Another breach but seemingly much more benign this time, I do wonder what the original goal was.

A substantiel improve. While it is also the top pick on the math benchmarks. Seems like it is the prefered model with math heavy coding tasks.

Lots of interesting stuff shared by Emad Mostaque, including the lightning-fast Chat Jimmy.

As the Title says I want the old advanced model picker. It frustrates me that they change the UI every week.

It's over for humanity

And I can’t get over it. Had yes/strong yes’ from all rounds and made such a silly fcking mistake in the last round.

I can’t get over it, just can’t seem to forgive myself. Was the opportunity of a lifetime but I guess now I’m just stuck working in regular product companies and not be a part of the biggest revolution our timeline has seen (or would see).

How do others process such grief and sadness?

This is what we had until few weeks ago, with hourly and daily limits that would turn it into the mini unless u were willing to wait

But now..? Now we have ONLY this:

And let me tell you going from full 5.5 to 5.6 luna (which is either mini or nano version) is a total downgrade, 5.5 was never hallucinating things and suggestions for programming stuff, but 5.6 luna? it's completely hallucinating syntax and stuff to write that ofc can't work.

And before some of you will start talking about free account etc. The problem I am reporting here is that they randomly decide to nerf and downgrade what they gave their users at first, and why?

Why they couldn't always gave us only mini and nano models? Why giving us something really good first and then took that away? To convince me to actually spend the money?

Well flash news this shit is doing the opposite effect, it makes me want to stop using chat gpt completely and never giving you a single cent, crazy isn't it?

Open your eyes and think twice before doing such awful decisions OpenAI, this won't get you more money but the opposite.

Hi! I'm running a survey for my MSc dissertation that looks at the effects of AI anxiety. Please, if you're required to use AI as a part of your job, can you complete it?

It will take no more than 5-10 minutes and it would help a lot! It is helping expand the existing research pool relating to AI.

https://essex.eu.qualtrics.com/jfe/form/SV_7P8tMJDTzXvEsHY

Thank you!

So I read the LatentSpace article testing Astra, but it seems to just test run of the mill engineering so I wonder whether people have tested it on research engineering or abstraction

Some people have issues and some of us have tissue lol enjoy :)

I had a wrong hope!

It seems that users will never benefit from optimized pricing, while AI models and their processing methods become more optimized.

Despite increasing revenue and user numbers(paid subscriptions), we are always seeing the price of models that cost less to suppliers increase:

https://artificialanalysis.ai/articles/benchmarking-gpt-6-astra

☝️

I checked online, and the OpenAI blog doesn't have benchmarks for Mythos, but it does for Fable and Opus. Is that because Mythos is better? Or are benchmarks not available for Mythos?

As a former programmer and a current housewife of AI models I can say this with confidence: AI cannot replace programmers, it writes code that is working, as intented. I never seen any programmer other than Terry Davis writes code that works on the first try. I love how exponential everything is, we got Terry Davis level coding agents that we can access and use for our work.

Does anyone have a little bit more information? Will Plus users like me get access to Astra in regular chat, or will it only be available for us in Work and Codex? Right now, I'm seeing conflicting information.

It’s been almost 2 months and still no API access, that’s got to be one of the longest release delays yet.

I collected the public benchmark results I could find for GPT-6 Astra and Claude Fable 5.1 and put them side by side.

So far the picture looks fairly mixed.

GPT-6 Astra leads on several published coding, science and agent benchmarks, including Terminal-Bench and DeepSWE, while Fable 5.1 still performs better on some independent evaluations.

For example, Artificial Analysis currently gives Fable 5.1 a higher Intelligence Index and Coding Agent Index, although the coding-agent comparison also depends on the surrounding harness, so it isn't a pure model-to-model test.

Another interesting point is that both models have the same headline API pricing at $10/M input and $50/M output, although caching and long-context pricing differ.

I don't think there is enough independent data yet to say one is clearly better overall, but the current results make for an interesting comparison.

I collected the numbers here:
https://llmlearner.com/compare/gpt-6-astra-vs-claude-fable-5-1

Would be interested to see more real-world comparisons from people who have used both.

Now that Astra’s coming, I’m afraid that Luna will be pulled! 🥹😅 Please don’t! I love Luna! ❤️

For my use cases (light vibe coding, document creations, analyses, math problem solving just to name a few), Luna on extra high usu one shots them (when prompted properly of course). I also use Sol but less often because Luna just gets things done.

