# Ornith-1.5-122B vs Ripple-65B Preview (slim)

> Ornith-1.5-122B scores 61 (C) to Ripple-65B Preview's 53.3 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-ripple-65b-preview.md (~9,750 tokens) · this version ~780 tokens · JSON https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-ripple-65b-preview.json · canonical https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-ripple-65b-preview
- Index: https://www.anchorterminal.com/llms.txt · API: https://www.anchorterminal.com/api/v1/index.json · MCP: https://www.anchorterminal.com/mcp · Updated: 2026-11-10
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Ornith-1.5-122B scores 61 (C) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in 2 of 4 scored categories. Ripple-65B Preview leads on risk and documentation and upkeep. Both do open-weight models you run yourself.

- Ornith-1.5-122B: C 61, rank #81 of 160 open-weight models · https://www.anchorterminal.com/tools/ornith-1-5-122b.min.md
- Ripple-65B Preview: D 53.3, rank #135 of 160 open-weight models · https://www.anchorterminal.com/tools/ripple-65b-preview.min.md
- Ornith-1.5-122B, good for Local coding and terminal agents on a 128 GB unified-memory machine or a 96 GB GPU setup, where a clear licence file and an answering publisher aren't required Ahead on Reach, 75 against 45; Agent interface, 85 against 75.
- Ripple-65B Preview, good for Teams with Blackwell GPUs or a DGX Spark who want an Apache 2.0 coding model to try for terminal and agentic coding work, and can run the publisher's code to serve it. Ahead on Risk, 55 against 50; Documentation and upkeep, 49 against 36.

| Category | Ornith-1.5-122B | Ripple-65B Preview |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 75 | 45 |
| Risk (20%) | 50 | 55 |
| Agent interface (10%) | 85 | 75 |
| Documentation and upkeep (10%) | 36 | 49 |

| Fact (where they differ) | Ornith-1.5-122B | Ripple-65B Preview |
| --- | --- | --- |
| Vendor | Ornith | Ricursive Intelligence |
| Licence | MIT | Apache-2.0 |
| Last release | 2026-10-24 | 2026-11-04 |
| Terms last updated | no document linked | not read yet |

Why the edge:
- Reach, Ornith-1.5-122B +30: Ornith-1.5-122B 75 (Memory 10, since Ornith's own MLX 4-bit build is 69.7 GB and needs about 83.6 GB with the fifth added, over 64 GB and inside the reference machine's 107.4 GB, a…); Ripple-65B Preview 45 (Memory 10 of 30, the smallest 4-bit build is Ricursive's NVFP4 at 57.2 GB, 68.7 GB with a fifth added, which is over 64 GB and fits our reference machine.)
- Documentation and upkeep, Ripple-65B Preview +13: Ornith-1.5-122B 36 (Model card 11, it covers intended use, architecture, parameters and context but not limitations, languages or a knowledge cut-off.); Ripple-65B Preview 49 (Model card 14 of 20, no limitations and no knowledge cut-off.)
- Agent interface, Ornith-1.5-122B +10: Ornith-1.5-122B 85 (Chat template 20, a Jinja file in the repository.); Ripple-65B Preview 75 (Chat template 20, chat_template.jinja in the repository.)
- Risk, Ripple-65B Preview +5: Ornith-1.5-122B 50 (File safety 25, safetensors and GGUF only and no Python in any of Ornith's repositories for this size, so the card's --trust-remote-code flag loads nothing from…); Ripple-65B Preview 55 (File safety 15, safetensors only, less 10 because the documented way to run it loads Ricursive's Python.)