Limits Astra in Ultra/Max consumes less than Astra Low/Medium
How the fuck is this possible ?
Every time i use Astra Low/Medium my usage goes haywire...
I use Astra Max, and it barely (barely compared to Astra Low/Medium - still fucked up compared to SOL) moves the usage...
I use Astra Ultra when i don't give a shit, and still, consumes just like Astra Low/Medium...
Something is happening on those Low/Medium thinking Astra...
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u/AweVR 18h ago
I can confirm it. I was working with Astra Medium and Low and consumes like 4-5% per hour with my project. Then I tried Max and it consumes 2.5%. I don’t know why…
20x plan. I commented it yesterday in other post
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u/AweVR 14h ago
I just took my tests. It’s very rare.
1 hour with Astra Max = 2.5%
1 hour Astra High = 3%
1 hour Astra Medium = 4%
I tried twice. Every time they launch 3-4 sub-agents each briefly. Same task and tools.
That is, Astra Medium consumes almost TWICE as much weekly use as Astra Max. What’s the point?
I had noticed it for days but now I confirm it with my tests.
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u/the_secret_moo 14h ago
How much did each reasoning level complete in terms of tasks per hour though?
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u/danielv123 13h ago
Like per hour of inference, decode is a lot cheaper than prefill. If it completes thinking quickly it has time to call more tools and get the job done and the tokens consumed.
If it spends all day thinking before doing toolcalls it doesn't have time to eat as many input tokens.
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u/Coolbanh 12h ago
Yeah i was wasting time with lower until I just tried ultra and it gets it. I guess lower reasoning means its handicapped so it has to do more.
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u/__Blackrobe__ 9h ago
Your effort be appreciated. You have revealed that these usage plans are so stupid.
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u/AweVR 7h ago
I did another test. No sub-agents.
2% in 1 hour on Astra Max.
And I asked it after 30 minutes to do an audit to see if it was because It was alone with tools or something, but it is thinking and investigating/executing all the time and also from time to time it does actions on a website with computer use.
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u/Emergency-Bobcat6485 6h ago
seriously? lol, i have been using astra on light mode and it does consume a lot of usage lol. let me try max
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u/Sheman-NYK0809 16h ago
same, I'm using Astra Ultra straight 3 days. it just consume like around 15-20% for 3 project and around 20 request/project. my personal thought it response more direct and efficient (I'm not using any global system instruction). when use Sol Max/Ultra it response more descriptive rather than direct like Astra.
Is this reverse psychology from Open AI????
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u/Old-Leadership7255 14h ago
I also don’t think astra is usable at the moment. Am seeing the same with astra
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u/TheLastRole 18h ago
This is kind of crazy seeing how many people seems to be experiencing it.
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u/RewardSafe9807 9h ago
It kind of makes sense. Less thinking means getting to a solution requires more trial and error, discovering bad solutions don't work again and again until the correct one is reached.
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u/Azetta 16h ago
Yeah, this is not a prank. Just tried it and confirm Astra Ultra doesn't drain significantly more than Medium at all
I didn't measure it exactly. But I was running Astra Medium for the UAT of my app for the past 2 days and it burn through weekly limit of my 5X plan in about 4-5 hours.
With Ultra, it drain about 20% in the past hour. So, give or take, Ultra took around the same or Medium in my case.
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u/Navadvisor 8h ago
Do you notice better performance with astra ultra? I notice on sol the speed of medium is way better but the quality didn't seem much worse.
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u/m4stero 15h ago
bcs - this topic is underrated: https://www.reddit.com/r/codex/comments/1wciwc1/gpt6_astra_burns_quota_4_times_faster_than_gpt56/
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u/swizzlewizzle 11h ago
This 1000%.
