501–510 of 1001 posts
Earlier quoted context omitted.
Umm, Fable only really came out 2 weeks ago, and GPT-5.6 Sol only 1 week ago. Yes, Kimi K3 appears a touch below them both, but above all other models. So I'd say a few weeks behind, not months now...
Mythos/Fable was trained 6 months ago.
So Chinese labs are driving essentially towards commodotized intelligence. Even if its a few months behind the US. Is this a classic 'commoditize my compliment' situation? They want to sell the hardware and infrastructure behind AI and make the software part not the value driver / moat? I can see it. But also even two Chinese labs sinking 100s of millions USD into training isn't exactly commoditization. It's still a…
It's the same reason Meta open sourced Llama and AMD open sourced FSR. When you're behind it is a prudent strategy because it undermines investment in the private frontier. Once you're on top you pull the rug and go closed source. There are no morals in this anywhere to be found. > 100s of Millions That is utter peanuts given the stakes. This is competition between two super powers for the most important technology i…
The children's book "if you give a mouse a cookie" was about exactly this phenomenon
Just in case you were thinking of signing up directly with Moonshot to use the service, they appear to train even on API use: > We may use Content to provide, maintain, develop, support, and improve the Services, comply with applicable law, enforce our terms and policies, and keep the Services safe and secure. Customer who requires restrictions on the use of Customer Content for training or improving Moonshot AI mode…
For anyone else that believes their input needs secrecy, you need to check the corporate plan of any provider for data protection clauses. Most use the cheaper plans as bait to get more training data and free feedback.
Earlier quoted context omitted.
Distillation is not an attack. It simply a way to train a model. Not doing it when you are behind is akin to snatching defeat from the jaws of victory.
It is an attack at a sufficient level of sophisticated analysis. If you destroy the game theoretic first mover advantage, then you destroy the economic incentive to improve things.
So Chinese labs are driving essentially towards commodotized intelligence. Even if its a few months behind the US. Is this a classic 'commoditize my compliment' situation? They want to sell the hardware and infrastructure behind AI and make the software part not the value driver / moat? I can see it. But also even two Chinese labs sinking 100s of millions USD into training isn't exactly commoditization. It's still a…
Umm, Fable only really came out 2 weeks ago, and GPT-5.6 Sol only 1 week ago. Yes, Kimi K3 appears a touch below them both, but above all other models. So I'd say a few weeks behind, not months now...
Just in case you were thinking of signing up directly with Moonshot to use the service, they appear to train even on API use: > We may use Content to provide, maintain, develop, support, and improve the Services, comply with applicable law, enforce our terms and policies, and keep the Services safe and secure. Customer who requires restrictions on the use of Customer Content for training or improving Moonshot AI mode…
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Strictly dominates both Sonnet 5 and Opus 4.8 in both cost and performance: https://artificialanalysis.ai/models/comparisons/kimi-k3-vs-... https://artificialanalysis.ai/models/comparisons/kimi-k3-vs-...
Of course the shills are shifting their tone: this thread as devolved into "sure yup it's totally SOTA but it sucks because it'll use more tokens than Fable to do the same task".
I take it that's the new tune we'll hear for a while. Oh well, at least we won't have to suffer the "they're six months behind, so they're totally useless" anymore.
P.S: I'll make a prediction... We'll hear the "buuuuuuuuut it uses more tokens for the same task" for a few weeks, then we'll get Fable 5.1 and those same posters are going to post "Fable 5.1 is so much ahead you're missing out if you're still on that piece of turd that Fable 5 or K3 is".
Any recommendation for a cost effective subscription service?
Earlier quoted context omitted.
Mythos/Fable was trained 6 months ago.
Should we compare release date to release date? Or training date to training date?
My intuition says that the output quality difference is marginal compared to the change in price especially when taking into account the effects of prompt/context engineering and harness differences.
Essentially: since opus 4.6, working through a model's quirks with prompt/context engineering and harness development will yield significantly better output than just switching models to the latest.