Ricursive AI presenta la vista previa de Ripple-65B: un modelo denso de pesos abiertos de 65B que obtiene 80,5 en Terminal-Bench 2.1

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Ricursive Intelligence just released Ripple-65B Preview. Ripple-65B Preview is a 65B dense open-weight model from Ricursive, pretrained from scratch for agentic coding. It is a general-purpose variant of the proprietary model behind Ricursive’s AI chip design platform. The main claim: 80.52 on Terminal-Bench 2.1, the top self-reported score among US open-weight models. Every MoE rival it beats on that test carries 501B to 975B total parameters. TL;DR Size: 65B dense (65B active), pretrained on 12T tokens, up to 1M context. Runs on: BF16 on datacenter GPUs (250 tok/s on 2x B200). Experimental NVFP4 build targets NVIDIA DGX Spark. Performance: Frontier-level agentic coding for its size; trails larger MoE models on knowledge and science coding. Best: Terminal-Bench 2.1, 80.5 (self-reported). Worst: SciCode, 39.4, lowest of the 5 models in Ricursive’s own table. What is Ripple-65B Preview? The release includes 3 checkpoints: an instruction-tuned BF16 model, an NVFP4 version, and a BF16 base model. All ship under Apache 2.0. Pretraining used a text-only corpus, mostly English and code, at 8K sequence length. Ricursive team states that the full R&D cost was $32M. That figure covers ladder experiments, data mixing, evals, synthetic data and the final runs. Ricursive team estimates that it is one-tenth the cost of the prior open-weight agentic coding leader. How does the architecture work? Ricursive chose a dense Transformer over Mixture-of-Experts to reduce infrastructure complexity. Training held 40% MFU with simple sharding, no custom kernels and no quantization. The model is thin and deep: a d_model of 5120 across 80 layers. It moves from Olmo 3’s Llama-like layout to a Gemma 3N-like design for stability. Key choices: Overprovisioned attention heads for reasoning, offset by more sliding-window layers and smaller windows. Gated attention, global NoPE and value residuals. FFN activation clipping, a technique also used in gpt-oss. Ricursive team built the