The phrase system card is so frustrating because it’s using the word card like it’s a metaphor for something but then abuses it. A card is supposed to be small and concise, that’s why it’s a card. This has many many pages and is an “addendum”.
Slightly off-topic to this model card but has anyone noticed changes to GPT6 Astra about a week after 6.1 came out?
I feel like 6 Astra just turned a lot lazier during planning and strangely enough claude has become a lot more assertive with Opus 5.5 while it used to be very lazy.
It’s really obvious during planning, it used to be that claude would just give a best effort guess and handwave things but now it will actually write small tests, launch playwright to test a hypothetical and even look things up in github during a question. This was something codex’s models were really good at.
I’ve read so many stories about people claiming models getting nerfed after a new release and now it seems I can finally join them.
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.
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.
Sol 6.1 is incredibly incredibly slow, 20t/s slow (vs the ~70-80 of Terra 6.1 and Luna 6). It wouldn't be so much slower if it was just a distillation, surely? There's something else going on there.
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.
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.
Last I checked machine learning wasnt the bottleneck for drug discovery and where it fits in the research process was largely solved 5+ years ago.. Weren't there already a ton of ML based drug discovery startups that went out of business, or got aquired, not because they werent useful but because they produced more candidates than the industry could use.
I dont think I want researchers offloading their thinking to some specialty model after seeing what its done to me and my colleagues in software, it certainly isn't making anyone more competent.
It really only enables people to be able to do less novel and lower quality work faster.
OpenAI and Anthropic creating these platforms seems mostly just to create narratives around AI that its going to lead to cures for disease, which it wont, but plenty of normies assume it will cure cancer now. Also a bunch tech oligarchs who are equally convinced or 'one-shot' they're going to live forever because of language models lol.
Promises of the Fountain of Youth continue to be a time tested tools for snake oil salesmen.
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.
Great military terms: "dual use", "Preparedness Framework", etc.
You just want to make all mathematicians and white collar employees unemployed and want to distract the press and the public with your extinction nonsense.
Though you may care about bioweapons because they could affect you and your trafficked children.
The phrase system card is so frustrating because it’s using the word card like it’s a metaphor for something but then abuses it. A card is supposed to be small and concise, that’s why it’s a card. This has many many pages and is an “addendum”.
So how is this different from Astra, if it's the same weights and zero other training has been done?
Did they essentially package a system prompt?
Slightly off-topic to this model card but has anyone noticed changes to GPT6 Astra about a week after 6.1 came out?
I feel like 6 Astra just turned a lot lazier during planning and strangely enough claude has become a lot more assertive with Opus 5.5 while it used to be very lazy.
It’s really obvious during planning, it used to be that claude would just give a best effort guess and handwave things but now it will actually write small tests, launch playwright to test a hypothetical and even look things up in github during a question. This was something codex’s models were really good at.
I’ve read so many stories about people claiming models getting nerfed after a new release and now it seems I can finally join them.
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.
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.
> GPT-Rosalind-6 uses the same weights as GPT-6 Astra, our flagship reasoning model.
Didn’t Sol-6.1 top Astra? Or is it maybe that Astra is still a larger model and therefore can comprehend deeper complexity better in the long run?
> Didn’t Sol-6.1 top Astra
No, Astra is still smarter (https://artificialanalysis.ai/models/?models=gpt-6-astra%2Cg...).
> 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.
Have you compared with Opus 5.5?
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
> It's widely suspected that sol 6.1 is a slightly detuned version of astra 6
My feel is it's the opposite of a "detune".
Sol 6.1 is likely a distillation that's highly instruction tuned and designed to perform better on benchmarks.
Sol 6.1 is incredibly incredibly slow, 20t/s slow (vs the ~70-80 of Terra 6.1 and Luna 6). It wouldn't be so much slower if it was just a distillation, surely? There's something else going on there.
But is it slower than Astra?
OpenAI naming scheme makes no sense. I get naming is hard but atleast keep it consistent.
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.
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.
Probably AI came up with the naming scheme ;-P Maybe they used Gemini for that or copilot.
It’s clear to me that open source will rule at the end of the day as everyone wants AI and nobody can rely on U.S crop/gov anymore.
that's if few currently funded by someone open labs will continue being funded without receiving huge chunk of revenue from their models use.
https://www.youtube.com/watch?v=g72ZxPyS1vk
https://en.wikipedia.org/wiki/Rosalind_Franklin
> We review applicants’ proposed uses, security controls and governance practices before granting access.
For-profit corporations shouldn't have this power, this is not democracy.
This is because of the democratically elected government demanding these controls. Remember Fable?
Last I checked machine learning wasnt the bottleneck for drug discovery and where it fits in the research process was largely solved 5+ years ago.. Weren't there already a ton of ML based drug discovery startups that went out of business, or got aquired, not because they werent useful but because they produced more candidates than the industry could use.
I dont think I want researchers offloading their thinking to some specialty model after seeing what its done to me and my colleagues in software, it certainly isn't making anyone more competent.
It really only enables people to be able to do less novel and lower quality work faster.
OpenAI and Anthropic creating these platforms seems mostly just to create narratives around AI that its going to lead to cures for disease, which it wont, but plenty of normies assume it will cure cancer now. Also a bunch tech oligarchs who are equally convinced or 'one-shot' they're going to live forever because of language models lol.
Promises of the Fountain of Youth continue to be a time tested tools for snake oil salesmen.
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.
You’d be a fool to use an OpenAI product with any proprietary data.
can you elaborate?
Great military terms: "dual use", "Preparedness Framework", etc.
You just want to make all mathematicians and white collar employees unemployed and want to distract the press and the public with your extinction nonsense.
Though you may care about bioweapons because they could affect you and your trafficked children.