GPT-Rosalind-6 System Card Addendum (deploymentsafety.openai.com)

I imagine this is basically not at all due to actual model changes.

If you use Codex, Claude Code, etc, those are being constantly updated. That sort of behavior would be highly harness dependent.

IMO Astra isn’t really lazy, just extremely literal so when you use it side by side with gpt-6.1-sol, it might appear more lazy but it’s often just the big difference in prompting between the 2. Astra wants a clear goal definition of what you want it to do, while sol infers a bit more. And the Claude’s infer a lot lot more.

From memory, prompts like “this file is a bit messy” would trigger Sol and Claude to start editing the file immediately, while Astra wouldn’t. But if you give Astra a clear plan and a definition of what you want it to do, then it will keep working until that ask is fulfilled.

Especially compared with Claude that’s a huge difference, Claude is so proactive, I have to ESC it multiple times during the day and tell it to wait because I only asked a simple question.

My guess is models are constantly tweaked behind the scenes to adjust to available resources, steer people to other models, accommodate usage on other models, try out optimizations, etc.

Performance seems to vary like the weather.

I think you also likely have three letter agencies involved heavily now. The NSA is, without a doubt, policing what can be offered to the public and ensuring what the NSA has access to is a step or two beyond what is public.

I thought the same.

My assumption is Astra is the biggest model in terms of param size and has the most intellectual horsepower.

But Sol-6.1 is more instruction tuned to perform better on the average use cases for most users, so it benchmarks better and feels better to use.

It's widely suspected that sol 6.1 is a slightly detuned version of astra 6

> Didn’t Sol-6.1 top Astra?

On specific coding skewed benchmarks yes.

But Astra is in a different league when it comes to doing research and investigative tasks that require exploration.

For my usecases, Astra is pareto frontier - it dominates Fable 5.1 on cost. Fable 5.1 is a phenomenal model but Astra is cheaper and uses way less tokens and steps to produce similar output.

> Didn’t Sol-6.1 top Astra

No, Astra is still smarter (https://artificialanalysis.ai/models/?models=gpt-6-astra%2Cg...).

I felt like I had a read on it right away – it's a 6[.0]-series model, and not named after space since it's specialist model, and I assumed it was named after Rosalind Franklin. Looks like the docs back that up, that it's based on Astra, which is not [yet?] at 6.1.

Probably AI came up with the naming scheme ;-P Maybe they used Gemini for that or copilot.

Also why are cards hundreds of pages long? Seems like a broken metaphor.

Honestly the names of most of the frontier models are just getting more and more confusing. Maybe this one gets a pass for being a specialist model for bio. But it's hard to decide which model to use at this point, especially OpenAI.

I'm a new Claude user. I get to choose between Fable, Opus, Sonnet, and Haiku. Effort medium, extra, max, high.

Makes sense.

that's if few currently funded by someone open labs will continue being funded without receiving huge chunk of revenue from their models use.

And who's going to be providing these models?

How is open source going to work when governments start declaring models as munitions?

There are a lot of unanswered questions the open communities are going to have to deal with.

This is because of the democratically elected government demanding these controls. Remember Fable?

No it's bog standard capitalism. If you want to buy something from somebody else, they (usually) have the right to refuse.

Corporations are not, nor ever have been a democracy.

What you are asking for is regulations, but as we have seen regulations are pointed at giving the government and corporations more control.

Maybe elect better leaders and not corrupt old businessmen.

When did you check and what did you find?

For me its not a solved problem and AI/ML would still help to solve plenty Drugdiscovery in itself is such a broad area

There are still normal labs out there doing research for years on one drug alone

And biology of humans is huge and complex, not only is it not solved, its also expensive.

Even if you just not do ML drug discovery, AI can hellp with making the research faster: writing grants, finding issues, creating plans/ideas/suggestions, hepling with finding the newest research, writing code for supporting or analysis, controling microscopes and machines.

This is what I've built my current startup on. We find that ideas, structures and molecules are cheap and actually making them in cells is the issue. Obviously this doesn't apply to small molecules but as groups like Anthropic move closer to Phase I I'm guessing we'll see a lot of frustration with existing CDMO providers like Wuxi or Lonza just like we see with Apple and their woes with Micron etc.

can you elaborate?