# Hy-Memory-30B-A3B vs Ripple-65B Preview (slim)

> Hy-Memory-30B-A3B 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/hy-memory-30b-a3b-vs-ripple-65b-preview.md (~9,850 tokens) · this version ~730 tokens · JSON https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ripple-65b-preview.json · canonical https://www.anchorterminal.com/compare/hy-memory-30b-a3b-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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Hy-Memory-30B-A3B scores 61 (C) on agent readiness against Ripple-65B Preview's 53.3 (D), and leads in 3 of 4 scored categories. Ripple-65B Preview leads on agent interface. Both do open-weight models you run yourself.

- Hy-Memory-30B-A3B: C 61, rank #68 of 160 open-weight models · https://www.anchorterminal.com/tools/hy-memory-30b-a3b.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
- Hy-Memory-30B-A3B, good for Agents with an external memory framework that want a local model for the write path on a 24 GB card Ahead on Reach, 85 against 45; Risk, 60 against 55; Documentation and upkeep, 68 against 49.
- 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 Agent interface, 75 against 65.

| Category | Hy-Memory-30B-A3B | Ripple-65B Preview |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 85 | 45 |
| Risk (20%) | 60 | 55 |
| Agent interface (10%) | 65 | 75 |
| Documentation and upkeep (10%) | 68 | 49 |

| Fact (where they differ) | Hy-Memory-30B-A3B | Ripple-65B Preview |
| --- | --- | --- |
| Vendor | Tencent (Hy team) | Ricursive Intelligence |
| Last release | 2026-10-16 | 2026-11-04 |
| Terms last updated | no document linked | not read yet |

Why the edge:
- Reach, Hy-Memory-30B-A3B +40: Hy-Memory-30B-A3B 85 (Memory 25, Tencent's own Q4_K_M GGUF is 18.24 GB, and 18.24 GB × 1.2 = 21.9 GB, which fits 24 GB but not 16 GB.); 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, Hy-Memory-30B-A3B +19: Hy-Memory-30B-A3B 68 (Model card 8, Intended use (the write path of Mem0, MemOS and Hy-Memory), parameters (30B, about 3B active) and languages (English and Chinese, in the metadata)…); Ripple-65B Preview 49 (Model card 14 of 20, no limitations and no knowledge cut-off.)
- Agent interface, Ripple-65B Preview +10: Hy-Memory-30B-A3B 65 (Chat template 20, chat_template.jinja in the repository.); Ripple-65B Preview 75 (Chat template 20, chat_template.jinja in the repository.)
- Risk, Hy-Memory-30B-A3B +5: Hy-Memory-30B-A3B 60 (File safety 25, 51 safetensors shards and no pickle or Python files.); Ripple-65B Preview 55 (File safety 15, safetensors only, less 10 because the documented way to run it loads Ricursive's Python.)