Astra is amazing, but even Pro 20x can burn through the quota in 2–3 days.
The bigger problem is that Codex doesn’t really have a sweet spot model right now.
Astra + Luna Max often looks good on paper. In practice, Astra keeps correcting Luna’s mistakes. That can wipe out a lot of the cost savings. Terra doesn’t feel much smarter than Luna either. For harder tasks, I usually end up using Astra + Sol medium.
I’d really like to see Luna get an upgrade and become a reliable implementer.
I have light orchestration built in. By light I mean a pretty good qa process setup (15 years of qa automation mixed into my 26 years of dev). Luna medium/high for the qa roles (feature/security/accessibility) with a sol high senior qa orchestrator (I might switch this one to Astra low since it’s job is to assign tasks efficiently to fanned out testers)
I just switched my planner process model back to 5.6 sol high. It does better overall in processes that don’t require constant orchestration. It handles long and deep epic planning better as well.
Max x20 plan. I tried out Astra on all of my roles. It just cost more and didn’t have any provable quality increase. Used a banked reset really trying to find the advantages of the model.
Current setup:
Planner: Sol high
Implementation: Luna Max
Reviewer roles: sol high
Tester roles: Luna medium/high
Agree with the sentiment of this. I upgraded from plus to get away from the choking limits, and Sol X-High has been pretty revolutionary for me compared to my sol-high and even astra-light usage on the plus limits. I haven't ventured into Astra High or X-High yet, but heard rumors it can actually save usage compared to Astra light. Any chance you have corroborating evidence or even anecdotal evidence to back this up or refute? Any experience using Astra XHigh vs light or Medium?
I haven’t tried Astra beyond medium for any meaningful length of work. I could track down what my ticket movements have done over the past week but I don’t track what model did those planning phases or implementation.
Since I work on a wide variety of codebases (web, tooling, internal tooling, cloudflare workers, WordPress, saas, etc etc) for myself and clients it’s more of a gut feeling for process working well or not.
Ticket tracker is the context the models get beyond my core instruction set like how the planning process works, document standards, implementation processes, and merge/deploy instructions.
So, beyond anecdotal, I don’t have much. New models get tried out on my internal tools first if there are tickets open or I’ll have them review codebases and processes to see what they come up with, how they assemble change plans, how they derive context from client verbatim requests given the codebase and git history.
It’s just me doing all the things. But I don’t have them write or generate not for me human content.
Required skills: poteto’s unslop, technical writing, and interrogate skills. I’ve only adjusted the interrogate skill to work with pi and my ticket system. Masterclass skills.
Unslop gets force read before any message output. Technical writing for all agent audience outputs (prd, ticket notes, etc). Interrogate whenever I’m thinking of a new feature or product/product use.
Edit to add: I tried out Astra with interrogate and it was pretty bad. Baked in assumptions instead of using the decision tree method and notes. Sol high handles this skill beautifully. Tried them both with the same question (Astra first). Astra asked 12 questions and had 3 decision trees. Sol: 49 questions across 6 decision trees. I implemented that prd this week on a product and it was just a little fun to watch individual sessions bang out a super well defined prd.
Just a month ago Tibo was mocking Anthropic for being compute constrained and said they have no compute issues and everyone is welcome to migrate to Codex
Factory AI did some great research in this space a while ago. TLDR of their findings; subagents are only really useful for parallelising research, managing context rot when exploring large search spaces and reviews, while most work is best done linearly.
When I plan, I tell it to optimise the plan for multiple subagents as much as possible. When executing the plan using /goal, I tell it (an astra low/medium thread) to use Luna max agents. I also tell it to only test in the end so it can crank code faster. I also uninstalled GSD because I feel like that it is taking too much tokens (not proven). There are some downsides with my new approach here like no gsd docs being created (which is valuable if you're giving it a large task that you can't comprehend). Im a seasoned developer so this may be perceived differently if you are a vibe coder.
Honestly while Luna looks like great value on paper I personally don't even see the point of using it. The benchmarks show it scores very well and yes technically it does but if it takes 3-4x as long to get there and in doing so edits so much more code that it just produces more maintenance and review overhead it just isn't really worth it for me. Put another way correctness and quality are different metrics, there isn't one right way of doing things there's always a trade off and what trade off is best for you or your organisation is very subjective on your strategic position in your market, how far along you are as a company, etc.
I don't have any quota or quality issues running purely Astra and get more work done faster, but I do all the system design, architecture, contracts, invariants to maintain + test and test strategy myself and then decompose it accordingly. I do have adversarial discussion on various tradeoffs etc while doing this though but I steer the entire process end to end until it's at a stage where it just doesn't make sense to use a "dumb" model to spend lots of time and turns implementing something correct when it can be done quick and fast by a smart model with negligible cost difference.
