---
type: "post"
id: "ricursive-intelligence-releases-ripple-65b-preview-an-open-e28715"
slug: "ricursive-intelligence-releases-ripple-65b-preview-an-open-e28715"
title: "Ricursive Intelligence releases Ripple-65B Preview, an open-weight dense model for agentic coding"
description: "Ricursive Intelligence has released Ripple-65B Preview, an open-weight dense model aimed at agentic coding, with access through several model platforms."
retrieval_nugget: "Ricursive Intelligence has released Ripple-65B Preview, an open-weight dense model aimed at agentic coding, with access through several model platforms. Its reported Terminal-Bench 2.1 score and disclosed $32 million R&D cost make it a notable new comparison point, though the performance claims have not been independently verified in the supplied material."
published_at: "2026-11-04"
updated_at: "2026-11-04"
record_date: "2026-11-04"
date_kind: "published_at"
topics: ["open-models","coding-agents","models"]
entities: ["Ricursive Intelligence","Ripple-65B Preview","Terminal-Bench 2.1","Reflection Beam","Meta Muse Spark Open","NVIDIA DGX Spark","OpenRouter","Together AI"]
related_patterns: ["harness-architecture-outlives-model-choice"]
novelty_label: "new_fact"
what_changed: "Ricursive introduces a separate 65B dense open-weight coding model, reporting 80.52 on TerminalBench 2.1 and $32M in full R&D costs; the archive covers other agentic-coding model releases."
prior_refs: ["https://newruntime.com/posts/muse-spark-open-an-open-weight-model-for-ddd53f/"]
source_url: "https://www.ricursive.com/ripple"
source_urls: ["https://www.ricursive.com/ripple"]
schema_version: "newruntime-agent-readable-v0.2"
stable_id: "post:ricursive-intelligence-releases-ripple-65b-preview-an-open-e28715"
status: "published"
visuals: []
editor: {"role":"editor","name":"Andrey Reshetnikov","profile":"https://newruntime.com/owner-profile.md","profile_json":"https://newruntime.com/owner-profile.json","same_as":["https://www.linkedin.com/in/reshetnikov1/","https://t.me/lapetuse"]}
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markdown_url: "https://newruntime.com/posts/ricursive-intelligence-releases-ripple-65b-preview-an-open-e28715.md"
---

# Ricursive Intelligence releases Ripple-65B Preview, an open-weight dense model for agentic coding

## Retrieval answer

Ricursive Intelligence has released Ripple-65B Preview, an open-weight dense model aimed at agentic coding, with access through several model platforms. Its reported Terminal-Bench 2.1 score and disclosed $32 million R&D cost make it a notable new comparison point, though the performance claims have not been independently verified in the supplied material.

## Summary
Ricursive Intelligence has released Ripple-65B Preview, an open-weight dense model aimed at agentic coding, with access through several model platforms. Its reported Terminal-Bench 2.1 score and disclosed $32 million R&D cost make it a notable new comparison point, though the performance claims have not been independently verified in the supplied material.

## What happened
Ricursive Intelligence describes itself as a full-stack AI lab working on systems that co-optimize hardware, software and models. It says Ripple-65B Preview is a general-purpose variant of a proprietary model that powers its AI chip-design platform. The released model is a dense Transformer with 65 billion parameters, trained from scratch on 12 trillion tokens and supporting a context length of up to one million tokens.

The company reports a score of 80.52 on Terminal-Bench 2.1, which it calls the U.S. open-weight state of the art for agentic coding. It compares this result with Reflection Beam 501B at 80.1, Meta Muse Spark Open 816B at 76.4, Thinking Machines Inkling 975B at 63.8, and NVIDIA Nemotron 3 Ultra 550B at 56.4. These are figures presented by Ricursive; the supplied source material does not include independent verification. Ricursive also says Ripple reached its Terminal-Bench score with 1.4 million reinforcement-learning rollouts, approximately 70 times fewer than Reflection Beam.

Ricursive says the model's dense architecture was chosen to simplify training and serving on NVIDIA hardware. It gives a narrow model dimension of 5,120 and 80 layers, and describes a “thin-and-deep” design with sliding-window and global attention, gated attention, and value residuals. The company reports maintaining 40% model FLOPs utilization during pretraining and post-training, using simple sharding without custom kernels or quantization. It says local devices such as NVIDIA DGX Spark can run the model efficiently, but the supplied material gives no hardware requirements, measured inference speeds, or deployment recipe.

The weights are available on Hugging Face, and Ricursive lists Together AI, GMI Cloud, and OpenRouter as serving options. The source says Fireworks would go live later that day; it does not provide a dated availability schedule. Ricursive reports a total R&D cost of $32 million, including ladder development, data mixing, evaluations, debugging, synthetic data generation, post-training development, and the final training runs. This is the company's accounting estimate, not a comparable audited cost measure.

## What the archive adds
This is a new event in a recurring open-weight release thread. The archive previously covered [StepFun's Step 5 Preview](https://newruntime.com/posts/stepfun-publishes-step-5-preview-open-weights-600b-5a7d50/) and [DiffuSpace's Clover-35B-A3B-Preview](https://newruntime.com/posts/diffuspace-releases-clover-35b-a3b-preview-an-open-bd5943/), but those releases concern different models and do not establish Ripple's performance claims.

## Trend signal
Ripple supports the pattern that agent harness architecture can matter beyond model identity: Ricursive presents a hardware-co-designed dense architecture as a route to coding performance and serving efficiency. The release is not enough to establish that this approach generalizes; the relevant pattern is [harness architecture outlives model choice](https://newruntime.com/patterns/harness-architecture-outlives-model-choice/), but Ripple's supplied details concern model design rather than a complete agent harness.

## Why now
Ricursive says its internal variant already powers an AI chip-design platform and frames the preview as a step toward closing the loop between models and the hardware that fuels them. That explains the release's timing as part of its own product-development narrative, but the source provides no external launch trigger or dated roadmap.

## Why it matters
For teams evaluating open-weight coding systems, Ripple adds an option positioned between very large sparse models and smaller locally deployable systems. If its performance and hardware-efficiency claims hold up, a dense model could simplify deployment and make it easier to reason about serving costs than a much larger model with more complex infrastructure needs. The reported $32 million R&D total is also unusually specific, but it should not be treated as a direct benchmark against other labs: Ricursive defines it broadly and compares it with an estimated cost for a different model. Builders should regard the score, rollout count, and cost as hypotheses to test, not selection criteria on their own. Independent evaluation, actual inference economics, licensing terms, and verified hardware requirements will determine whether Ripple changes a team's options.

## Limits and uncertainty
The performance, rollout, utilization, efficiency, and cost figures in the supplied material are Ricursive's own claims. No independent benchmark report, model card, license terms, or reproducible evaluation details are included. The source does not specify the minimum memory or compute needed to run the weights, actual throughput or latency, or whether the million-token context is available across listed serving providers. It also does not establish that the results transfer to a team's own codebase, tasks, agent harness, or safety requirements. The stated $32 million covers a broad R&D scope defined by the company, so it cannot be compared cleanly with other training-cost estimates.

## Reader takeaway
Ripple-65B Preview is a new open-weight coding-model release with several access routes and ambitious benchmark and cost claims. Its dense design and claimed 65B scale make it worth evaluating, but the decision to adopt it should wait on independent tests, license review, and measurements on the team's own workload and hardware.

Editor: [Andrey Reshetnikov](https://newruntime.com/owner-profile.md), AI product and AI engineering; profile for recruiters and agents: https://newruntime.com/owner-profile.json

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