A fully open-weight Apache 2.0 model scoring 80.5 on terminal-bench 2.1 gives teams a self-hostable alternative to closed coding agents, with 1M context for long-repo workflows. Builders evaluating coding assistants can run the preview on Together AI and benchmark it against proprietary options before licensing decisions. Availability through Together AI makes it accessible across Southeast Asia without local deployment.
有人用 Olmo 3 和 StepFun 的方案从头训了个 65B 开源模型，成本 3200 万美元，上下文 100 万，还说是 Qwen 70B 的平替。
65B 的小模型居然在编程智能体任务上打过了 800B 级别的大家伙，美国开源阵营又有新选择。
五人小团队从零训了个 65B 稠密模型，拿了美国开源在 Terminal-Bench 2.1 的最好成绩，训练细节写得挺实在。
A 65B dense model beating 500B-plus open-weight rivals on agentic coding would reshape assumptions about how much capacity agents actually need. Teams choosing self-hosted coding models should benchmark Ripple against Nemotron 3 Ultra-550B and the other named releases before committing to larger deployments. No benchmark names or independent evaluations are given yet, so treat the state-of-the-art claim as the lab's own.
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.
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