Ripple-65B Preview: U.S. Open-Weight SOTA on Agentic Coding

Posted by Philpax 6 hours ago

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Comments

Comment by advael 5 hours ago

Category narrowing to this degree really has diluted the term "SOTA" quite a lot

You mean it's state of the art for this subset of benchmarks you're targeting? No? Well surely it's state of the art for models of a certain size category? Huh, what about models that are available for local inference? O, state of the art for open models of a certain size made in the US specifically. Uh. Neat!

Comment by gpm 4 hours ago

Playing games like this also makes it very easy to accidentally say things that aren't true, like in the headline of the article...

> Introducing Ripple-65B Preview, a Frontier Open-Weight Model

It's not "frontier", it's not "frontier" even in the category of open-weight models (though usually that phrasing would mean frontier over all, and also open weight)... it's only "frontier" in the small sub-category of US made open-weight models.

Comment by GuB-42 3 hours ago

It is technically frontier, if by "frontier" we mean the Pareto frontier.

Being at the Pareto frontier means that you can't get something better on one aspect without compromising on some other aspect. The trick is that it is easy to be at the Pareto frontier by having enough dimensions.

Here, we have 4: score, number of parameters, origin (US) and open-weight. You can get a better score or a lower number of parameters, but you have to compromise on at least one of the other aspects. That's a pretty weak frontier if you ask me.

Comment by derefr 3 hours ago

“Origin” isn’t part of the Pareto frontier because it’s not a quantitative dimension.

(You could say that “ability to be trusted by US government/military users” is quantifiable, but I would argue that by that metric, this model falls behind non-US-originated open-weight models that have more eyes on them!)

Comment by Routine8502 2 hours ago

Discrete dimensions can exist in Pareto considerations. Not that I'm too fond of this game, but given that "Origin" is definitely part of the calculus of model deployment, it makes sense to consider it in a Pareto calculation.

Comment by bastawhiz 1 hour ago

It's just a different pareto frontier because you're filtering out items that don't fit your criteria. "Model was made by Anthropic" would be an equivalent dimension. You would never plot this, you're just reducing the subset of models you're comparing.

By that logic, any model is a frontier model. My GPT-2 clone is a frontier model when you compare it against models that were trained on my homelab.

Comment by getcrunk 1 hour ago

Ahh I run into this issue all the time with ai when applying any sort of dimensional reasoning abstractly it always implicits continuous

Comment by yorwba 4 hours ago

AFAIK the currently biggest US open-weight model is Inkling, which they include in the comparison, so at least their claim is not limited to a size category. Still they score 0.42 percentage points higher than the second-best model on their chosen benchmark, so it's hardly a meaningful lead.

Comment by quixoticelixer- 3 hours ago

Fourth most popular comedy folk rock duo

Comment by hkt 2 hours ago

..in New Zealand!

Comment by frinxor 4 hours ago

The US bit got me as well.

I guess it makes sense - there is a segmentation where users may want to run on domestic "frontier LLMs."

Comment by twelvechairs 2 hours ago

Why should US or China matter if its open weights? Its going to matter a whole lot more whether you are running it locally or where your provider is surely.

[Edit i think the preferred combination for those who actually want some data security at the moment and arent at the scale of self hosting llms is chinese open weight models served in germany or France where theres some better data privacy/security]

Comment by swiftcoder 4 hours ago

For sure, but unfortunately, there's a non-zero chance the state department at some point makes the "US" part of this mandatory for a bunch of companies.

Comment by visarga 5 hours ago

No, the term is well defined. It means being on the Pareto curve of performance vs size, such as: https://tinyurl.com/26sw35fw

Comment by vikramkr 50 minutes ago

Terms can have multiple definitions and language is descriptive not prescriptive. If you intend to communicate that you mean "Pareto frontier" in the current context you need to include the word "pareto." If you don't then "frontier" will be understood as referring to mythos/astra/opus 5.5 tier models in intelligence (max capability regardless of tradeoffs). It doesn't matter that the second definition in the dictionary is the one you want to convey (charitably assuming you're not trying to mislead given the value of the hype of being able to call your model a frontier model) if your audience understands what you're saying to mean the first definiton.

