# Hy-Memory-30B-A3B vs LBS-27B (slim)

> Hy-Memory-30B-A3B scores 61 (C) to LBS-27B's 55.8 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

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- 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 LBS-27B's 55.8 (C), and leads in 3 of 4 scored categories. LBS-27B 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
- LBS-27B: C 55.8, rank #118 of 160 open-weight models · https://www.anchorterminal.com/tools/lbs-27b.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 60; Risk, 60 against 50; Documentation and upkeep, 68 against 43.
- LBS-27B, good for Agents with about 96 GB of GPU memory that need to find click targets on dense professional screenshots, or read fine detail in large images, and can run a small crop loop and wait for independent results. Ahead on Agent interface, 70 against 65.

| Category | Hy-Memory-30B-A3B | LBS-27B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 85 | 60 |
| Risk (20%) | 60 | 50 |
| Agent interface (10%) | 65 | 70 |
| Documentation and upkeep (10%) | 68 | 43 |

| Fact (where they differ) | Hy-Memory-30B-A3B | LBS-27B |
| --- | --- | --- |
| Vendor | Tencent (Hy team) | inclusionAI (Ant Group) |
| Licence | Apache-2.0 | Apache-2.0 (the card's metadata and licence section; the LICENSE file sits in the GitHub repository inclusionAI/UI-Venus on its LBS branch, not in the Hugging Face repository) |
| Last release | 2026-10-16 | 2026-10-26 |

Why the edge:
- Reach, Hy-Memory-30B-A3B +25: 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.); LBS-27B 60 (Memory earns 10, since the only build is 54.73 GB of BF16, about 66 GB with a fifth added, which fits the reference machine but not 64 GB.)
- Documentation and upkeep, Hy-Memory-30B-A3B +25: 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)…); LBS-27B 43 (The model card earns 8, with intended use, one limitation and parameters, and no architecture, context, languages or cut-off.)
- Risk, Hy-Memory-30B-A3B +10: Hy-Memory-30B-A3B 60 (File safety 25, 51 safetensors shards and no pickle or Python files.); LBS-27B 50 (File safety earns 15, safetensors only but with the documented route running the publisher's scripts to serve it and to write its system prompt.)
- Agent interface, LBS-27B +5: Hy-Memory-30B-A3B 65 (Chat template 20, chat_template.jinja in the repository.); LBS-27B 70 (The chat template earns 20, Qwen's Jinja file in the repository.)