{
  "id": 18170143,
  "title": "Ripple-65B Preview: U.S. Open-Weight SOTA on Agentic Coding",
  "url": "https://urgent.news/2026/11/04/ripple-65b-preview-u-s-open-weight-sota-on-agentic-coding",
  "topic": "ai",
  "section": "AI",
  "published": "2026-11-04T18:53:29.000Z",
  "source": {
    "name": "Hacker News",
    "slug": "hacker-news",
    "url": "https://www.ricursive.com/ripple"
  },
  "original_language": "en",
  "account": "Ricursive Intelligence, a full-stack AI lab, has achieved a U.S. Open-Weight State-of-the-Art (SOTA) on Agentic Coding with their proprietary model, Ripple-65B Preview. This general-purpose variant of their AI chip design platform model was trained from scratch to power their chip design platform.\n\nThe model, pretrained on 12 trillion tokens with a support for up to 1M context length, outperformed larger US open-weight models such as Reflection Beam 501B, Meta Muse Spark Open 816B, Thinking Machines Inkling 975B, and NVIDIA Nemotron 3 Ultra 550B in agentic coding tasks. Specifically, it achieved a U.S. open-weight state-of-the-art score of 80.52 on TerminalBench 2.1, surpassing the previous SOTA.\n\nRicursive Intelligence's model is dense, providing strong reasoning performance with low-latency token generation, and has a small parameter envelope that enables efficient local device running. Ripple-65B Preview is available on platforms such as Hugging Face, Together AI, GMI Cloud, and OpenRouter.\n\nThe development and training of Ripple-65B Preview cost only $32 million, significantly less than the estimated $100 million spent by OpenAI for GPT-4 training in 2023, and the projected $10-100B for training frontier AI models by 2026-2027. Ripple-65B Preview was designed for efficient training and serving on NVIDIA hardware, with a thin-and-deep model aspect ratio to reduce infrastructure complexity while still enabling strong reasoning and agentic performance.",
  "summary": null,
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}