Comment by advael 4 hours ago

Irrelevant. A term can be well-defined and used correctly while also being diluted. In this case, I am pointing out that it's diluted by narrowing the category in which the claim is being made. I have no doubt that in this narrowed category, the term is being used correctly. This has no bearing on the fact that by narrowing the category in that way, this claim is made remarkably less impressive or interesting to many people hearing it, including myself

Comment by bastawhiz 1 hour ago

Is it really on the pareto curve if you get to define arbitrary conditions for what other models are plotted on the curve?

Comment by O_H_E 5 hours ago

well yes but the y axis heavily depends on the benchmark you choose

Comment by epolanski 4 hours ago

By...whom? An ISO standard? Highly cited scientific literature?

A tinyurl pic linked by randoms over the internet?

Comment by dev_l1x_be 4 hours ago

State of the AI-Apocalypse.

Comment by cyanydeez 2 hours ago

It means ot was released recently.

It was already meaningless when US labs used it.

It means new.

Comment by stewartpark 5 hours ago

It's great to see another competitive open weight model. Congratulations!

But as is, it's probably hard to become popular amongst hobbyists and local model enthusiasts like Qwen's 27B class models or MOE models because it's a 65B dense model and inference will be prohibitively slow on consumer grade hardware, and seems like the goal/mission of the company behind this model is to automate chip design.

So if I have to assume and connect the dots, this is for the companies who want to use AI in air-gapped environments for chip design so that they protect their IPs (not leaking to third party AI labs/hostile parties) and can afford lots of server grade inference hardware for a quick unlock with the big dense model.

Neat!

Comment by deepsquirrelnet 8 minutes ago

> it's a 65B dense model and inference will be prohibitively slow on consumer grade hardware

Times are changing and with MTP and DFLASH, dense models are back in style. For qwen 3.8 27b, you can get great token generation rates with MTP and for quality, it's much better than a MoE of comparable size.

Comment by 0x00cl 2 hours ago

Maybe is a bit more niche, but I'm pretty sure there is plenty of people who have enough RAM to run dense models like this one, I don't know how slow is slow but people are willing to sacrifice speed if it means they get a good model running locally.

Look at "SC117/Qwen3.8-Flash-Next-GSQ-RCO-abliterated-GGUF" on HuggingFace, It says it has 3.5M downloads this month and its offering a model that is 2-bit and 3-bit quantized (66GB - 83.9GB).

If this gets quantized to 4-bit (Q4_0) which usually there is very little loss in intelligence compared to the base model, then the model will probably be around 40GB, and looking that people are downloading a model that is at least 20GB bigger, then it might allow even more people to run a bigger model.

But it will only be popular among hobbyist if its as good as it claims to be.

Comment by sorenjan 2 hours ago

Qwen 3.8 Flash Next is a mixture of experts model though. It has 6B active parameters per token, so you can cache large parts of the model in RAM and move it in and out of VRAM as needed. If you do that with a dense model it gets much slower.

Comment by Tade0 1 hour ago

Experts are not typically moved between VRAM and system RAM during inference, as it would be too slow.

What happens instead is that the "dense" parts of the model like e.g. the router are put into VRAM and the remainder of VRAM is occupied by the KV cache and whatever experts still fit.

The rest - usually just the experts - reside in RAM, but it's bo big deal.

Additionally, some models utilise an n-gram table, of which only one row is read per token, so it can actually be stored on disk without issue. Example:

https://huggingface.co/ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RC...

Comment by entrope 22 minutes ago

> Experts are not typically moved between VRAM and system RAM during inference, as it would be too slow.

For some definition of "typical", sure, but it depends on what performance trade someone is willing to accept. Strata LLM is geared around streaming weights as needed. It lets me run DeepSeek-4.1-Flash 1031, heavily quantized (ISTA-DASLab's 2.25 bpw quant), on a single Radeon R9700 plus 256 GB host RAM. 500 tps prefill and 30 tps decode isn't great, but it's a large fraction of what I get with Qwen3.8-27B quantized to fit entirely in VRAM for short prompts, it's faster past about 80k context, and DS4.1F can use a 1M-token context. It takes forever to fill that, or even to refill if the KV cache doesn't match, but it works.

