# Hy-Memory-30B-A3B vs Ling-3.1-flash-1019 (slim)

> Hy-Memory-30B-A3B scores 61 (C) to Ling-3.1-flash-1019's 54.8 (C) 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-ling-3-1-flash-1019.md (~10,400 tokens) · this version ~780 tokens · JSON https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ling-3-1-flash-1019.json · canonical https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ling-3-1-flash-1019
- 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
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)

Hy-Memory-30B-A3B scores 61 (C) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in every scored category. 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
- Ling-3.1-flash-1019: C 54.8, rank #126 of 160 open-weight models · https://www.anchorterminal.com/tools/ling-3-1-flash-1019.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 45; Agent interface, 65 against 60; Documentation and upkeep, 68 against 56.
- Ling-3.1-flash-1019, good for Teams with a multi-GPU server that want an MIT-licensed flagship for long agent runs and coding

| Category | Hy-Memory-30B-A3B | Ling-3.1-flash-1019 |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 85 | 60 |
| Risk (20%) | 60 | 45 |
| Agent interface (10%) | 65 | 60 |
| Documentation and upkeep (10%) | 68 | 56 |

| Fact (where they differ) | Hy-Memory-30B-A3B | Ling-3.1-flash-1019 |
| --- | --- | --- |
| Vendor | Tencent (Hy team) | inclusionAI (Ant Group) |
| Licence | Apache-2.0 | MIT |
| Last release | 2026-10-16 | 2026-11-04 |

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.); Ling-3.1-flash-1019 60 (Memory 0, inclusionAI's only build is the FP8 release at 573.78 GB, × 1.2 = 688.5 GB, above 256 GB, and no runtime's library has a build.)
- Risk, Hy-Memory-30B-A3B +15: Hy-Memory-30B-A3B 60 (File safety 25, 51 safetensors shards and no pickle or Python files.); Ling-3.1-flash-1019 45 (File safety 15, safetensors only, no .bin, .pt or .pth files (25), less 10 because the card's documented vLLM command passes --trust-remote-code, which runs the…)
- Documentation and upkeep, Hy-Memory-30B-A3B +12: 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)…); Ling-3.1-flash-1019 56 (Model card 11, Intended use (complex reasoning, multi-step tasks and agentic workflows), architecture, parameters (560B total, 25B active) and context (1M claim…)
- Agent interface, Hy-Memory-30B-A3B +5: Hy-Memory-30B-A3B 65 (Chat template 20, chat_template.jinja in the repository.); Ling-3.1-flash-1019 60 (Chat template 20, chat_template.jinja in the repository (6,042 characters), which handles tools, the thinking switch and preserved reasoning.)