Ripple-65B Preview delivers strong agentic coding results as a fully Apache 2.0 open US model
Ricursive Intelligence is releasing Ripple-65B Preview, a 65-billion-parameter open-weight language model that the company says achieves the top score among open-source U.S. models on the TerminalBench 2.1 coding benchmark at a total R&D cost of $32 million. On other benchmarks, however, Ripple falls behind the likewise newly released western open model Inkling 2 Small. The article Ripple-65B Preview delivers strong agentic coding results as a fully Apache 2.0 open US model appeare
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An AI chip design firm has released an open-source language model with 65 billion parameters that achieved the highest score among open US models on a coding benchmark test. The model was trained on 12 trillion tokens and developed for a reported cost of $32 million, using a standard dense Transformer architecture optimized for Nvidia hardware rather than the more common mixture-of-experts approach. While it outperformed other open US models on one benchmark, it scored lower than a recently released competitor model on other coding tests, though it may be easier to deploy on personal hardware. The model is available under an open Apache 2.0 license, meaning it can be freely used and modified.
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Ricursive AI Releases Ripple-65B Preview: A 65B Dense Open-Weight Model Scoring 80.5 on Terminal-Bench 2.1
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), p
webAI Releases TwIL-8B, an 8.8B Reasoning Model Scoring 98.2 on MATH-500
webAI has released TwIL-8B, an 8.8B reasoning model for math, code and instruction following. The TwIL-8B reasoning model is post-trained from IBM’s Granite 4.2 8B. Weights are on Hugging Face under a non-commercial license. webAI’s launch post frames the pitch around cost per task, not raw scale. The 4-bit build is 4.98 GiB, sized for CPU or modest-VRAM GPU inference. TL;DR Size: 8.79B parameters, dense. 131,072-token context, inherited from the base model. Runs on: CPU o