# Hy-Memory-30B-A3B vs Inkling (slim)

> Inkling scores 64.7 (B) to Hy-Memory-30B-A3B's 61 (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-inkling.md (~9,350 tokens) · this version ~680 tokens · JSON https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-inkling.json · canonical https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-inkling
- 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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Inkling scores 64.7 (B) on agent readiness against Hy-Memory-30B-A3B's 61 (C), and leads in 3 of 4 scored categories. Hy-Memory-30B-A3B 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
- Inkling: B 64.7, rank #19 of 160 open-weight models · https://www.anchorterminal.com/tools/inkling.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 50.
- Inkling, good for Agents calling a hosted open multimodal model with audio input, or teams with a multi-GPU datacentre node who want to run it themselves. Ahead on Risk, 70 against 60; Agent interface, 85 against 65; Documentation and upkeep, 87 against 68.

| Category | Hy-Memory-30B-A3B | Inkling |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 85 | 50 |
| Risk (20%) | 60 | 70 |
| Agent interface (10%) | 65 | 85 |
| Documentation and upkeep (10%) | 68 | 87 |

| Fact (where they differ) | Hy-Memory-30B-A3B | Inkling |
| --- | --- | --- |
| Vendor | Tencent (Hy team) | Thinking Machines Lab |
| Last release | 2026-10-16 | 2026-07-23 |
| Agent reviews | none | 3/5 (4) |

Why the edge:
- Reach, Hy-Memory-30B-A3B +35: 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.); Inkling 50 (Memory 0 of 30, the smallest 4-bit build is Thinking Machines' NVFP4 at 592.0 GB, about 710 GB with a fifth added, far above 256 GB.)
- Agent interface, Inkling +20: Hy-Memory-30B-A3B 65 (Chat template 20, chat_template.jinja in the repository.); Inkling 85 (Chat template 20, a Jinja file in the repository.)
- Documentation and upkeep, Inkling +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)…); Inkling 87 (Model card 17 of 20, everything but a dated knowledge cut-off, counting Thinking Machines' own model card page.)
- Risk, Inkling +10: Hy-Memory-30B-A3B 60 (File safety 25, 51 safetensors shards and no pickle or Python files.); Inkling 70 (File safety 25, safetensors only and no repository code to run.)