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), pretrained on 12T tokens, up to 1M context.
- Runs on: BF16 on datacenter GPUs (250 tok/s on 2x B200). Experimental NVFP4 build targets NVIDIA DGX Spark.
- Performance: Frontier-level agentic coding for its size; trails larger MoE models on knowledge and science coding.
- Best: Terminal-Bench 2.1, 80.5 (self-reported).
- Worst: SciCode, 39.4, lowest of the 5 models in Ricursive’s own table.
What is Ripple-65B Preview?
The release includes 3 checkpoints: an instruction-tuned BF16 model, an NVFP4 version, and a BF16 base model. All ship under Apache 2.0. Pretraining used a text-only corpus, mostly English and code, at 8K sequence length.
Ricursive team states that the full R&D cost was $32M. That figure covers ladder experiments, data mixing, evals, synthetic data and the final runs. Ricursive team estimates that it is one-tenth the cost of the prior open-weight agentic coding leader.
How does the architecture work?
Ricursive chose a dense Transformer over Mixture-of-Experts to reduce infrastructure complexity. Training held 40% MFU with simple sharding, no custom kernels and no quantization.
The model is thin and deep: a d_model of 5120 across 80 layers. It moves from Olmo 3’s Llama-like layout to a Gemma 3N-like design for stability. Key choices:
Ricursive team built the recipe through laddering experiments starting from Olmo 3’s ladder. It rejected changes that failed to hold at 4B scale, such as 2-stream mHC. The technical post reports a 16x code compute-efficiency gain at 4B scale.
How was it post-trained?
Post-training ran midtraining, long context, SFT, long-context SFT and mixed RL. Long-context stages trained at 2M sequence length. Ricursive bought no post-training data and built its own synthetic pipelines.
The RL recipe combines truncated importance sampling, score centering and a linear length penalty. Ripple hit 80.5 on Terminal-Bench 2.1 with 1.4M rollouts. Ricursive puts that at roughly 70x fewer than Beam, which reported over 100M rollouts.
How does Ripple-65B compare on benchmarks?
Ricursive team ran missing evals in-house using OpenCode, and Terminus 2 for SWE-bench Pro v2.0 Hard and DeepSWE 1.1. Scores below are Ricursive’s presented data.
- Terminal-Bench 2.1: 80.5, vs Beam 80.1, Muse Spark Open 76.4, Inkling 63.8, Nemotron 3 Ultra 56.4.
- SWE-bench Pro v2.0 Hard: 79.1, the top score in Ricursive’s table.
- SWE-bench Verified: 76.5, below Beam and Inkling.
- Math: 98.3 on AIME 2025 and 90.5 on HMMT Feb 2026.
- Weak spots: SciCode 39.4, DeepSWE 1.1 44.3, MCP Atlas 71.67, GPQA Diamond 85.2.
Ricursive team also reran Terminal-Bench 2.1 with the official Terminus 2 harness at 128K. Most rivals dropped there; Beam fell to 70.8. Muse Spark Open rose to 78.7, still below Ripple’s 80.5.
