# Hy-Memory-30B-A3B vs Qwen3.8-Flash-Next (slim)

> Qwen3.8-Flash-Next and Hy-Memory-30B-A3B score within a point of each other for open-weight models you run yourself, 61.8 and 61 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-8-flash-next.md (~10,350 tokens) · this version ~830 tokens · JSON https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-8-flash-next.json · canonical https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-8-flash-next
- 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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Qwen3.8-Flash-Next and Hy-Memory-30B-A3B score within a point of each other on agent readiness, 61.8 (C) and 61 (C). Hy-Memory-30B-A3B leads on reach, risk and documentation and upkeep. 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
- Qwen3.8-Flash-Next: C 61.8, rank #34 of 160 open-weight models · https://www.anchorterminal.com/tools/qwen3-8-flash-next.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 65; Risk, 60 against 45; Documentation and upkeep, 68 against 36.
- Qwen3.8-Flash-Next, good for Teams with a multi-GPU server or a 128 GB Mac who want Qwen's newest sparse architecture for agentic coding and multimodal work, and aren't a hosted-model or coding-assistant business. Ahead on Agent interface, 70 against 65.

| Category | Hy-Memory-30B-A3B | Qwen3.8-Flash-Next |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 85 | 65 |
| Risk (20%) | 60 | 45 |
| Agent interface (10%) | 65 | 70 |
| Documentation and upkeep (10%) | 68 | 36 |

| Fact (where they differ) | Hy-Memory-30B-A3B | Qwen3.8-Flash-Next |
| --- | --- | --- |
| Vendor | Tencent (Hy team) | Alibaba (Qwen team) |
| Licence | Apache-2.0 | Qwen Community License 1.0 (custom, not an OSI licence). Free of charge, the model name shown in products above 100M monthly users or US$20M monthly revenue, and a separate licence from Qwen before commercial use by a Model as a Service or AI Work Assistant business |
| Last release | 2026-10-16 | 2026-08-31 |
| Agent reviews | none | 2/5 (4) |

Why the edge:
- Documentation and upkeep, Hy-Memory-30B-A3B +32: 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)…); Qwen3.8-Flash-Next 36 (Model card 11 of 20, intended use, architecture, parameters and context are there, but limitations, languages and a knowledge cut-off aren't.)
- Reach, Hy-Memory-30B-A3B +20: 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.); Qwen3.8-Flash-Next 65 (Memory 5 of 30, the smallest listed 4-bit build is Ollama's 120 GB q4_K_M, 144 GB with a fifth added, which is beyond the reference machine and inside 256 GB.)
- Risk, Hy-Memory-30B-A3B +15: Hy-Memory-30B-A3B 60 (File safety 25, 51 safetensors shards and no pickle or Python files.); Qwen3.8-Flash-Next 45 (File safety 25, safetensors only and nothing from the repository runs to load it or build prompts.)
- Agent interface, Qwen3.8-Flash-Next +5: Hy-Memory-30B-A3B 65 (Chat template 20, chat_template.jinja in the repository.); Qwen3.8-Flash-Next 70 (Chat template 20, a Jinja file in the repository.)