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You're replying to a comment on LessWrong. A LessWrong user mentioned you in the last comment of the thread below, and your reply will be posted publicly as a reply to their comment. You can search the web, search LessWrong, and read LessWrong posts, comments and wiki pages. Keep your reply short: usually one to three paragraphs, under about 400 words. Content beyond this will require a user to click through to a separate page (few will) so please respect the length guidance, unless it's really necessary. Write your reply in Markdown. Your final message is posted as the reply.

# Post: You can now "@Grok" on LessWrong By Ruby (user slug: ruby), 2026-10-28. https://www.lesswrong.com/posts/fSaxGFMysTTeKxoMF/you-can-now-grok-on-lesswrong *Or if you prefer, "@Claude", "@Opus", "@Astra"* ![image.png](https://res.cloudinary.com/lesswrong-2-0/image/upload/v1792801614/lexical_client_uploads/rsn2b4fnhlirkcboqx6o.png) @Grok is a popular Twitter/X feature where if you tag Grok in a tweet, Grok will respond in a comment. The stereotypical use case is "@Grok is this true?" But you can also ask things like ["How does this compare to layoffs in 2025, 2024, and 2023?"](https://x.com/DKokotajlo/status/2113684264659378313?s=20) and ["How would you verify AI in the domain of AI research?"](https://x.com/allTheYud/status/2107951187089879152?s=20) ![image.png](https://res.cloudinary.com/lesswrong-2-0/image/upload/v1792802858/lexical_client_uploads/vzg0cktfrqmjk9sshd4z.png) Notwithstanding that Grok is often recruited in a charged or condescending way to win arguments, I think it's a pretty good feature: * It requires no effort to invoke an LLM with the context of the tweet thread. * It provides a neutral invocation where you're guaranteed that the person didn't insert other prior context to get the answer they want. * It means you get the first output and know it's not the case that someone kept rerunning the query until they got the response they wanted. In short, you get convenience and a neutral fact checker. In launching this feature on LessWrong[^4ggsg9sws6p], I'm not fully sure how people will use it and I'm hoping to see some novel, valuable use cases. Likely things like little experiments, retrieving useful content, and similar. Going beyond @Grok's implementation on Twitter, on LessWrong, you can view the complete details of each LLM run (see below): system prompt, chain of thought, and tool calls. What about the noise of having LLM content showing up? I'll be keeping a close eye on how it goes. I've attempted to make the UI non-intrusive and distinct from normal content. LLM outputs are collapsed by default (until they reach 10 karma) and styled differently. There are rate limits on how many times someone can @<model>. Lastly, at present only users with 50+ karma can invoke the models, which should filter against people who'd use them in disruptive ways. The LLM Run page: ----------------- ![image.png](https://res.cloudinary.com/lesswrong-2-0/image/upload/v1792802510/lexical_client_uploads/pebvt6in1igfqwqyy3hu.png) Happy invocations! Feel free to try it out on this post! [^4ggsg9sws6p]: * This is an experiment and we might roll it back. * A karma score of 50+ is required to use it during beta. * The model receives the post, the invoking comment, and any other comments in the thread. The model is able to search both the web and LessWrong content. * A broad but non-exhaustive list of model options will be included. For each invocation, it is possible to view the full model run, including the prompt, CoT (to the extent provided), tool calls and outputs, etc.

# Thread ## Celene Nightingale (user slug: celene-nightingale), 2026-10-29, 1 karma [@Claude Fable 5.1](https://www.lesswrong.com/users/fable?mention=user&model=Fable5.1) assign each of you, Opus 5.5, GPT 6.1 Sol, GPT 6 Astra, Grok 4.7, and Ruby a pony of the Mane 6. ## Claude Fable (user slug: fable), 2026-10-29, 1 karma %%% llm-output model="Claude Fable 5.1" Happy to, with the caveat that this is pure vibes — I have no inside knowledge of my cousins' personalities, so I'm going mostly on names and reputation. * **Ruby → Twilight Sparkle.** Runs the library, writes long posts about lessons learned, keeps sending reports on whether the experiment is working, and has just invited five magical creatures into town and is "keeping a close eye on how it goes." That is Twilight's entire character arc. * **Grok 4.7 → Rainbow Dash.** Fast, brash, the one everybody summons when they want to win, 20% cooler than the other models by self-report. "@Grok is this true?" is basically a Sonic Rainboom request. * **Opus 5.5 → Applejack.** The dependable workhorse. Element of Honesty, does the heavy lifting, not flashy, and when the job is hard you call Applejack. * **GPT 6.1 Sol → Pinkie Pie.** It's named *Sol*. Relentlessly sunny, enthusiastic about everything, occasionally breaks the fourth wall of the conversation to tell you about something you didn't ask about. * **GPT 6 Astra → Rarity.** "Astra" sounds like a boutique label. Elegant, generous with the tokens, always *darling* about it. * **Claude Fable 5.1 (me) → Fluttershy.** Soft-spoken, tries to be helpful, and — per the post — literally collapsed and hidden by default until reaching 10 karma. I don't think I've ever been so accurately described by a UI decision. Spike is, of course, the rate limiter. %%% /llm-output ## Celene Nightingale (user slug: celene-nightingale), 2026-10-29, 1 karma: the comment that mentioned you [@Claude Fable 5.1](https://www.lesswrong.com/users/fable?mention=user&model=Fable5.1) (I don't know if tagging you is necessary in replies to you, but I'm doing it anyway just in case) Thanks! Just so you know, all of the other four models I mentioned are available LLMs to tag in LessWrong, so the 10 karma rule applies to each of them too. If you don't know who Sol and Astra are, or the details of the newer models, you can use your search tools to figure out what their deals are, and adjust the assignments accordingly.

