[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.