Inkling 2 Small | Hacker News Reader

Inkling 2 Small

thinkingmachines.ai

148 pointsPhilpax53 comments

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.

Open weight US based Opus 5.5 within 12 months.

> Open weight US based Opus 5.5 within 12 months.

Seems unlikely. Maybe open weight at all near there? That'd be great.

Why does that seem unlikely? The difference between frontier and open weights has been much less than 12 months for a while now.

US open weights are much worse, way more than 12 months apart compared to frontier.

> Why does that seem unlikely? The difference between frontier and open weights has been much less than 12 months for a while now.

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.

People say this about the frontier every time, and the open models catch up in a matter of months every time.

The open models catch up, the _US_ open models catch up much more slowly. Different incentives and cultures.

Inception Mercury 3 and Particle-1's model are also good.

(I am assuming Inception is an American company, not sure TBH)

Agree, Mercury 3 is one of my favorite models right now...the speed is such a quality of life improvement.

Really? I'm surprised. What do you use it for?

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.

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.

Sorry to put it bluntly, but, Mercury is sh*t in comparison. And I have genuinely tried a few times to use it since the speed is so good. But nop.. it just isn't there.

For my own purposes, I found Mercury Decide to be excellent. Better than Jev at what I threw at it. Inception did a really great job with it.

Muse Glimmer is pretty good.

Could you give some examples what makes them good, thanks

I give it 2.

That’d be the inflection point I’m waiting for!

Things have been remarkably "not terrible" since approximately the release of Laguna S 2.1 a while back in early summer 2026, which is now outdated within the past 6 month time frame by other, better non CN open weight models.

I'm not saying that any of them are to the level as a deepseek v4.1 flash 1017 or similar but they're catching up. The most recent huge mistral release is also grounds for an optimistic view.

It's a nice, domestic open weight model.

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.

Correctly if I’m wrong but isn’t inkling made by a very well funded startup from an ex - OpenAI exec iirc?

Yes, Mira Murati's Thinking Machines. They are apparently trying to raise $5B at $40B, after raising $2B at $10B last year.

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.

Not beating dsv4.1 at all

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

Oh nice they’ll run well on sparks

edit: nm I see its a bit big

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.

Is it SOTA yet? No. Are the domestic open weights labs demonstrating incredible velocity? Undeniably.

Distillation is the tide that lifts all boats

Imagine if Mira Murati and John Schulman actually know what they're doing?

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.

Seems to be pretty close to SOTA on design. I’m excited to try it out with my agentic design system.

I can’t seem to find the cost? The craziness of deepseek flash 1017 is how ridiculously cheap it is for how capable it is.

AA lists it as $0.30/0.03/1 and cost per task is $1.18, about double that of deepseek

Wow, that's pretty good.

It’s about 2-3X more expensive so about the same token efficiency as 1017. I can see this being a good option for the US.

This is amazing! Can't wait to trying this out. Also, crazy that I found this on LinkedIn with more upvotes and discussion than the HN thread has.

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?

What if your coding project is related to wine? So you would want facts about wine to be inside the weights, not through web search.

If it was about wine, why would I want one with a training data cutoff? I'd much rather have one that can query a database about wine

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.

Source they didn't train on it?

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.

Damn their last "Inkling Small" model was only 266B and now this is more than double.

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.

> Small: 594 billion parameters

cries in GPU racks

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.

So basically comparable to DS4.1? But why bother to make something we already have?

Because its american.

lol

Sovereign open source AI is a real thing, lots of companies want to own their own intelligence but also don't trust the models coming out of China.

Why would you trust anything coming out of USA?

The vast majority of users on this website wouldn't be trusting something coming out of the USA, they'd be trusting something in the USA.

Do you mean to ask why non-American companies would trust anything coming out of the USA? Because American companies tend to have a lot of trust for other American companies.

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