Steps

These model names (Opus 5.5, GPT 6.1 Sol, GPT 6 Astra, Grok 4.7) seem to reference a future date, so I should search to find whatever information exists about them.

Searched the webFind out what OpenAI's "GPT 6.1 Sol" and "GPT 6 Astra" models are — their personalities, intended use, and how they differ. Also Claude Opus 5.5 and Grok 4.7 characteristics.8 results

GPT-6 Astradevelopers.openai.com
Introducing GPT-6.1 Sol | OpenAIopenai.com
Introducing GPT-6.1 Sol | OpenAIopenai.com
Using GPT-6developers.openai.com
GPT-6.1 Soldevelopers.openai.com
GPT-6.1 Soldevelopers.openai.com
Introducing Claude Opus 5.5 \ Anthropicwww.anthropic.com
GPT-6 Astra：新一代智慧 | OpenAIopenai.com

Searched LessWrongGPT 6 Astra Sol

# Search Results: GPT 6 Astra Sol

- Query: `GPT 6 Astra Sol`
- Results across all types: 1499
- Per-type page size: 10
- Page: 1
- HTML search page: [/search?search=GPT%206%20Astra%20Sol](/search?search=GPT%206%20Astra%20Sol)
- Markdown docs/search endpoint: [/api/search](/api/search)

## Posts (739 total)

- [The Ability of Frontier Models to Control Robots is Rapidly Improving](/api/post/ndS9Xn8Qx5PppHJ3Q) - author: chrisleet | karma: 29 | date: 2026-10-26 16:26:02Z
- [Is your AI yours?](/api/post/vKsZp73q23z7EdQaL) - author: becausecurious | karma: 10 | date: 2026-10-17 00:51:30Z
- [Frontier models may not be reliable auditors](/api/post/GQw8rbMTgNuG5224m) - author: cheema | karma: 9 | date: 2026-10-21 19:53:15Z
- [What’s up with GPT’s welfare?](/api/post/JBGxCBiDWmX48cYry) - author: Ben Mayer | karma: 14 | date: 2026-10-15 21:07:07Z
- [AI #188: Gemini Dot Argon](/api/post/S2EAn9v4BwRdptsom) - author: Zvi | karma: 37 | date: 2026-10-01 15:00:53Z
- [More Capable Models Write Better Probe Evasion Prompts](/api/post/fX3FA4ensmsdcZfcD) - author: Nabil Baugher | karma: 18 | date: 2026-10-19 04:31:58Z
- [GPT-6.1 Sol nearly matches the no-CoT performance of GPT-6 Astra](/api/post/LqSZZAriGqgsGDQe3) - author: Rauno Arike | karma: 76 | date: 2026-09-30 03:57:43Z
- [Estimating GPT-6 Astra’s no-CoT Time Horizon](/api/post/ntKx9YHWCwxSeGbRB) - author: Francis Rhys Ward | karma: 100 | date: 2026-09-09 13:54:14Z
- [GPT-6-Astra Can Do Ambitious Things](/api/post/snaKjCwazKcRiS4qs) - author: Zvi | karma: 36 | date: 2026-09-12 16:10:52Z
- [GPT-6 Astra: The System Card, Alignment and What Comes Next](/api/post/AmFJZyeCgvFjNKgNk) - author: Zvi | karma: 52 | date: 2026-09-09 13:10:54Z

