[jychang]

> Not quite as "smart" as Fable, but it is incredibly capable.

THIS IS BECAUSE GPT-5.6 SOL IS... just a more posttrained version of GPT-5.5, not a brand new bigger model than GPT-5.5. It's not like how Mythos is bigger than Opus.

OpenAI switching to Sol/Terra/Luna renaming is just a way to rip off people and charge more usage for the same sized model.

GPT-5.6 --------> GPT-5.6 Sol

GPT-5.6-mini ---> GPT-5.6 Terra

GPT-5.6-nano ---> GPT-5.6 Luna

Except OpenAI is about to advertise GPT-5.6 Sol and GPT-5.6 Terra as a whole tier better, than if they named it GPT-5.6 and GPT-5.6-mini.

[threatripper]

My feeling is that GPT-5.5 doesn't lack the raw intelligence so much as it lacks "methodology". I don't know how exactly to put it... how to approach a problem, how to take care of the details and side effects, how to handle unexpected difficulties and bugs, how to not spin out of control, how to write solid code, how to clean up afterwards, how to document, how to give useful feedback... the things that you learn on the job.

So, if they improved a lot in those areas, then GPT-5.6 could become a lot more useful compared to GPT-5.5 even though it might score lower in many benchmarks. It's possible but unlikely since their approach was mostly brute force in the past.

[gck1]

Is Fable really that much different? I almost instinctively create elaborate processes, workflows, set up a bunch of linters and dump research docs any time I bootstrap a new project regardless of what model I'm using. They all spiral out of control if they're not following a predefined process.

[theshrike79]

(Based purely on my feels of using both daily since forever)

Claudes are more creative and get shit done, suggesting and implementing stuff you didn’t ask for but actually kinda needed. Will leave gaps and bugs though. More of an artist, communicates a bunch during the dev process too.

GPT is the engineer, given exact specs it’ll disappear into its dark corner and putter away at doing exactly what was asked, nothing more nothing less. Very very good at spotting gaps from Claude’s get shit done code.

[satvikpendem]

Yes it is. With Fable you don't need to create any sort of elaborate process, it seems to understand the user's intent much better than Opus class models.

[solenoid0937]

There's been a ton of discussion on HN about this but yes. It's a totally different level from Opus.

[Topfi]

Very. Fable 5 is incredibly efficient token wise, second only to GPT-5.5 and is far more affordable run-to-run than the pure input/ouput costs would suggest. Task adherence, task inference, tool calling and task assessment are all significantly ahead of GPT-5.5, especially as the later strongly degrades the second compaction comes into the mix, I suspect because of OpenAIs obsessive optimisation of reasoning tokens into a hard to read (and thus also hard to compact) mess.

Fable 5 meanwhile has a reliable 1m context window and compaction that the few times I did eval it does also do well. Not quite as easy to trust as GPT-5.4, but that's mainly because with thats 272k context window I simply got more familiar with GPT-5.4s incredibly dependable compaction.

Purely concerning encoded information wise, Fable 5 is near or on the same level as Gemini 3.1 Pro in my limited test set focused on those tasks, which in very niche cases can make a difference even with coding, but the truest advantage for coding assistance (besides frontend/UX) is that the code Anthropic models provide is more parsable. Hard to explain, but I can read, follow and mentally map Fable 5 (and even Opus 4.5-4.8) output far more than GPT-5.4 or GPT-5.5 code.

Task orchestration and (more importantly) knowing when to recommend against using such vs Opus 4.8 is another strength of Fable 5 I've use liberally, there is an understanding of what a tasks requirements and the most optimal setup for success are, I have not yet seen before. Computer use is also solid, albeit not as token efficient as GPT-5.5 for my limited use cases.

Lastly, I will say that the classifier has become far less intrusive for me compared to the initial release. During the previous launch window, on Claude.ai I triggered the classifier for simple frontend tasks for regular (not security vocabulary containing) webpages. Now that is no longer the case. Inside Claude Code I occasionally triggered the classifier previously, but after the re-release, I only managed one, even when working with a privacy focused section of the code base containing a significant number of code comments with security and privacy focused wording. That one instance was rectified quickly by trying again, so I really am having a hard time following how others experience the issues some describe. I do have routing to Opus 4.8 without confirmation by me deactivated too, simply because I want to know if it ever happens, so it's not that I missed reroutings.

