Ricursive Intelligence Just 发布 Riple-65B 预览。 Ripple-65B Preview是一种来自Ricursive的65B密集开放量级模型，从头到尾预先训练用于代理编码。 它是Ricursive的AI芯片设计平台背后的专有模型的通用变体。 主要说法：80.52在Terminal-Bench 2.1上，是美国开放量级模型中自报得分最高的。 每个在测试上击败的MOE对手都携带501B到975B的总参数。
TL；数据
- 大小：65B 稠密(65B 活动)，在12T令牌上预训，上下文最多1M.
- 运行于：数据中心GPU上的BF16(250吨/秒在2xB200上). 实验NVFP4搭建目标NVIDIA DGX Spark.
- 性能：其大小的前沿一级代理编码；跟踪更大的关于知识和科学编码的教育部模型。
- 最佳：终端-奔驰2.1、80.5(自报)。
- 最糟糕的是：SciCode,39.4，在Ricursive自己的5个模型中最低。
Riple -65B预览是什么？
发布内容包括3个检查站：一个指令调制的BF16型，一个NVFP4型，以及一个BF16基型。 Apache 2.0下的所有舰只 Pretrain使用文字唯文体，大多为英语和代码，长度为8K序列。
Ricursive团队表示，研发的全部成本为32M. 这一数字包括梯形实验、数据混合、电子数据、合成数据和最后运行。 Ricursive团队估计，这是前开放式代理编码领导者成本的十分之一。
建筑如何运作？
Ricursive选择了一种密集的变形器而不是“专家混合”来降低基础设施的复杂性。 培训举办了40%的MFU课程，内容简单，没有定制内核，也没有量化。
模型薄而深：一个跨80层5120的d 模型。 它从奥尔莫3的Llama型布局转向Gemma 3N型稳定设计。 关键选择：
- 为推理提供过多的注意，但被更多的滑动窗层和较小的windows所抵消。
- 关注度高、全球无核电站和剩余价值。
- FFN激活剪辑，一种技术也用于gpt-oss.
校对：Soup 它拒绝了未能维持在4B级的更改，例如2-流mHC. 技术员额报告16x代码计算效率收益为4B级。
训练后如何？
训练后跑中训，上下文长，SFT，长文SFT和混合RL. 2M序列长度训练的长文本阶段。 Ricursive没有购买培训后的数据，并建造了自己的合成管道。
RL配方结合了短重采样，分数中心化和线长罚。 Riple在Bench 2.1号航站楼上以1.4M的推出打出80.5分。 比Beam还少70倍
里普尔-65B如何比较基准测试？.
ricursive团队使用OpenCode在内部运行缺失的evals，而Terminus 2则为SWE-bench Pro v2.0 Hard and DeepSWE 1.1. 以下为Ricursive提供的数据。
- Terminal-Bench 2.1:80.5, vs Beam 80.1, Muse Spark Open 76.4, Inkling 63.8，尼莫特龙 3 Ultra 56.4.
- SWE-bench Pro v2.0 Hard:79.1，里库尔西夫的桌子最高分。
- SWE-bench 验证：76.5，位于Beam和Inkling以下。
- 数学：98.3，关于AIME 2025,90.5，关于HMMT 2026年2月。
- 弱点：SciCode 39.4,DeepSWE 1.1 44.3,MCP Atlas 71.67,GPQA Diamond 85.2.
骑行队还以128K的官方Terminus 2牵引装置重置终端-Bench 2.1. 大部分对手都掉在那里；彬跌到70.8. Muse Spark Open 升至78.7
比较表
ricursive模式卡的Riple得分。 竞争者从每个供应商自己的来源得分。 所有自报。
开发者可以在哪里运行？
重量在Hugging Face上。 ricursive列出了Together AI,GMI云和OpenRouter在发布，之后还有烟火。 兹Q 2Z的页面目前标志着这个模式即将出现。
在Datacenter GPUs上，该型号在第8批的2×B200上达到250吨/秒，然后进行量化。 实验性NVFP4检查站对MLP层进行定量，并在BF16中保持注意。 NVIDIA DGX Spark等本地设备的诱导团队将其定位。 也列出一个DFlash2变种。
关键外卖
-65B稠密，12T令牌，1M上下文，Apache2.0.
- 80.5号航站楼 - Bench 2.1号航站楼，位于美国开放权重的顶部。
- 32M全面研发，1.4MRL推出。
- NVFP4构建目标 DGX Spark级本地硬件。
- 在SciCode、DeepSWE和MCP Atlas上有更大的MOE对手。
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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:
- Overprovisioned attention heads for reasoning, offset by more sliding-window layers and smaller windows.
- Gated attention, global NoPE and value residuals.
- FFN activation clipping, a technique also used in gpt-oss.
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.
Comparison table
Ripple scores from the Ricursive model card. Competitor scores from each vendor’s own source. All self-reported.
Where can developers run it?
Weights are on Hugging Face. Ricursive lists Together AI, GMI Cloud and OpenRouter at launch, with Fireworks later. Together AI’s page currently marks the model as coming soon.
On datacenter GPUs, the model reaches 250 tok/s on 2x B200 at batch 8, before quantization. The experimental NVFP4 checkpoint quantizes MLP layers and keeps attention in BF16. Ricursive team positions it for local devices like NVIDIA DGX Spark. A DFlash2 variant is also listed.
Key Takeaways
- 65B dense, 12T tokens, 1M context, Apache 2.0.
- 80.5 on Terminal-Bench 2.1, top among US open weights.
- $32M full R&D, 1.4M RL rollouts.
- NVFP4 build targets DGX Spark-class local hardware.
- Trails larger MoE rivals on SciCode, DeepSWE and MCP Atlas.
Check out the the launch blog, the model card and the NVFP4 checkpoint. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well.
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Asif Razzaq is the CEO of Marktechpost AI Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.
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