Ricursive Intelligence open-sources dense Ripple-65B Preview under Apache 2.0

Ricursive Intelligence released Ripple-65B Preview on 4 November 2026 under the Apache 2.0 licence, publishing BF16 and NVFP4 instruction-tuned checkpoints alongside BF16 base weights on Hugging Face. Rather than adopting a multi-hundred-billion-parameter sparse mixture-of-experts layout, Ricursive trained a dense 65-billion-parameter Transformer (80 layers, hidden dimension 5,120) combining gated attention, global NoPE (no positional encoding on global layers), value residuals and feed-forward clipping to sustain 40% model FLOPs utilisation without custom GPU kernels.

Pretrained across 12 trillion tokens with context lengths up to 1 million tokens for $32M in total end-to-end R&D spend (including ablations, staff compute and post-training), the architecture proved 16 times more compute-efficient on code than the Olmo 3 scaling ladder at the 4B scale. Post-training required 1.4 million reinforcement-learning rollouts, roughly 70 times fewer than Reflection’s Beam model.

On agentic coding evaluation TerminalBench 2.1, Ripple-65B Preview scored 80.52, edging US open-weight MoE peers up to 15 times larger in total parameter count: Reflection Beam 501B (80.1), Meta Muse Spark Open 816B (76.4), Thinking Machines Inkling 975B (63.8) and NVIDIA Nemotron 3 Ultra 550B (56.4). Because all 65B parameters fit in NVFP4 on compact desktop systems such as NVIDIA DGX Spark, teams can run local coding agents without multi-node MoE memory footprints. Hosted inference is live via Together AI, GMI Cloud and OpenRouter, with per-provider pricing set by each host.