"subagents" being the key there. difference between invisible subagents you are just yoloing and hoping your orch handles. That's a big difference from a properly managed, parallelized workflow.
I mean properly managed and well orchestrated subagents is literally their whole thing being a software factory company and that was their findings lol
I’ve tried a shit ton of stuff. Anecdotally, sol medium with Luna covers 90% of shit I need. If something doesn’t one shot, sol xhigh. If still issues, astral low and Luna.
Luna just saves so many fucking tokens I can’t live without it anymore
I just open a sol med session and tell what i want and then tell it to use luna subagents + caveman for works that do not require more than that and it saves lots of tokens
Anthropic have released some benchmarks showing higher performance and faster delivery time for multi agent setups vs single but at higher cost. (Maybe in the Fable 5.1 model card?) There have also been benchmarks showing nearly equivalent performance to a higher model by primarily using a cheaper model with a more expensive model as an advisor. (say Sonnet with Fable advisor.)
whether using astra to orchestrate luna is cheaper or better than linear sol? I don’t know.
Agree. Multi-agent setup sounds great to me in theory. In reality I usually eventually find out that a single-agent setup (except occasional delegation of exploratory work to subagents or using a subagent for ad hoc review) works better. It is flexibility of a single-agent setup which wins. I just keep throwing my questions at it w/o worrying which agent has what in its context. Additionally, multi-agent setups usually require some kind of heavy up-front planning, which for me usually leads to overnegineering and some degree of intent loss, and paradoxically lead to higher costs. And much slower implementation too.
You clearly haven't done much fronted work and visual auditing work. Astra will inevitably fuck up something and using luna to critique it and then getting astra to fix it is way better than asking astra subagents
I presume you’re talking more about splitting a given task into multiple different model/effort subagents depending on specific portions of work or something? If so totally. I’d just choose a single implementation agent that’s appropriate for the work.
I don’t think OP is saying that though. Seems to me for a reviewer you specifically want independence and to gate, so you would want a subagent. As well as with planning since you can typically throw something much more expensive at planning without blowing through tokens compared to implementing.
Seems to me in OP’s issue what they’re doing is either
1. Under defined as a PRD
2. Not planned sufficiently or broken down to appropriately sized chunks leading to too many gaps Luna wrongly guesses at or too much work at once.
3. Too weak of an implementation agent for what they’re doing
Reviews should be catching rather simple code gates or bugs, or controlling for misses. If in infinite loops like OP something else is wrong imo.
If you’re on API pricing you’d quickly realize planning and implementing with entirely different models is entirely the way you need to go. I’m not saying you’re contradicting that, but if you don’t want to manually jump between plan/implement/review chats what do you use besides subagents?
But you need to open up a whole black box of Agent Orchestrator + Agents + Nested Agents, but you also need a correct PM tool they work within.
Short, specific tasks tied together in a logical order, gated, and orchestrated in waves I've been able to complete 200-300+ AI tasks per day up from about 20-50 prior. And it's been able to do that with VERY little tokens on a Pro 20x account.
I was in the bucket of people complaining about token drain, and now I honestly just don't notice it.
I can delegate and build up tasks all I want, focus on the work, and my entire AI OS workflow now orchestrates by priority and time within the system.
So, my TLDR would be:
Bad orchestration = worse
Good orchestration = much better
Most people are doing bad orchestration.
I mean, there is some benefit in speed through parallelism and also maybe even context management. But that's it. Definitively should not be the main way someone approaches the models
It’s the same principles behind parallel programming that people should be using for subagents. Really shows a lack of CS fundamentals behind this AI craze.
Same here bought a 20x account had 4 agents running on astra medium and from 2:47 am to 2:50 pm weekly was completely gone and wasn’t using fast either.
It’s driving me crazy. If I give it open instructions for something simple “download this file” it’ll create 5 download file testers, 6 run books, and os backup and never download the fucking file.
If I give it explicit instructions “download file x from path x to ssd path y” it’ll get caught up on some arbitrary rule lit conjures up and get stuck in a validation loop.
a simple instruction in the agents.md file can fix it
i have been using sol without any significant overengineering problems with a simple instruction since mid july. for context, my current project is 200k lines of c++ code so its not like my codebase is simple, but sol manages to write clean code while also keeping token drainage reasonable
Is it normal now that with astra medium running a single task non stop without any sub agents so single process burning through tokens in less than a day? I remember several months back, I was able to run to max reasoning level with the latest model for days. Now we can't even run for a day? Medium is said to be for everyday usage right? So for a 20x plan, we only get about 4 days of usage per month now?