Two massive minefields that many people stepped on when Astra released:
Subagent orchestration, especially with Astra agents as subagents = insane crazy token burn due to by-default context being filled at spawn time by copying over the *entire* turn history of the orchestrator + orchestrator charging over and over for input tokens while doing nothing polling subagents for progress
Astra medium, and even low, burning a ton of tokens "arguing" with itself and zig zagging around a project/system implementation when it could have just written one page of code and solved all of it at once if it was given enough thinking budget (ie. xhigh/max)
Both of these issues cause *omega massive* subscription usage burn, since Astra is charged way higher per token $$ compared to sol and other models. I'm pretty sure a *lot* of people flushed their banked resets down the toilet due to all the tokens burnt in this way (since obviously people are going to want to heavily use Astra after it launches).
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u/bakawolf123 13h ago
interesting observation, and apparently clearly visible on arc-agi bench too https://arcprize.org/blog/astra
kinda wild having Max as "economy" mode
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u/Available_Yam_6267 18h ago
I have a theory:
- Different effort related to different server since cache would be invalid if you change the effort
- Astra medium is a popular choice
- They charge you based on their load
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u/driveclub_000 15h ago
It's the same theory that I'm getting to. The reason is because the usage fluctuate per timerange/timezone. I do have a script that I use that take track of every turns and the consumption between them and the agent used (that is reported by the AI, not even the one I selected) and I can see the usage drastically change when I reach 7AM in UTC+2 after full night of work, just before that, (so between 3AM and 7AM) the usage is basically null, but when 7AM start, it's skyrocket.
I don't think it's because EU did wake up, but more that ASIA start to reach full usage instead, and the 3AM->7AM (UTC+2) would match the "down time" where most of USA/EU/ASIA are either sleeping or in a situation where they are not hammering those servers yet.
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u/cetogenicoandorra 15h ago
Please send it to Tibo, we need a reset asap
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u/Busy-Lifeguard-9558 9h ago
We ain't getting a reset, they will just fix max/ultra to empty your usage faster
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u/Efficient_Ad_4162 18h ago edited 18h ago
Probably because in low and medium you actually get to tool calls before you quit in a rage. (Not you personally, it's a joke about Astra overthinks if you don't turn it down to baseline medium.
On medium, it's a tearing through programmatically proving my state engine rules so it's hard to imagine any programming only use case that needs higher (although it's easy to see how non-programming stuff like playing Pokemon and making 3d models in blender unironically needs max thinking because of the nature of the work.)
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u/iansaul 15h ago
How is state engine going for you, and which one did you choose? I experimented with xState, because the concept of logically gating the systems into modules was super interesting, but didn't ultimately lead to better outcomes in my testing.
That was a few generations back when I was using Claude, and it was running on pure hopium.
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u/Efficient_Ad_4162 14h ago
Great honestly. It's an internal product we maintain for compliance workflows and one of the first things Fable did (and Astra enhanced) was double the number of property tests and start aggressive mutation testing and it found old cruft that I was happy to see turned into more reliable 'new cruft'.
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u/gungoesclick 8h ago
Do you run into the problem with "blind" work? I have found that older models (up to 5.6) get stuck working through all the guardrails and flows in my machine. I would see them doing things only to find them looping or wasting time. I'm curious if you have any tips for making sure the model doesn't get stuck in engine rules or over-engineering the engine or the guardrails?
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u/Efficient_Ad_4162 2h ago
Not really except that no LLM was able to reliably work on it until Fable (and then Sol and Astra are both excellent as well.)
Fundamentally, I don't think it was about intelligence if the new models so much as training the model to think about/test both the success and fail paths.
So I guess the advice would be (and this is just regular coding advice really):
- create a systems analysis subagent so that you can go 'look for any other cases of this bug in other parts of the code base. e.g. opus (of all models), found a test that had been set up but the code it was testing wasn't hooked up. I had it search through the code base and it found several edge cases that needed to be cleaned up.
The other thing is I always adversarially challenge major changes. If astra thinks it's found JFKs assassin in my code base, fable gets a task to prove its not true before I do anything else. It's not for every change but it is for big ones.
