Ripple-65B Preview: Ricursive's 65B open-weight model tops US rivals on agentic coding
Ricursive Intelligence released Ripple-65B Preview, a 65B dense open-weight model trained from scratch on 12T tokens, claiming the top US open-weight score on Terminal-Bench 2.1 (80.5) and SWE-bench Pro v2.0 Hard (79.1).
Why it matters
A 65B dense model that claims to beat 500B-975B MoE rivals on agentic coding benchmarks, built for a claimed $32M end-to-end, under Apache 2.0.
What you could build with it
An indie developer building a terminal coding agent could run Ripple-65B Preview as the default model on two B200 GPUs and measure task completion against a larger hosted model, testing whether the smaller Apache-2.0 model cuts inference cost without losing agentic-coding quality.
Does it hold up?
Too early to judge: weights landed Nov 4 and all benchmark figures are vendor-reported; OpenRouter lists the model but caps context at 16K tokens versus the claimed 1M, and no independent evaluation has appeared yet.
Built with Ripple-65B Preview
- Ricursive AI Releases Ripple-65B Preview: a 65B dense open-weight model scoring 80.5 on Terminal-Bench 2.1article · MarkTechPost breaks down the release: 65B dense, 12T tokens, 1M context, three checkpoints (BF16, NVFP4, base) under Apache 2.0.
- AlphaChip creators' Ripple-65B: top US open coding AI for $32marticle · Coverage of the $32M R&D cost claim, the AlphaChip founders, and the 250 tokens/sec throughput on two B200 GPUs.
- Ripple 65B vs Claude Opus 5.5: two different examsarticle · A careful check of the benchmark claims: Ripple's 80.5 is on Terminal-Bench 2.1 while Opus 5.5's 66.4 is on Terminal-Bench 4.0, so the numbers are not directly comparable.
Learn more
First spotted on article: source.
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