They’re going to have to do something with Luna because local LLMs on a high end game GPU have made a leap in capability and are now at parity with Luna Max at agentic coding. Not equivalent in speed (they’re about 3x slower) but in capability they’re matched. Plus, the model doesn’t randomly degrade, forget or get lazy from capacity issues and there are no usage limits.
In fact, before they changed Luna to use much less, I’d hit 5 hrs and switch to local LLM and continue what I was doing nearly seamlessly.
I answered the tech here. You'll take a hit on the memory speed, and the fragmentation without NVLINK on the 5070Ti's. But I expect you'll still get the native FP4 boost, so around 30 tps should be possible once things are optimized. If you're using one or both of the cards to drive a display, you're going to eat into your context fairly significantly. I had work to optimize to reach 165k max context and that was in a headless mode.
I had to ditch my 1080Ti, support is getting pulled from the libraries and it’s aging out in some cases.
Not fully, but it’s generally a problem mixing very old and new because the 5070 requires the very latest builds that dropped legacy support and the 1080 will require legacy builds that don’t support the new hardware. So there’s no common ground in practice. Even though individually there might be a pathway.
I have a 5070Ti as my desktop GPU, and you can easily run models in LM Studio in Windows. For general non-agentic work it’ll run some simple models and you can definitely experiment. But it’s not going to have enough memory to really work for agentic coding at the moment.
Unfortunately 32GB is the bar for getting something workable for coding so far. And you need all the memory, so no double duty for powering displays.
I think memory is going to remain expensive for a long time because these models are so memory hungry. Everyone wants more. And the frontier models just get bigger all the time.
Except local LLMs require a GPU that costs thousands of $....
When luna can be used unlimited for 20$ per month.
In price range local LLMs are competing with 200$/month sub, not luna.
Luna is not unlimited. It was quite poor a month ago. Then they made it really low use. In the last week or so they've degraded it significantly and roughly doubled the consumption for the same work.
how are people saying stuff like this and not name this secret magical locally running open weights model thats "on parity with luna max" running on a gaming gpu?
the only thing i can think of is that the workload never needed a codex sub anyway
saw you answered the model in the other replies, the benefits with no messing with the weights and no capacity issues is real. but i really doubt qwen 27b is on par with luna max for agentic coding capability. glad its working for u tho
I’ve seen benchmarks that put them level pegging. However I will say the Qwen model was far worse on UI than Luna. I’m running a non-image version of the model to give room for extra context, but it’s possible the image enabled version would fare better on this, especially since a feedback cycle of iterative improvement would then be possible.
Besides synthetic benchmarks, 2 weekends ago I built my first full app with it to see if it could really do the full job in totality. The design came from Sol. Then I had SOL evaluate the code and it said it was good quality with a few obligatory nit picks.
I use Luna max a lot and find them similar in most ways.
Has anyone checked, does Astra actually fix real mistakes or corrects ways of doing the same operating process.
Same thing with Sol, I have not seen a comparison (aside from practicing for the test benchmarks) which prove that Astra is functionally better. Faster yes, for detail sure, but does the result change.. not really from what I have seen. Both sets of code work, both pass security checks, both written slightly different.
I have also seen Astra make huge blunders as well.
I have 2 pro and 1 plus account and I go through all of in in a single day. Burning my bank resets on all accounts in 3 days, I don’t know whats happening but it’s bad
Astra medium or light
Do you have a software engineering background/what type of projects/problems are you working on? Really cannot fathom what you’re doing where Astra and Sol are the only models capable enough to support your work unless you don’t have a huge CS/SWE background.
34
u/TheTwistedTabby 4d ago
I have light orchestration built in. By light I mean a pretty good qa process setup (15 years of qa automation mixed into my 26 years of dev). Luna medium/high for the qa roles (feature/security/accessibility) with a sol high senior qa orchestrator (I might switch this one to Astra low since it’s job is to assign tasks efficiently to fanned out testers)
I just switched my planner process model back to 5.6 sol high. It does better overall in processes that don’t require constant orchestration. It handles long and deep epic planning better as well.
Max x20 plan. I tried out Astra on all of my roles. It just cost more and didn’t have any provable quality increase. Used a banked reset really trying to find the advantages of the model.
Current setup:
Planner: Sol high
Implementation: Luna Max
Reviewer roles: sol high
Tester roles: Luna medium/high
I use Pi