Comment by julianlam 8 minutes ago

Something doesn't seem right. Vanilla llama.cpp can hit 50 t/s on an R9700. Q6 (albeit with limited context of 64k, more if kv cache is quantized) fits entirely on the card.

Comment by stewartpark 2 hours ago

I hear what you're saying - there are people who will use it even at 0.1 tokens/s and run tasks over night or over multiple days if it is good. but Qwen3.8 Flash Next is a MoE model (6B active) so the memory bandwidth/compute required to calculate each token is very different from a 65B (meaning, 65B all active) dense model!

Comment by 59nadir 4 hours ago

Is it competitive overall or just competitive when compared to "U.S. Open-Weight models that are otherwise pretty decent at agentic coding"? The latter does not imply the former.

Comment by regularfry 4 hours ago

What'll be interesting is how it performs relative to Qwen3.8 27b when quantised to the same size. Which, of course, is a comparison they don't want to do.

Comment by 40four 4 hours ago

Well, obviously. These newer American models aren’t competing with Qwen yet. They are competing for American businesses and organizations that can’t/won’t use Chinese models.

They are playing catch-up, but I’m happy to see new players to the game, and it will be interesting to watch how they develop

Comment by wmf 6 hours ago

Congratulations I guess but it's a pretty bad look to not even mention those other open weight models.

Comment by karmakaze 5 hours ago

What's funnier is that after cherry picking models for comparison, they further cherry picked Inking for size comparison instead of better and smaller Beam 1.0

The bar chart also shows Nemotron 3 Ultra instead of smaller and better Nemotron 3.5 Super.

Comment by tedivm 5 hours ago

We've always been at war with eurasia.

Seriously though, it's just easier to pretend that China doesn't exist when they're stomping us this badly.

Comment by stewartpark 4 hours ago

I think it's because the primary users of this model won't even look at "those other open weight models" as options. This is the right chart to present to those who would consider this model. (e.g. US Gov't agencies, chip design industry, etc etc)

Comment by flatline 5 hours ago

Right? These are not likely to be meaningful points of comparison to anyone but the authors.

Comment by wmf 5 hours ago

Maybe Palantir will use it or something. There probably is a market for US models but I don't know how large it will be.

Comment by Onawa 5 hours ago

US Gov won't let Chinese models be hosted on any internal infra, not even supposed to use them for local AI. So there is absolutely a market for US-made or non-Chinese models there.

Comment by schleck8 4 hours ago

Because Chinese models heavily rely on distillation and regularly behave strangely in public benchmarks (where they score much better than in private evaluations), even if many world class researchers are Chinese. It's difficult to quantify to what extend labs like Moonshot would be competitive if they could not distill from OpenAI, now that Anthropic mostly blocks it

https://www.theinformation.com/articles/openais-astra-model-...

Comment by petcat 5 hours ago

Do those other models usually mention all the competition? I haven't noticed

Comment by 2001zhaozhao 5 hours ago

This model scores 80.5 on Terminal Bench 2.1

For reference Qwen 3.8 Flash Next (which is bigger than this model but probably cheaper to run due to MoE) scores 84.3, and Qwen 3.8-27B scores 73.0.

I think this model could be competitive, depending on how much it was benchmaxxed compared to Qwen?

Comment by bluejay2387 4 hours ago

There are several situations where some of us are explicitly prohibited from using models from companies not under the jurisdiction of the United States. It doesn't mean these models are competitive when looked at from a global view but it is also not an irrelevant differentiation.

Comment by Computer0 3 hours ago

It's the same positioning Mistral makes.

Comment by antoineMoPa 3 hours ago

I trained a tiny LLM earlier this year, it's fits under 10mb and it's completely useless, can I call it SOTA for my neighborhood?