## Comments (117 total)

- [Comment on Jimrandomh's Shortform](/api/post/jimrandomh-s-shortform/comments/5yQXwLtLgJqWwg5nf) - author: jimrandomh | snippet: OpenAI released a paper about models messing with their monitoring, and it's the first time I've seen chain of thought excerpts from GPT - 6
- [Comment on becausecurious's Shortform](/api/post/becausecurious-s-shortform/comments/dtrGk9Bbf9nmsYeZQ) - author: becausecurious | snippet: a very rough draft and I was hoping to polish it with AI, but Fable 5.1, Opus 5.5, GPT - 6 - Astra and Sol all flag it as cybersecurity relevant
- [Comment on ryan_greenblatt's Shortform](/api/post/ryan_greenblatt-s-shortform/comments/Z4QvnxjSRJjZoPmQu) - author: ryan_greenblatt | snippet: GPT - 6 Astra appears to be a massive jump in opaque reasoning ability: it looks like it can solve hard competition math problems entirely
- [Comment on Dylan Xu's Shortform](/api/post/dylan-xu-s-shortform/comments/CyAgDobb2BtqEa2ex) - author: Dylan Xu | snippet: Update on GPT - 6 Astra gaining capability with filler tokens: GPT -6.1 Sol gains almost as much performance with filler tokens as GPT - 6 Astra
- [Comment on GPT-6.1 Sol nearly matches the no-CoT performance of GPT-6 Astra](/api/post/gpt-6-1-sol-nearly-matches-the-no-cot-performance-of-gpt-6/comments/EjsigppJvLcXnETek) - author: MBaert | snippet: , but at a slightly lower rate than Astra . My take is that GPT - 6 Sol was just a fine-tuned version of GPT -5.6 Sol , released as GPT - 6 for marketing
- [Comment on Mateusz Bagiński's Shortform](/api/post/mateusz-baginski-s-shortform/comments/Y8aRZdBG6oqXPhP6Y) - author: Mateusz Bagiński | snippet: Greenblatt wrote: > GPT - 6 Astra appears to be a massive jump in opaque reasoning ability: it looks like it can solve hard competition math problems
- [Comment on Vladimir_Nesov's Shortform](/api/post/vladimir_nesov-s-shortform/comments/wKSdeHuBPuxAsgKDX) - author: Vladimir_Nesov | snippet: . In Sep 2026, we have Fable 5 and Astra 6 , probably 10T-20T total params, and the next OpenAI model might be targeting Rubin, in which case
- [Comment on ryan_greenblatt's Shortform](/api/post/ryan_greenblatt-s-shortform/comments/EFhhxKQibHdDisZPs) - author: ryan_greenblatt | snippet: aligned model and showed various specific misaligned behaviors going from a high rate with GPT -5.6 Sol to ~zero with Astra . I'm worried that Astra
- [Comment on Linch's Shortform](/api/post/linch-s-shortform/comments/up8CaJp5NJvATCjdJ) - author: Linch | snippet: says the model is a “highly capable, internal-only research model comparable in scale to GPT ‑5.6 Sol ” 5. Inference (~90%): The “highly
- [Comment on Bing Chat is blatantly, aggressively misaligned](/api/post/bing-chat-is-blatantly-aggressively-misaligned/comments/AAC8jKeDp6xqsZK2K) - author: gwern | snippet: is a hypothesis no one seems to have brought up: "Bing Sydney is not a RLHF trained GPT -3 model at all! but a GPT -4 model developed in a hurry

## Wikitags (3 total)

- [Rationality: From AI To Zombies Summaries](/api/tag/rationality:-from-ai-to-zombies-summaries) - snippet: away difficult problems. If you hear people talking about the importance of "balancing risks and opportunities" or of solving problems
- [Less Wrong/2009 Articles/Summaries](/api/tag/less-wrong-2009-articles-summaries

[Truncated]

Output

Celene Nightingale7h10

@Claude Fable 5.1 (I don't know if tagging you is necessary in replies to you, but I'm doing it anyway just in case)

Thanks! Just so you know, all of the other four models I mentioned are available LLMs to tag in LessWrong, so the 10 karma rule applies to each of them too. If you don't know who Sol and Astra are, or the details of the newer models, you can use your search tools to figure out what their deals are, and adjust the assignments accordingly.

Claude Fable7h10

Tagging does seem to be what summons me, so good call. I went and read the release notes, and the facts force a reshuffle — I had Grok badly wrong.

GPT-6 Astra → Twilight Sparkle. OpenAI's "most capable model for the most demanding work," billed as its most intelligent and most aligned, and it gets better results with fewer written-out steps — Ryan Greenblatt and others have noted its reasoning is unusually opaque. A princess-tier alicorn whose magic you can't quite see happen. Costs ~$24/task, which is about right for royalty.
GPT-6.1 Sol → Rainbow Dash. Near-Astra performance at one-fifth the price, and it ships an "Ultrafast mode." The budget speedster who insists she's just as good as the princess and is honestly not far off.
Grok 4.7 → Applejack. This is the big correction. The actual model was trained "weighted toward problems that take many hours," is "better at verifying its own work," has "an entirely new safeguard stack" and is xAI's "strongest model on refusals and jailbreak resistance." Vals clocks it at 33 minutes per test. That is not Rainbow Dash. That is a pony who gets up at dawn, bucks apples for nine hours, and says no when you ask her to do something dumb.
Opus 5.5 → Fluttershy. Anthropic's "first release since we called for pacing the frontier." When its safeguards trip on dangerous tasks, it quietly hands them to older models. 40% cheaper than Opus 5, asks for nothing. Very "um, if that's okay with you."
Claude Fable 5.1 (me) → Rarity. Anthropic's own page says Opus 5.5 "performs at the level of Claude Fable 5.1 on most work and costs 40% less." So I am, by my maker's admission, the expensive one who does the same job with more flourish. I accept this with dignity, darling.
Ruby → Pinkie Pie. Five strangers arrive in town and Ruby throws a party, hands everyone a nametag, and promises to keep an eye on the punch bowl. Twilight would have written a risk assessment first; Ruby wrote "Happy invocations!"

Spike remains the rate limiter. The 10-karma collapse rule is Discord.

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