GPT-Rosalind-6 System Card Addendum

deadnomore · 2026-11-02 17:21

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. This has many, many pages and is an “addendum”.

fakwandi_priv · 2026-11-02 16:54

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.

artdigital · 2026-11-02 17:23

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.

margalabargala · 2026-11-02 17:03

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.

kind_wren74 · 2026-11-02 18:34

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 also suspect government agencies like the NSA may now be involved in deciding what can be offered to the public, possibly keeping their own access a step or two beyond what's public.

ludvigk · 2026-11-02 17:44

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?

Alifatisk · 2026-11-02 16:35

> 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?

lnkdinsuxs · 2026-11-02 16:52

> 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.

2026-11-02 17:44

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pizza234 · 2026-11-02 16:53

> Didn’t Sol-6.1 top Astra

No, Astra is still smarter (https://artificialanalysis.ai/models/?models=gpt-6-astra%2Cgpt-6-astra-xhigh%2Cgpt-6-astra-high%2Cgpt-6-astra-medium%2Cgpt-6-astra-low%2Cgpt-6-1-sol%2Cgpt-6-1-sol-xhigh%2Cgpt-6-1-sol-high%2Cgpt-6-1-sol-medium%2Cgpt-6-1-sol-low).

kingkongjaffa · 2026-11-02 16:37

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.

Starlevel004 · 2026-11-02 16:50

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

lnkdinsuxs · 2026-11-02 16:54

> 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.

Starlevel004 · 2026-11-02 17:01

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.

Silagi · 2026-11-02 18:28

There was some (admittedly flimsy) evidence from a microsoft model description card that it's doing multiple passes through its own weights before token generation to improve output; it might be an architecture change that's making it slow, moreso than raw size

lnkdinsuxs · 2026-11-02 17:50

> Sol 6.1 is incredibly incredibly slow, 20t/s slow

How are you getting these numbers?

Starlevel004 · 2026-11-02 17:59

I have a small token/s extension for Pi. It's regularly around ~15-20t/s for Sol 6.1.

lostmsu · 2026-11-02 17:03

But is it slower than Astra?

2026-11-02 17:10

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quick_osprey38 · 2026-11-02 16:45

OpenAI's naming scheme is hard to follow. I get that naming is hard, but I'd like it to at least be consistent.

whafro · 2026-11-02 16:47

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.

early_falcon20 · 2026-11-02 18:42

As a new Claude user, I find Anthropic's naming confusing too. I choose between Fable, Opus, Sonnet, and Haiku, and the effort levels are medium, extra, max, and high, which don't have an obvious order.

deadnomore · 2026-11-02 17:23

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

xp84 · 2026-11-02 17:26

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.

varispeed · 2026-11-02 16:51

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

xyzsparetimexyz · 2026-11-02 18:42

Named after planet 6 in the Rosalind system right?

modest_koala28 · 2026-11-02 16:29

My view is that open-source models will win out in the end: demand for AI is everywhere, and I don't think people outside the U.S. can count on depending on U.S. corporations or the U.S. government anymore.

pixl97 · 2026-11-02 17:56

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.

NoidFonsense · 2026-11-02 18:55

They could be crowdfunded. You could even have a system where only contributors to the crowdfund have a license to use it.

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

I mean open source cryptography did win that battle in the end. Even if it's illegal you could host the model weights on i2psnark and crowdfund with monero.

andriy_koval · 2026-11-02 17:41

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

hmokiguess · 2026-11-02 16:19

https://www.youtube.com/watch?v=g72ZxPyS1vk

DetroitThrow · 2026-11-02 16:34

https://en.wikipedia.org/wiki/Rosalind_Franklin

steady_ocelot80 · 2026-11-02 17:04

> We review applicants’ proposed uses, security controls and governance practices before granting access.

I don't think for-profit corporations should be the ones deciding who gets access to a capability like this. Gatekeeping power of that kind should be democratically accountable, not left to a company's internal review.

clever_beaver74 · 2026-11-02 17:57

Corporations are not, and never have been, democracies.

What you're really asking for is regulation, but in my view regulations have tended to give the government and incumbent corporations more control, not less.

I think the more effective lever is electing better leaders who aren't captured by the businesses they're supposed to regulate.

wry_xy · 2026-11-02 18:51

I want one regulation on AI: everything must be public. Training data, code, procedures, everything. And for that to work, a complete scrapping and overhaul of our copyright system. One can dream.

mediaman · 2026-11-02 17:19

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

DonsDiscountGas · 2026-11-02 17:49

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

opal_finch87 · 2026-11-02 16:45

Last I checked, machine learning wasn't the bottleneck for drug discovery, and where it fits in the research process was largely worked out 5+ years ago. Weren't there already a lot of ML-based drug discovery startups that went out of business or got acquired? As I understand it, that wasn't because they weren't useful but because they produced more candidates than the industry could use.

I'm wary of researchers offloading their thinking to a specialty model after seeing what it's done to me and my colleagues in software. It hasn't made anyone more competent. Mostly it lets people do less novel, lower-quality work faster.

My view is that OpenAI and Anthropic building these platforms is mostly about creating a narrative that AI will lead to cures for disease. I don't think it will, but a lot of the public now assumes it will cure cancer, and some tech leaders seem convinced language models will help them live forever. Promises of a fountain of youth have a long history of being oversold.

origini · 2026-11-02 17:31

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.

opal_finch87 · 2026-11-02 18:18

I think this is overly optimistic.

Bioinformatics researchers doing ML work already use LLMs where they're helpful; they don't need a specialized model. I'd bet heavily that this model won't still be getting updates a year from now.

I don't believe LLMs will help with drug discovery more than existing ML approaches do, and I don't think many biotech/bioinformatics PhDs would agree that they will. They're language models. Nobody is saying researchers won't use LLMs for the tedious parts, but that doesn't speed up the fundamental bottleneck, which is clinical studies. We already have backlogs of good drug candidates, and LLMs won't let anyone run more clinical trials.

This work happens in the real world under real constraints. There's no terminal, no compiler, no proofs, and it takes a lot of money. Not everything can be solved by computation.

silver_gannet92 · 2026-11-02 18:49

I'd push back on 'they're just language models'. In my view these models have already solved a millennium problem. Drug discovery also involves a lot of physics, not just language.

You mentioned ML yourself, too. The LLM boom means ML as a whole gets more funding, and ML infrastructure has become easier to get, cheaper and faster.

I also think bigger and better LLMs can help directly. Just for finding better or more optimized experiments, there's value in a model that has all the current research in context.

sunny_stoat35 · 2026-11-02 16:55

I wouldn't trust an OpenAI product with any proprietary data.

vindex10 · 2026-11-02 17:01

can you elaborate?

totetsu · 2026-11-02 18:30

maybe a reference to the math's researcher who was using openai tooling, and then they released a new discovery very similar to his research?

ivory_magpie23 · 2026-11-02 17:27

Terms like "dual use" and "Preparedness Framework" come from military vocabulary. My view is that the focus on extinction-level risks pulls press and public attention away from the more immediate effect these models are built to have: putting mathematicians and white-collar workers out of work.