That all being said, we are still far from a stage where I'd not want to review the output, but yes, I do rate Fable 5 very highly. GPT-5.5 can have a similar ceiling but long horizon has become less usable over GPT-5.4 and in either case, parsing their output is (far more) of a chore. Maybe post training can address some of this, hopeful on the compaction front myself. Also interested in what happened to OpenAI models on AWS Trainium, I was expecting that to be a major boon for their commercial adoption, but haven't heard anything since then...

On the post training front, I am still hopeful that the Gemini team can finally get tool calling and task adherence to an acceptable level as we do need every competitor possible and purely considering the information density the model was trained with, they have great potential.

[deadlast2]

I use Open AI and Claude a lot right like a lot everyday for hours multiple hours. Open AI gives much more value for money than Claude much more I'd say x 10. Mainly I use it for writing fiction books and literally Claude is locked 90% of everyday trying to jip me for tokens. It's not as good at coding for what I do which is a very complicated application. However it is very good at writing it's really good which is why I keep it right but over 90% maybe actually all of my work except the initial draft of a chapter is done by open AI.

[CorbenDallas]

God save us from these ai generated fiction books.

[ppaattrriicckk]

> OpenAI switching to Sol/Terra/Luna renaming is just a way to rip off people and charge more money for the same sized model.

Excuse me, but what are you on about?

Unless I'm mistaken, they have literally(1) stated that it will cost $5 per 1M tokens in, and $30 for 1M output tokens. The same as GPT-5.5.

[1] https://openai.com/index/previewing-gpt-5-6-sol/

[whs]

Sounds like the same problem as condom. Nobody want to buy the "mini" size.

[Davidzheng]

Post-training can have big gains no? I don't think the current sizes at ~1T are saturated in intelligence (it's like saying AlphaGo Master is just a post-trained version of AlphaGo Lee)

[behnamoh]

OpenAI already has a Mythos level model, it's called GPTCyber and before that, it was called gpt-5.5-pro.

[andy12_]

No, GPTCyber is specifically trained for cybersecurity, and GPT-5.5-pro is just an ensemble of many subagents, not an actual model.

Mythos is simply a much bigger model in terms of parameters and I don't think OpenAI will have anything of its size anytime soon (My theory is that OpenAI had given up on scaling up parameters after GPT4.5 flopped).

[behnamoh]

how do you know gpt-5.5-pro is an ensemble? if it is, then how did OpenAI do it? why no other company has been able to pull it off?

[andy12_]

It's pretty much confirmed by OpenAI here [1].

> We generally treat GPT-5.5’s safety results as strong proxies for GPT-5.5 Pro, which is the same underlying model using a setting that makes use of parallel test time compute.

And Gemini also provides something similar. Gemini Deep Think models are pretty much the same thing [2]. As to why no other company uses this, I don't really know. Maybe compute constraints?

[1] https://deploymentsafety.openai.com/gpt-5-5

[2] https://deepmind.google/models/gemini/deep-think/

[jychang]

Plenty of other companies do this. Meta Muse Spark has a "Contemplating" which is this. Kimi had this on their website too, IIRC.

[behnamoh]

Interesting, but how do they "combine" the results of all those parallel agents? How do they know which parts of each agent response is signal vs noise?

[mejutoco]

What is the language of those words (sol, terra, luna). It does not seem to be a single language.

Spanish: Sol, tierra, luna

Italian: Sole, terra, luna

Catalan: Sol, terra, lluna

Portuguese: Sol, terra, lua

Might as well call it gelatto, siesta, fiesta if they think it sounds cool.

[jobigoud]

As you may have guessed, it's Latin.

[satvikpendem]

I wonder what those languages all stem from...

[adammarples]

What a tantalising mystery there is here waiting to be solved!

[mejutoco]

Honestly, I speak a few languages and it did not ocur to me. I deserve the snark, but downvotes?

[ingve]

Latin.