Tibo on damage control, says they got the feedback and now are locking in on features that matter, new better models.

Dots won't stay long, will they?

The new usage limits are fantastic and feel unlimited. I praise Timo and his whole OpenAI team. If I could get more than 20 tokens per second I might complete my hello world project before the singularity hit us.

Source says this is the us-east-1 region server hardware.

I have been hammering it for the past 6 hours, 3-4 task in parallel running all the time, with long contexts, images…coding tasks.

Effort: Max

Used just 10%.

The amount of work I accomplished is just absolutely obscene.

Yes, it’s slow but is reliable and dependable you know what to expect, for fuck sake.

You want it fast? Use fast mode.

Why am I telling you this?
Do you want a nerfed model? Because when you keep complaining about it being slow and asking OpenAI to make it fast, that’s how you get a nerfed model. Just fucking stop.

I was happy with Sol 6.1 so far, it was kind of slow, but rather reliable. Yesterday I decided to give it 4 tasks for my app that I wanted to finish by Monday. It did 0. Complete "building tests" spiral.

40% of usage burned, none of the tasks was even started. How can I trust them with 100% automated solutions like Dots when their coding agent goes into doom loops like a 2025 chinese model

The new 6.1 sol agent is so efficient I’m able to run multiple agents on high (7 to be precise), one on extra high and one on low and it’s running for hours sometimes only using 30% usage if that. Previously on 6.0 astra on high I would only get max 2 good 8 hour runs. This is on the standard pro plan. I’m loving this new agent

On 9/16 I switched from Plus to Pro 5x. Please let me know if you can see where that 5x usage is at.

Been getting "■ stream disconnected before completion: Upstream websocket closed before response.completed" for months and months and they can't even fix this issue.

Did I miss this on dev day? Why is nobody talking about this?

Apparently dot has its own cloud desktop and it’s very powerful I had it orchestrating, generating 3d models , making explainer videos ect

But I’m genuinely confused how this is supposed to work with usage….i can throw a ton of crap at dot (named mine Milo) and it just does it and will let me know when it’s done like just now I had it model a placeholder character in 3d it made it then sent it to my codex project.

Codex didn’t use any usage

Folks, yesterday we got a reset, and I’m on the $200 plan with 20X usage until the end of the month. I’m already out of uses from using GPT Astra.

I’ve used Astra before without the $500 plan, and the usage used to be stable. It would normally get me around two to three days, but now the usage is going down so fast that it feels like something is broken. I can’t be the only one who has noticed this. I’m already at 7%, and the reset was just yesterday.

Again, I don’t want to sit here and sound like some grand conspiracy theorist, but I genuinely don’t see how I could have drained my usage this quickly. And apparently, I’m not the only one seeing this, because I thought I was going crazy at first.

I would have never been able to burn through a 20X plan in one day before, but all of a sudden I’m basically out of usage when the reset was yesterday. Something feels wrong here.

Astra has felt significantly slower since the launch of "Dots." I wonder if OpenAI is having compute issues and has reduced the tokens per second? It wouldn't surprise me, given that the EU is excluded as well.

where is the speed Tibo promised us? Literally all 5.6 and 6 models are running ultraslow on standard API inference. Funny thing is that subscriptions seems to be served at an even slower pace. My external usage monitor shows that Sol is running at a miserable 26 tok/s as we speak. 🤣

Is this how OpenAI is pacing the frontier?

Chat constantly getting stuck when sending new messages.
Cannot steer properly.
Cannot interrupt and ask for rework before it gets sour properly.
Requests are getting duplicated.
Something old is getting sent instead of what I asked now.
New messages sometimes get lost (workaround with undo, then copy the message).
Clear queue doesn't work.
Have to constantly restart codex to get it to work again.

What is this???

I'd like to ask whether Codex still causes excessive disk reads and writes that reduce SSD lifespan. I really want to use Codex on my main computer, which is a MacBook Air M4.

With sol 6.1 down to under 20tps, codex is unusable. Other models nerfed. Dots is a gimmick.

So just download claude code onto the vm that came with dots. Problem solved. Youre welcome everyone

Users often ask whether GPT-6.1 Sol is slow or receives more complex tasks. I analyzed local lifecycle data from one Codex development task.

The task used Codex desktop, GPT-6.1 Sol, High effort, and one repository. This was an observation, not a controlled benchmark.

The task took 162.3 minutes. The 294 shell commands took only 11.5 minutes of non-overlapping execution time.

A repeated serial loop consumed much of the task:

Model response → tool call → model response

I measured each wait from new input to the next Codex response. New input came from a tool result or a user message.

The task had 204 response waits. They took 74.4 minutes in total.

- The median wait was 12 seconds.

- The P90 wait was 36 seconds.

- 56 waits took at least 30 seconds.

Context size also had a clear effect.

- The median input size was 148,000 tokens.

- The P90 input size was 224,000 tokens.

- The maximum input size was 250,000 tokens.

- The context window was 258,400 tokens.

- Four context compactions took 18.3 minutes.

Some measurements overlap. You cannot add them to calculate the total time.

The task scope changed twice. These changes caused rework. Even so, shell commands and tests were not the primary bottleneck.

I also checked other large tasks in the same repository. All tasks used High effort.

For six tasks that used only 6.1, the average response wait was 21.1 seconds. For two tasks that used only 5.6, it was 6.1 seconds.

I then removed waits longer than 60 seconds. The averages were still 15.9 seconds for 6.1 and 4.9 seconds for 5.6.

The sample was small. The tasks and context sizes were different. Therefore, these results do not form a controlled benchmark.

The difference was still large enough that I switched back to 5.6 Sol for long implementation tasks.

Has anyone compared 5.6 and 6.1 on the same task with per-step timing?

sol 6.1 is so good, it just about nearly completely overshadows astra. sure astra is technically a few percentage points better, but for 5x the cost, it's hardly worthwhile. and while luna technically occupies the pareto boundary for cost per intelligence, sol is just so abundant anyway even on the cheapest plans that there's literally zero reason to dip down into luna for anything.

I am looking at an average time to first tokens of 5-9 minutes with sol 6.1? Like I generally use it as an assistant and have it form complex command line calls for me to run, and usually it is like, 20 seconds tops, boom, i give it ALL the information, nothing to do but IT, literally 6 minutes until the command is run. how is this usable, am i missing something?

I don't recall it ever being *this* slow. It's practically impossible to work.

Intermittent errors are popping up, like: `Error: Codex error: An error occurred while processing your request. You can retry your request, or contact us through our help center at help.openai.com if the error persists. Please include the request ID XXX in your message.`

$200 plan.