Ricursive Intelligence Releases Ripple-65B Preview Under Apache 2.0
A 65B dense model matching or beating a 501B sparse model on Terminal-Bench 2.1 would shift assumptions about how much parameter count matters for agentic coding. The Apache 2.0 licence and local runnability mean teams can self-host a competitive coding agent without API dependence. Benchmark figures here are company-reported, so independent verification should be the first stop before adopting it.
Ricursive Intelligence released Ripple-65B Preview, an open-weight model it says beats much larger US models on agentic coding. Ripple scored 80.5% on Terminal-Bench 2.1, ahead of Reflection's Beam-501B at 80.1%, which activates 23B of 501B parameters per task. The company says Ripple was trained from scratch on 12 trillion tokens with a US$32 million compute budget. The model supports up to 1 million tokens of context, runs locally on a DGX Spark, and ships under Apache 2.0.
Ok, *this is impressive*: Ricursive Intelligence just released Ripple-65B Preview, an open-weight AI model that it says outperforms much larger US models on a key agentic coding benchmark. And even runs locally on my DGX Spark.
Ripple scored 80.5% on Terminal-Bench 2.1, narrowly ahead of Reflection’s Beam-501B at 80.1%, according to the companies’ reported results!
The size comparison comes with a detail: Ripple uses all 65B parameters for each task, while Beam activates 23B of its 501B parameters.
Ricursive says Ripple was trained on 12 trillion tokens with a $32 million compute budget - from scratch. The model supports up to 1 million tokens of context and is available under Apache 2.0.
