - maybe it uses lots of subagents? Even Deepseek & GLM have 1 Million context windows now, so it "feels" strange for people to actually prefer the 275K window. But that's just my intuition.
    bjord · 1790796534 · focus · HN ↗
    neither, actually, just that unattended "oneshot" sessions are incredibly token inefficient
    if you talk about them (in which you lean on an LLM as a sort-of independent employee) and conservative, chunk-based usage (in which you use the LLM as more of an extension of yourself), you're comparing apples to oranges
    a predefined spec obviously reduces that gap but how much is highly dependent on the level of detail
    jeremyjh · 1790721571 · focus · HN ↗
    I don’t usually have a problem doing a complete task in that context size. OMP does make a lot of use of rewind which may be helping - basically forks itself and sends back a summary after a long tangent. Coding takes use a Luna max agent.
    I’ve also found compaction not to be a problem when it does happen.
    threecheese · 1790722953 · focus · HN ↗
    How do you trigger this? I've been messing with OMP lately for funsies.
    jeremyjh · 1790725222 · focus · HN ↗
    Its in settings under Tools->Checkpoint/Rewind. I don't know why its not enabled by default.
    onlyrealcuzzo · 1790721660 · focus · HN ↗
    If it's compacting every 5 mins, you're going to notice it in your cache miss ratio and your costs...
    rrvsh · 1790728012 · focus · HN ↗
    It doesn't - try it first
    Benjamin_Dobell · 1790728918 · focus · HN ↗
    The context window is configurable. I've been using ~600k for months. No, not API pricing, on a Codex sub.
    ~/.codex/config.toml
    model = "gpt-6.1-sol" model_context_window = 700000 model_auto_compact_token_limit = 630000
  7. AmazingTurtle · 1790711222 · focus · HN ↗
    you can actually leverage 400k and 1M contexts in codex with very little code changes to the harness. note that excess context past the.. 250k or 400k mark (i don't remember) is charged at 2x the price.
  8. manmal · 1790714234 · focus · HN ↗
    Your tool calls (MCPs?) are very likely too wasteful. Apply some filtering logic on the offending tool’s output. Either a wrapper CLI, or just tell codex how to filter.
  9. Gareth321 · 1790753414 · focus · HN ↗
    > Cache doesn't help you much when you are compacting every 5 minutes...
    It's crazy on Codex. I sometimes get just 2-3 turns before it compacts. It has forced me to use persistent project documentation for everything. Maybe that's not a bad thing but unless it reads all the documentation after every compaction (and uses half its cache), it goes off the rails. By comparison, Opus 5.5 is a breath of fresh air. It takes FAR longer to hit the cache limit and that means it keeps useful information in working memory far longer. I think this alone has resulted in a massive productivity and efficiency increase for me.
    RugnirViking · 1790756222 · focus · HN ↗
    iirc you can still turn the compaction limit up in codex, though they don't make it easy. It costs way more when you use "large context" though, more than the ~256k that codex allows by default. You can also use the large context via the api directly
  10. exfalso · 1790770098 · focus · HN ↗
    what. I use Astra xhigh, sometimes max, never ran out of tokens on the 100$ thing. I'm using pi though which is by definition harder better faster stronger than claude code/codex.
  11. jmalicki · 1790777652 · focus · HN ↗
    Use more subagents.
    The longer your chat gets, the slower and more expensive it gets.
    Subagents are expensive but they scale way closer to O(n) than O(n^2).
    Have some agents make bug reports/feature requests/roadmaps (linear is very AI friendly), others coordinate, others work on grinding out an individual ticket.
    If there is a good ticket-level description, it's a waste of time IMO to have a main agent do it, that should be an agent with fresh context that will do it better faster (the shorter the context, the better models are at using the context they're given).
    jaktet · 1790781658 · focus · HN ↗
    Subagents will inherit the context window at the point in which they are spawned, but it sounds like you're more referring to orchestrating/conducting/managing multiple agents?
    jmalicki · 1790784559 · focus · HN ↗
    Both... even subagents inheriting the context window doesn't cost a huge amount if the context window was never that large, but yes orchestrating/conducting/managing multiple agents is even better though higher thought cost (but the newer claude agents are really good at this in my experience, part of why I am using Claude a lot lately despite the models being more expensive that ChatGPT's for the same performance when taken alone).
    Whenever I see my main agent do a compaction, that to me is a clear sign I didn't have it delegate bounded tasks enough.
    Still, I see no evidence Codex or Claude Code inherit full context of the main agent in subagents, I've always seen them be prompted, but this is something high priority on my list of unknowns to understand better...
  12. KetoManx64 · 1790931880 · focus · HN ↗
    Do you just keep one conversation going for all projects? That's the only way I've seen other people in my company burn through all their tokens.
    Everyone else that uses memory files and a new conversation for each new sub project/feature rarely hit their weekly allotments.
3. verdverm · 1790703869 · focus · HN ↗
  cache is typically 10%, is this OAI setting a new level at half, 5%?
  1. crazylogger · 1790704723 · focus · HN ↗
    The backdrop being deepseek offering 1% (I remember it was ~1% when 4-pro first came out early this year - 4-pro is now removed) / 2% (current for 4.1-flash).
4. sscaryterry · 1790704326 · focus · HN ↗
  [dead]
  1. JimDabell · 1790704596 · focus · HN ↗
    > most people, get hardly a days usage out of a 20x account
    This is not even remotely true.
    peterbell_nyc · 1790704927 · focus · HN ↗
    This is the distribution of usage. Spin up a bunch of loops or fire a semi-autonomous factory at a project and it's pretty easy to blow through a 20x account in a few hours if you can afford the sandboxes, CI and other infra required.
    If you're running 2-3 parallel agent session with a few sub agents and waiting for you to prompt them, you'll have a very different experience!
    JimDabell · 1790705223 · focus · HN ↗
    > Spin up a bunch of loops or fire a semi-autonomous factory at a project and it's pretty easy to blow through a 20x account in a few hours if you can afford the sandboxes, CI and other infra required.
    This is a tiny minority of people, not “most people”.
  2. user43928 · 1790704747 · focus · HN ↗
    It's obviously true.
    With the 80% price cut, this is competitive with Opus 5.5 despite the subscription downgrade.
    Additionally, it was said that existing 20x subscriptions retain the higher limits for some time.
    I have seen you make these immature accusations that users here are OpenAI employees multiple times today.
    sscaryterry · 1790705168 · focus · HN ↗
    It is not obviously true. Please provide real proof. OpenAI's customers are tired of their BS.
  3. minimaxir · 1790704777 · focus · HN ↗
    if an openai employee is reading this plz hire me i am unemployed and i need a job
    (Usage limits are entirely dependent on what you're doing with them. If you're not running it on 1 million LoC codebases you can get a lot of mileage out of even a 5x account particularly with the recent cheap models)
5. vcryan · 1790772096 · focus · HN ↗
  People's volume and approach varies. I'm a happy customer and I use my entire double max subscription on planning and analysis and have other models doing all my implementation work because I would burn through my subscription in a day or less. It's difficult to calculate, but I'm something like 10-20 billion token per week consumer and I can't use a US-based model to do this volume of implementation work.
  Also, a lot of this work is verification to ensure that AI generated code does what is intended and is safe to merge and deploy. That verification work is critical and uses a lot of tokens.
6. pvab3 · 1790776964 · focus · HN ↗
  gpt 6 Sol was already supposedly better and 50% cheaper than 5.6 Sol right? I didn't understand why they were keeping 5.6 Sol

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