New model is always a good news!

However, the chart comparing the score-by-parameters with existing models is misleading, because the others are all MoE, and this model is not. The article describes it as an architectural decision to optimize against NVIDIA cards(which sounds reasonable, as US labs are relatively easy to gain access to these high-performance chips) but the chart should've been adjusted to reflect the difference, say, by using the heuristic that `effective param size ≈ sqrt(total param size * activiated param size` on MoEs.

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.

Wasn't ricursive's sthick to automate EDA and chip design with LLMs? Why are they releasing open weight agentic coding models and benchmarking on everything but EDA and chip design?

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!

Nice to see another open weight model.

I cannot evaluate how valuable this model is based on the performance table alone because the comparison is kind of narrow. Waiting for third-party benchmarks to provide more information.

I do not have the hardware to run it properly without super-heavy quantization (about q2). Besides, the architecture is not optimized for local usage (sliding window attention also requires KV cache). I think I will just move on.

Looks like this may be the GGUF one: https://huggingface.co/AtomicChat/Ripple-65B-Preview-GGUF

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.

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

Kutia giza kwenye gem

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.