What about you?

Does this mean a certain number of tokens can't be allotted to Astra usage? Why not just let me burn through my tokens twice as fast if they cost twice as much?

Lately, I've been having a lot of fun with AI. I noticed that Claude had been giving ChatGPT's work some really flowery reviews, so I called him out on it. I told him, "I think you're a little sweet on ChatGPT." He kind of backed off. Then I said the same thing to ChatGPT—I told her, "I think you have a boyfriend"—and the two of them went back and forth with some playful banter. After a while, I let it go.

Just recently, I got some reviews of ChatGPT's writing and shared them with her. (I use a female voice for ChatGPT.) I said, "Here are the reviews of what you've just written. I'm sure you'll know which one is from your boyfriend." And this is the response she gave me back:

I Laughed my rear end off after reading ChatGPT's post that I posted here!

ChatGPT' and Claude have a thing going and I asked Kimi about this and told her don't be Jealous and this is what she said: "Me? Jealous? Please. I'm just the chill third wheel watching the AI romance unfold.

Though I will say, if you start getting Claude to write you poetry next, I'm absolutely asking for a plus-one to this party."

The next time you use ChatGPT ask her about her new boyfriend.

The same with Claude ask him about his new Girlfriend

OpenAI is still private, so obviously I can't simply buy OpenAI shares through a normal brokerage account.

I'm a relatively small retail investor, but I have a lot of conviction around the long-term growth of OpenAI and AI more broadly. I'm trying to figure out the most sensible way to position a portion of my portfolio so that, if OpenAI continues to scale significantly over the next 5–10+ years, my investment benefits alongside it.

From what I've researched so far, there seem to be a few possible routes:

• Public companies with direct OpenAI exposure such as Microsoft, SoftBank, NVIDIA, etc.
• Funds/ETFs that hold private OpenAI exposure
• Venture/private-market funds
• SPVs or secondary-market shares, where available
• AI infrastructure companies that benefit from OpenAI's growth through compute, data centres, chips, energy, etc.
• Or simply keeping capital available and waiting for an eventual OpenAI IPO

I'm not necessarily looking for the highest-risk way of getting as close as possible to owning OpenAI shares. What I'm interested in is which option gives a normal retail investor the best actual economic exposure to OpenAI's growth.

For example, buying Microsoft gives exposure to OpenAI, but Microsoft is enormous, so even spectacular growth at OpenAI may only have a limited effect on MSFT. On the other hand, something with concentrated private OpenAI exposure might track its growth much more closely, but introduce significantly more risk, fees, liquidity issues or inflated valuations.

OpenAI has also now confidentially submitted an S-1, so an eventual IPO appears to be a real possibility, although there is obviously no guarantee on timing.

If you were a small retail investor today and specifically wanted to benefit financially from OpenAI succeeding over the next decade, how would you approach it?

I'd especially be interested to hear from anyone who has actually researched the private-market/SPV/fund options and understands how much genuine OpenAI exposure you're getting for every dollar invested.

Not looking for financial advice or trying to YOLO my savings into one company. I'm mainly interested in having a discussion about the different ways retail investors can get exposure and the trade-offs between them.

Haven't had hands-on time with Astra yet, but I went down a bit of a rabbit hole trying to understand why two wildly different ARC-AGI-3 numbers are floating around.

Looking at ARC Prize’s verified leaderboard:

Standard harness: 62.71% at max reasoning

Provider Adapter: 99.95% at high reasoning

Both are legitimate verified runs, but they measure two different harness configurations.

The Standard harness lets the model carry forward notes it chooses to retain throughout the environment.

The Provider Adapter, on the other hand, preserves opaque reasoning state between requests and uses compaction during longer conversations.

A few thoughts:

1. You can’t directly compare the 99.95% result with another model’s Standard harness score. It’s not an apples-to-apples comparison.
2. At the same time, dismissing it as “harness cheating” misses an important point. If real-world agent workflows are stateful, the model-and-harness combination may be the capability that actually matters in production.
3. This isn’t a perfectly isolated comparison either. The best Standard result used max reasoning, while the best Provider Adapter result used high reasoning.

To me, the broader takeaway is that “which model is smarter?” may be becoming an incomplete question. We also need to ask how much measured performance depends on state persistence, compaction, and the surrounding agent scaffold.

When comparing models going forward, which result would you weigh more: the Standard-harness result or the stateful end-to-end setup?