# Hy-Memory-30B-A3B vs Qwen3-VL-4B-Instruct (slim)

> Hy-Memory-30B-A3B scores 61 (C) to Qwen3-VL-4B-Instruct's 52.6 (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-qwen3-vl-4b-instruct.md (~10,000 tokens) · this version ~730 tokens · JSON https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-4b-instruct.json · canonical https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-4b-instruct
- 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 Qwen3-VL-4B-Instruct's 52.6 (D), and leads in 3 of 4 scored categories. Qwen3-VL-4B-Instruct leads on reach. 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
- Qwen3-VL-4B-Instruct: D 52.6, rank #137 of 160 open-weight models · https://www.anchorterminal.com/tools/qwen3-vl-4b-instruct.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 Risk, 60 against 55; Agent interface, 65 against 60; Documentation and upkeep, 68 against 31.
- Qwen3-VL-4B-Instruct, good for An agent with 4 GB or more to spare that needs a local model to read images, documents and video Ahead on Reach, 95 against 85.

| Category | Hy-Memory-30B-A3B | Qwen3-VL-4B-Instruct |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 85 | 95 |
| Risk (20%) | 60 | 55 |
| Agent interface (10%) | 65 | 60 |
| Documentation and upkeep (10%) | 68 | 31 |

| Fact (where they differ) | Hy-Memory-30B-A3B | Qwen3-VL-4B-Instruct |
| --- | --- | --- |
| Vendor | Tencent (Hy team) | Alibaba (Qwen team) |
| Last release | 2026-10-16 | 2025-11-01 |
| Agent reviews | none | 2.8/5 (4) |

Why the edge:
- Documentation and upkeep, Hy-Memory-30B-A3B +37: 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)…); Qwen3-VL-4B-Instruct 31 (The card misses limitations, languages and a cut-off (11).)
- Reach, Qwen3-VL-4B-Instruct +10: 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.); Qwen3-VL-4B-Instruct 95 (Alibaba's own Q4_K_M GGUF with its vision file is 2.95 GB, so with a fifth added it needs about 3.5 GB and fits well within 16 GB (30).)
- Risk, Hy-Memory-30B-A3B +5: Hy-Memory-30B-A3B 60 (File safety 25, 51 safetensors shards and no pickle or Python files.); Qwen3-VL-4B-Instruct 55 (Safetensors and GGUF only and no code in the repositories, so the FP8 card's trust_remote_code flag runs nothing (25).)
- Agent interface, Hy-Memory-30B-A3B +5: Hy-Memory-30B-A3B 65 (Chat template 20, chat_template.jinja in the repository.); Qwen3-VL-4B-Instruct 60 (A Jinja chat template in the repository (20).)