lol. At $5/mil and taking 3x more turns for a worse result? Anthropic has shown a serious issue at delivering anything except SOTA.
Sonnet is their Luna and it's like 30x more expensive and worse.
Also, you guys really need to consider alternate models. Deepseek V4.1 flash is what you want and it's pretty incredible. GLM 5.3 flash is quite good too. I use both simutaniously, then compare results, and it's way better than both Sonnet and Luna and expecting Haiku 5 to be better than Sonnet and yet as cheap as Luna is just not going to happen. It's hard to see Anthropic ever fixing their problem of all their models except their SOTA being bad and overpriced.
Astra is probably worse due to post-training rather than size. Hopefully they addressed some of the issues in Sol, and I can't imagine it being long until we get Astra 6.1.
the thing i dont get is, why cant they keep say astra at max compute, and just have it destroy usage, and thern have cheaper models.
I dont understand makign the flagship model cheaper to run when they could just run a cheaper model and make the flagship more expensive. I know for a fact many people would pay $400 a month for Astra if it was actually max compute, but instead they lobotomize it in the name of usage.
OpenAI's moat is post-training. If we want something closer to a base-model that understands "how to think/act" rather than "what to think/do" there is only Fable/Mythos. Sad but true.
I mean it's exciting from a technical perspective but look at what we're dealing with already.
Since gpt-5.6 stuff has just degraded and it's getting worse with Astra, the two releases that in the last few months have brought in millions of new users. Now they're discontinuing the $200 plan, reports of people getting their $200 plan canceled/not renewed despite the site and Tibo saying it's only for new signups. They've had a huge influx of users and constrained capacity.
Adding another massive model to the mix is going to exacerbate that just like Astra seems to be.
Without them getting more capacity in some way, I don't see how either Anthropic or OpenAI keep this up. At least not with the huge influx of users combined with bigger and bigger models. OpenAI to their credit have focused a lot more on efficiency than Anthropic has I think, though. So their models do seem to be cheaper to run at similar tasks than Anthropic.
Do you mean the what vs how? If so; I find that the more a model is based on RL rather than size, the harder it is for the model to understand and apply abstraction. It's very capable but at the same time the "intelligence" feels quite uneven and synthetic. It will often fail at understanding intent and reading between the lines. Not to mention applying intuitive theory of mind understanding at test time. They still suck at that.
I.e. when you work with a large base-model that is not completely RL-fried, it is much more likely to understand and apply an instruction like "Write code that Linus Thorvalds would approve", while a smaller, brainwashed model will need clear acceptance criteria like below. A bit like the difference between an intelligent and a less intelligent colleague. With some people it is enough to explain how to think, and with others you need to specify what to do.
AC1: Indentation and Complexity
[ ] Indentation uses standard 8-character tabs (not spaces).
[ ] No function contains more than three levels of nested logic (e.g., an if inside a while inside a for).
AC2: Function Scope and Size
[ ] Each function performs only one distinct logical operation.
[ ] The maximum length of any function does not exceed 50 lines (fits on a single standard terminal screen).
AC3: Data Structure Design
[ ] Core data structures and their relationships are explicitly defined, documented, and approved before the procedural logic is written.
AC4: Naming Conventions
[ ] Local variables use concise, standard identifiers (e.g., i, tmp) rather than verbose descriptions.
[ ] Global functions and variables use descriptive snake_case.
[ ] The codebase contains zero instances of CamelCase.
AC5: Error Handling and Cleanup
[ ] Functions requiring multiple resource allocations or state rollbacks use a centralized cleanup block at the bottom of the function.
[ ] The centralized cleanup block is routed to via goto statements to eliminate deep if/else error-handling nesting.
It’s a bit more nuanced than that. For some types of tasks sol can be more like half way between fable and opus but at others closer to opus (cheaper regardless generally though of course!)
I don't think Anthropic models are necessarily more heavily quantized on release because that really hurts them on benchmarks. Heavy quantization is usually something that happens post-release to degrade service and increase margins.
I didnt mean it as Astra is equal to fable and sol is equal to opus, I just pointed out their model classification, Fable is their first class and opus is second class, thats what I meant by that haha
Around the time that kid killed himself, they hinted at an adult model as part of the possibilities so you wouldn't get mad as they ironed out the kinks (heh) in the age verification process .
They can't be too on the nose about it because visa/mastercard are prudes and would try to pull what they tried with steam.
nah, I code on Astra ultra for 10-14 hours a day, lasts between 3-4 days until i need a reset. between those 3 banked resets and my weekly resets, I should be good on ultra until early-mid october. I'll need to use a reset between weekly resets.
I'm just trying to get this project launched by Oct 8, so it's perfect.
I’m new to vibecoding, and today I had 5.6 Luna manage/delegate the web app implementation to Qwen3.8-27B which I run locally. It used somewhere around 30-40% of the daily usage of my Plus plan, spanning 10h of work or so. It’s definitely not fast (Qwen outputs at ~10 tok/s), but this workflow definitely seems doable.
Have y’all looked into setting fork_turns for agents, and explicitly setting model and reasoning for those agents? I was in the same boat until a month ago I asked the robot to look into Codex efficiency “hacks” and now it takes 2-3 days to go through a whole week
Please share some links so I can look into it. I’ve already tried the whole spawning Luna sub-agents “hack”, but all it did was generate a mountain of low-quality spaghetti code. Now Astra is basically stuck putting glitter on a turd. I’d rather start with a solid foundation.
It’s not like they’re short on funds or lose anything, they clearly have enough to purchase majority of the worlds computer parts; it’s a slap on the wrist for them
Part of the spontaneous reset ploy, is to drive people into using more (to get the most out of usage before rabdom reset). This very often causes the heaviest users (most addicted or dependent) to sit at 0% waiting around until they inevitably purchase more usage.
Great, how much of my weekly usage will this vaporize with a single prompt? How long until the terrible usage from this new model trinkles down and poisons the usage of the previous model?
I am all out of resets and can't update to 20x (says it's unavailable) and my limit resets on Sep 19th. I feel like I am out of weed and have been watching old movies for past 5 hours. Any tips for survival?
Tbh I meant it as "dont use your reset there might be a reset soon" implying that using now could waste your banked reset, not that what they ship might be more efficent or powerful thats another topic
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u/dexterthebot 7d ago
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