The best open models today have been “good enough” to do most knowledge work for a few months. This might be the first US one good enough too. That’s a big deal.
MoA schemes that use proposers/task-separation/supervisory roles can still benefit from very fast diffusion models like mercury, the overall speed doesn't have to be bottle-necked by the slowest model -- that depends on the arrangement it's deployed in.
but to your point , yeah diffusion models can be kind of brain dead without proper coaxing.
my ideal arrangement is a smarter/bigger model supervising lots of proposals from diffusion models. The diffusion models spit out a big proposal each, the bigger model can vet something as minor as the headline or filename for each proposal, time still ends up being saved.
The most important thing here is training was entirely nvfp4 - this is best model by far that has ever been trained in that precision and explains why they were able to do it so fast
If you cant beat Deepseek on price im not using it. I actually dont care about beating DS in capabilities, I dont think you need more intelligence than Deepseek for 99% of the things people work on, and I dont want LLMs to do everything, that kind of defeats the point. I just need a smart fuzzer and to offload some tedious busy work.
So why should I pay for 3-20x more for a few small percentage points in preformance. (I would prefer DS did some things better, but largely I can force my desired behavior), but I could only have access to Deepseek 4.1 and still beat almost everyone using opus/fable/astra on a productivity basis, I need automation tools not "do my entire job so Im worthless tools", and DS has already more than gotten there for me.
Very refreshing to see benchmarks where the reporting team is not the clear winner. They are claiming to be better than GLM 5.3 flash, which is impressive if it holds up outside of benchmarks.
Given the time to market and the date in which these labs were founded, I find it far more likely that the lag was representative of them establishing proper internal (RL etc) tooling.
I feel like this is a dumb question, but why are we spending any effort on wine comparisons? Is there a way to get rid of experts on these to just be ones for code?
Just looking through the benchmarks, the model seems to be very high on visual/spatial capabilities. So I assumed they just tried some hard tasks on it and was surprised by wine identification and thought it was surprising that it could identify wines.
It's not like they're training on that. That's just a good showcase of how the model is good at generalizing & understanding (not benchmaxxed). You might want something similar to these experts for tasks like detecting anomalies or discoloration or whatever.
You could probably run a Q2-Q3 with 2x DGX Sparks. So...$12k at today's prices. Plenty of folks are running DS 4.1 Flash with Strata and some GPU + bulk in system memory. Maybe more like $3k-$4k? But speeds come down quite a bit.
I don’t like it. Poor form to squat on an old companies name like that, one which has a rich legacy in AI. I know the trademark was cancelled so all I can say is I don’t like it.
Open weight US based Opus 5.5 within 12 months.
(I am assuming Inception is an American company, not sure TBH)
My read on Diffusion LLMs has been that it's cool tech, but only useful for things like like code autocomplete or editing a block of text.
They don't beat autoregressive models on quality per compute, assuming you are serving lots of users and can batch requests.
but to your point , yeah diffusion models can be kind of brain dead without proper coaxing.
my ideal arrangement is a smarter/bigger model supervising lots of proposals from diffusion models. The diffusion models spit out a big proposal each, the bigger model can vet something as minor as the headline or filename for each proposal, time still ends up being saved.
Seems unlikely. Maybe open weight at all near there? That'd be great.
Frontier and open weights? Yes.
Frontier and _US_ open weights? No.
I'd expect a US open weights provider to suddenly get bought or go non-open if they get near Opus 5.5 anyway.
Without hundreds of millions in 9 days a small team is producing a model that can beat DeepSeek 4.1 and a platform to tune it.
Optimistic for the future.
So why should I pay for 3-20x more for a few small percentage points in preformance. (I would prefer DS did some things better, but largely I can force my desired behavior), but I could only have access to Deepseek 4.1 and still beat almost everyone using opus/fable/astra on a productivity basis, I need automation tools not "do my entire job so Im worthless tools", and DS has already more than gotten there for me.
cries in GPU racks