The parent + subagent workflow has become critical for keeping the reasoning agent (parent) context-lean while also letting me chat to the main agent while work is getting done.
My main process is to use Fable to reason and then spawn Opus subagents, and I get amazing results, and I'm always looking into what the subagents are doing.
- Unbearably slow
- A token eating machine like no other
- Constantly compacting
- A model (like other GPT ones) that hides thinking traces and thinking summaries, which infuriates me
I've been in the Claude camp for a while, but the way it writes has left me with a a brick for a brain and wanted to see if Astra was as good as they say. Well, I can't know, because in the time it takes for it to actually build anything useful, I've moved to other ideas.
Unbearably, annoyingly slow. I keep thinking I must be doing something wrong.
However, it is not a 'token eating machine'. In fact it uses a third of the output tokens of Opus 5.5, Fable 5.1, or Opus 5.
17k for Astra xhigh vs 61-66k.
The rest still stands, though.
But if I've learned anything is that in a 2 months I might have completely turned around, who knows
https://developers.openai.com/api/docs/pricing?latest-pricin...
One potential deciding point is that Claude still has a $200/mo 20x plan, where, since Sept 11, OpenAI does not and has no ETA for the return.
I downgraded my OpenAI plan 2 months ago to the $100/mo, but my usage has gone way up, but now I can no longer upgrade to the $200/mo plan ("This option is temporarily unavailable"). Thankfully I have 2 usage resets available, but I'll probably be switching back to Claude; I was super happy with Astra but I'm burning through tokens and have 4 days before my next reset.
Its a great release, I will use both heavily.
>> $4 → $2
>> $20 → $10
Do you mean 100% more expensive? GPT 6 is 100% more expensive than 5.6 per your post.
If these price changes mean that coding plans have effectively more usage then that's great, but Codex is surviving on resets from my own experience using it. I was glad to go back to Claude.
This is great, but practically, I'm not going to start working on more side projects.
Perhaps in another 6-12 months I'll be fine to drop down to $20/m instead of $200.
A lot of what I'm doing has pretty expensive build/testing processes between iterations - even on a 40 core machine - so I'm not burning tokens 24/7 like some people may.
I'd guess I'm probably spending >50% of the time running tests & build processes & tooling and the remainder is purely burning tokens.
I also have some internal tooling (that I will hopefully open source soon) that makes LLMs substantially more correct (thus more efficient) - so there's that, too.
There are a ton of use cases that open up with cheaper models.
E.g. extensive security scanning on every PR, quality scans, adversarial reviews etc
Should be B vs A correct?
Else it's confusing
We don't know how much they are bleeding financially, it might just be a front
Highly subjective take
What kind of work do you do, out of curiosity
If they’re subsidizing my usage, that’s great.
In the event of a crash, the investors who put countless billions into this will be still be seeking to maximize their return. Even if it is just pennies on the dollar. Assets (including compute hardware) will be sold, just as they are also sold when any other business fails.
Or maybe a crash doesn't happen. Maybe prices rise to the moon instead and there's nothing we can do to lower them.
Or maybe (just maybe!) a crash never happens and there's never a huge price increase. Prices stay low-ish.
All of these possible outcomes suggest to me that the maximally-sane option that a user can select, today, is to burn it while it lasts. And then, if/when a crash or a massive price increase occurs, just adjust accordingly. (The rest of us will all be in that same boat, too.)
Is there a world where OpenAI starts charging $2,000/month for what we previously were paying $20 for? What are we going to do? AWS could totally jack up the prices for EC2 instances as well, but we've come to rely on that as well.
When they cut prices on luna the first time around they took (literally) millions of users from anthropic.
The "paradox" is when an increase in efficiency which would decrease the use of a resource all else equal, instead indirectly causes more use.
Surely, a large part of the increase of the demand in LLMs is in their intelligence, but to hit the demand models needed to be made more efficient, and labs found that more efficient models, still demanded more usage.
Cache read/write decrease by 50% or similar? That's where most (95%+) of the cost is for agentic coding workloads.
Performance increases both with larger model (Luna vs Sol)
And with more reasoning (low vs xhigh)
GPT-5.6-Sol, GPT-5.6-Terra, and GPT-5.6-Luna were released in July of 2026.
The first release from the GPT-6 series was GPT-6-Astra. GPT-6-Astra happened on around September 3, 2026, and the previously-mentioned GPT-5.6-* widgets remained available.
Today, September 22, 2026, we now also have GPT-6-Sol and GPT-6-Luna added into the mix.
As I write this, all of the model identifiers I've mentioned are available to select for use within Codex.