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

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!

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!

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

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?

Oh, I thought this was from the company recursive.com who released the very good for its size model Flint.

I am looking at the HF model page https://huggingface.co/ricursiveAI/Ripple-65B-Preview and it looks like I need to wait a day for an Apple Silicon version.

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.

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?

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!

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

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.

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).

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

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.

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

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

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

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

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.

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

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

The comparison chart is befuddling.

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.

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.

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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?