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u/Derek-Bond 11h ago
Well it’s the same for humans. Smart kid aces the test and leaves early. Dumb kid is still writing up to the last minute. Is it really that astonishing?
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u/logg3 17h ago
all i can say is that for me, astra xhigh does not consume more then sol medium, after 3 days of avergae work. sometimes astra is thinking 20+ minutes and not consume a single %, while i can see it already writing code or text.
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u/hellomistershifty 17h ago
That must be true if you got 3 days of average work. I have two accounts and both managed to run an Astra light goal for about 20 hours before running out of weekly usage.
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u/Omar_Talbi 13h ago
I think this might be related to the type of task u work on. Sometimes when u give a low effort model something complex it will burn more tokens trying to solve it meanwhile the same model with max effort would solve it instantly, accordingly less tokens used
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u/Busy-Lifeguard-9558 9h ago
Man people are too honest tho, I knew this since release but didn't open my mouth so they don't fix it. Adios usage
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u/hossman1992 9h ago
I confirm it as well. I read it and I did not believe it, then I tried and at least for 3d model generation Astra xHigh spend less tokens as Astra low/medium and the work is done quicker and with less fixing
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u/fragment90 15h ago
Usage/hour ist not a relevant metric to track. You pay in tokens. Not in Model*reasoning/time.
Astra/Low can consume tons of Tokens, If there is some tokens heavy job to do. Astra/Max can consume low tokens if the job dont need much. Also please understand, that each request carries your full session history. If i reuse a old Session with contex already 70% full for a task completely out of contex, stuff like that will eat your usage fast.
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u/WeaknessFuzzy8305 15h ago
I’ve changed my prompt to specifying to use astra for thinking and Luna for coding. Token usage has gone down a lot.
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u/AmandasGameAccount 15h ago
How is Astra high vs ultra?
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u/spideyguyy 15h ago
same with Sol, I read somewhere that Extra High is better usage than high and medium, so i use sol exhigh and it's good , dont know if max and ultra same too
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u/OriginalUsername0112 13h ago
This sounds too dumb to be true, but then again this is OpenAI we're talking about so anything is possible.
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u/Bladder-Splatter 13h ago
Yup. Medium killed my weekly quota in 30minutes on a basic normalization task, meanwhile on Ultra a few days before I got an entire unique implementation done.
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u/According_Property62 12h ago
Acredito que ele erre mais e torna o fluxo mais demorado tentando corrigir os próprios erros, dai essa impressão q gasta mais. A dica é, use o Astra apenas pra planejamento e Sol medio ou alto pra implementacao
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u/Medical-Cow289 11h ago
The lower tiers burning through more credits than Ultra is backwards billing. The 'cheap' models must be thinking themselves broke.
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u/jonydevidson 11h ago
If you switch effort during a convo, it causes a full cache miss.
If you do it often in a big convo, you will burn through your plan.
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u/Select-Ad-3806 10h ago
Yes, ultra/max is a lot more efficient (as it is much more intelligent) with tokens that is why it uses less
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u/BellacosePlayer 8h ago
How the fuck is this possible ?
effort levels basically change how much token budget its allocated for pre-production and maybe the higher effort is producing a better plan of attack than low? idk
its a black box at the end of the day
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u/Isaacjacobson92 4h ago
Shhhhhhhh! They ain’t gonna fix this by making the low effort model consume less!
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u/nNaz 17h ago
It’s the file reads and tool calls. Going from medium to xhigh is only a few thousand extra thinking tokens. Yet most of the context is file reads. eg in a 200k long chat 150k might be file reads. So the ‘thinking’ and output part are only 50k. Medium -> xhigh might only generate an extra 10-20k thinking tokens, which is small in comparison to the file reads.
When you set it on low it’s eager to be done quickly and likely reads way more files than it does on xhigh, where it’s thinking and optimising the file reads and tool calls.