Yes, but we are getting close to the point where the difference exists, but it is less and less significant. Tbh, Qweb27b is perfectly find for a lot of tasks; anecdotally, the difference lies less and less in quality of responses, but in speed. Local models have a hard time there for a reason ( the cheapest stuff you can get now is stuff like previously mentioned 128gb ryzen ai ).
So trade-offs are there, but do those matter to all people the same way ( are they not equal in the same way to everyone )?
I would say from experience, that yes the local models today are as good as the old frontier models. That was my original claim. I am sticking too it. It won't be long before local models match say fable 5.x as well. And people will still say "it's not as good as x". No it's not but it's as good as the thing everyone freaked out about 6 months ago. If that wasn't good enough for all y'all then then why did you all use it? Exactly.
This is the elephant in the room that OpenAI and Anthropic specifically aren't talking about -- when "good enough" is surpassed.
Because there are a large number of use cases (and large sub-portions of others) where genius-level AI isn't required.
Which means once that threshold is surpassed in people's relevant fields, available margin on that is going to collapse to commodity levels.
It's difficult to see how either pure-play AI company maintains its valuation once that happens. Their TAM is based on capturing a big chunk of all work, not just that which requires the highest intelligence.
> its not done training, why would they release a model that hasn't finished training?
Because clearly they have no problem with releasing newer versions of models even just a week apart.
> besides, we know anthropic are sitting on models too
It is from your crystal ball or from other bullshit you heard from Anthropic employees on Twitter?
We all know Anthropic had Mythos and Fable, and they turned out to be completely normal models, entirely in line with the capability of their predecessors.
All they do is lie, and you're believing their lies.
The poster is not claiming Bel is a secret AGI. Just that it exists and only exists internally at the moment.
It's rumored to be over 10T parameters. When released it'll probably be very good at certain tasks, albeit slow and expensive and not necessarily "wiser". You don't have to make this a binary.
Also, Mythos was in fact a significant step-up in several ways. It fits the trend line, but only because the trend line for LLMs is quite steep. Plus Astra is still in many ways less intelligent than Fable/Mythos despite being released much later.
> its not done training, why would they release a model that hasn't finished training?
> Because clearly they have no problem with releasing newer versions of models even just a week apart.
You can see how it is pure non-sequitur, right?
> We all know Anthropic had Mythos and Fable, and they turned out to be completely normal models, entirely in line with the capability of their predecessors.
Fable was absolutely not in line with capabilities of other models when released. For cybersec work it was much, much better.
Yes and if I remember the drama correctly - Kimi's license or terms of use says that for commercial use cases (or was it user count?) - you must declare credit to Moonshot and Kimi.
It's important to mention: they were compliant, because they trained the model at an AI hosting provider that had a partnership with Moonshot AI, but Moonshot didn't know Cursor was a customer.
Note that something that helped the misinformation was that, on Twitter, there were Kimi employees expressing their surprise that the base model was Kimi K2.5, and their indignation that Cursor didn't credit Kimi. They later deleted their tweets (what I infer from that is that some employees were not aware of some pre-existing agreement or understanding between Cursor and Kimi until the drama happened).
How can distilled opus become better than original? There are numbers of reports including anthropic that kimi team was participating in fraudulent activities
Agentic reasoning and tool use
Coding and data analysis
Computer-use agent development
Computer vision
Moonshot (Kimi models) employed hundreds of fraudulent accounts spanning multiple access pathways. Varied account types made the campaign harder to detect as a coordinated operation. We attributed the campaign through request metadata, which matched the public profiles of senior Moonshot staff. In a later phase, Moonshot used a more targeted approach, attempting to extract and reconstruct Claude’s reasoning traces.
And when you here unsubstantiated rumours* that say Anthropic has been sending exchanges to say Alibaba's Qwen, will you als oconclude the same about the entire US AI industry?
Agree, but it seems dependent on field. One day I wanted a browser extension made, and 5.2-codex-max added hundreds of lines of code several times, and for 15-20 iterations I did not change one thing, or even have an opinion on what it was doing. This is extremely uncommon for other models for me, even Opus I would say. And yes, I mostly do small green-field things and not even that works all the time, even if LLMs are clearly at their best there.
reply