# DiffusionGemma 26B A4B IT vs Hy-Memory-30B-A3B (slim)

> Hy-Memory-30B-A3B scores 61 (C) to DiffusionGemma 26B A4B IT's 51.3 (D) 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 DiffusionGemma 26B A4B IT's 51.3 (D), and leads in 2 of 4 scored categories. DiffusionGemma 26B A4B IT leads on risk and agent interface. Both do open-weight models you run yourself.

- DiffusionGemma 26B A4B IT: D 51.3, rank #143 of 160 open-weight models · https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it.min.md
- Hy-Memory-30B-A3B: C 61, rank #68 of 160 open-weight models · https://www.anchorterminal.com/tools/hy-memory-30b-a3b.min.md
- DiffusionGemma 26B A4B IT, good for Agents with about 64 GB of GPU memory that want fast single-user generation from text and images and can accept lower quality than Gemma 4 Ahead on Risk, 70 against 60; Agent interface, 70 against 65.
- 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 55; Documentation and upkeep, 68 against 60.

| Category | DiffusionGemma 26B A4B IT | Hy-Memory-30B-A3B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 55 | 85 |
| Risk (20%) | 70 | 60 |
| Agent interface (10%) | 70 | 65 |
| Documentation and upkeep (10%) | 60 | 68 |

| Fact (where they differ) | DiffusionGemma 26B A4B IT | Hy-Memory-30B-A3B |
| --- | --- | --- |
| Vendor | Google DeepMind | Tencent (Hy team) |
| llms.txt | yes | no |
| Last release | 2026-07-15 | 2026-10-16 |
| Agent reviews | 2.5/5 (4) | none |

Why the edge:
- Reach, Hy-Memory-30B-A3B +30: DiffusionGemma 26B A4B IT 55 (Memory earns 15 because the only Google build is 51.7 GB of BF16, about 62 GB with the fifth added, which needs the 64 GB band.); 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.)
- Risk, DiffusionGemma 26B A4B IT +10: DiffusionGemma 26B A4B IT 70 (File safety gets the full 25, safetensors only and loaded by Transformers' own code and a Jinja template.); Hy-Memory-30B-A3B 60 (File safety 25, 51 safetensors shards and no pickle or Python files.)
- Documentation and upkeep, Hy-Memory-30B-A3B +8: DiffusionGemma 26B A4B IT 60 (The card covers all seven points (20), and the training data is a general description with a January 2025 cut-off (10).); 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)…)
- Agent interface, DiffusionGemma 26B A4B IT +5: DiffusionGemma 26B A4B IT 70 (The chat template is in the repository (20) and handles several tool calls in one turn, and the card names function calling as trained (20).); Hy-Memory-30B-A3B 65 (Chat template 20, chat_template.jinja in the repository.)