Comment by thesnarkitecht 4 hours ago

Speaking as someone who has High-Memory Mac Studios, it is great that there is finally a model that will run at usable speeds with high quality. In my experience, the sweet spot for Mac Clusters is around 200GB of weights, which this is far under. Congratulations to the recursive team for building an open model that will actually run!

Comment by Tepix 2 hours ago

It will be slow on Macs, it's a dense model.

Comment by syntaxing 5 hours ago

When a post doesn’t mention Qwen for comparison, you know the performance isn’t going to be great.

Comment by ethan-boneh 5 hours ago

I mean Qwen 3.8 27B hits 73% on TB2.1 and 42.2% on DeepSWE....

Comment by minimaxa 4 hours ago

Agentic coding benchmarks look great.

I think we will find the market is going to move into omni multi-modal models: text, moe, agentic tasks, voice, images, and video all in one LLM.

Comment by a11r 4 hours ago

I understand the desire to look the best in some self defined category. But not showing any comparison against the actual leaders in open weight renders all of the benchmarking completely information-less for the actual end users (who care about real performance comparison against the real field).

Comment by roundup 2 hours ago

Global open-weight SOTA ... https://huggingface.co/moonshotai/Kimi-K3.1-Pro

Comment by purplemantis 4 hours ago

These are pretty odd benchmarks to use. Terminal Bench 2.1 is old. SWE-bench was disavowed by OpenAI. DeepSWE is clearly benchmark-hacked by Gemini.

Comment by dofm 5 hours ago

So we have a Recursive and a Ricursive, both making LLMs?

Comment by wmf 5 hours ago

Time to start Rickursive, the lab that will never give you up and never let you down.

Comment by 6thbit 5 hours ago

And it should acquire the existing two

Comment by neutronicus 5 hours ago

Roll them up, you might say.

Comment by throwaw12 5 hours ago

we need RichUrsive

Comment by xyzrt 5 hours ago

One making self-improving AI via AI-chip co-design, the other relying only on software for self-improving AI.

Comment by dofm 5 hours ago

I hope they never have to call each other about anything. It could get confusing if neither knows who is the base case.

Comment by giwook 5 hours ago

There seems to be some recursion going on here.

Comment by mrinterweb 3 hours ago

Not comparing to Qwen3.8 27B gives cherry-picked benchmark comparison energy. Still, Ripple-65B outscores Qwen3.8 27B, according to the reported benchmarks.

Comment by walrus01 1 hour ago

Showing a terminal bench 2.1 score at the top is very misleading when terminalbench 4.0 is the current metric as a very quick glance for anything we care about for 30B to 180B size possible to locally-host models.

Comment by kylehotchkiss 1 hour ago

Ouh this is the size of model I want to see more of (just got a 98gb m5u studio)

Comment by soundworlds 5 hours ago

Not to be confused with https://recursive.com/

Comment by jszymborski 5 hours ago

Would love a 27B model. Wonder if we'll get a distillation.

Comment by kylehotchkiss 1 hour ago

Ouh that fits right on a base M5U Studio! Excited to try it.

Comment by RGS1811 5 hours ago

The comparison chart is befuddling.

Comment by cyberax 4 hours ago

So the model is "meh" at best, but the cost of the training is interesting. So we can extrapolate that in 3 years the cost will be below $5m for a model like this.

It's very well within the capabilities of any reasonable organization, maybe even crowd-funded.

Comment by ltbarcly3 2 hours ago

What is this thing where model comparisons blatantly pick other crap models and show themselves at the top left (cheap and effective)? Put all the open models, or put all the cheap models, or whatever, but just cherry picking is egregious.

Comment by johnnyApplePRNG 5 hours ago

[flagged]

Comment by teo-mateo 3 hours ago

"get out of my feed" what a nasty attitude.

Comment by 6thbit 5 hours ago

I'm sorry but what is "U.S." implying here?

Is this sponsored by the us gov? did it only train on US data? does it only work on the US?

Comment by conception 5 hours ago

It's a US group that made it, in the same vein that OpenAI and Anthropic have US models.