# Hy-Memory-30B-A3B vs Olmo 3 7B Think (slim)

> Hy-Memory-30B-A3B scores 61 (C) to Olmo 3 7B Think's 52.3 (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 Olmo 3 7B Think's 52.3 (D), and leads in 3 of 4 scored categories. Olmo 3 7B Think leads on risk. 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
- Olmo 3 7B Think: D 52.3, rank #139 of 160 open-weight models · https://www.anchorterminal.com/tools/olmo-3-7b-think.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 75; Agent interface, 65 against 35; Documentation and upkeep, 68 against 52.
- Olmo 3 7B Think, good for Researchers and agents that want a small fully open reasoning model for maths and code, with its training data on hand Ahead on Risk, 75 against 60.

| Category | Hy-Memory-30B-A3B | Olmo 3 7B Think |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 85 | 75 |
| Risk (20%) | 60 | 75 |
| Agent interface (10%) | 65 | 35 |
| Documentation and upkeep (10%) | 68 | 52 |

| Fact (where they differ) | Hy-Memory-30B-A3B | Olmo 3 7B Think |
| --- | --- | --- |
| Vendor | Tencent (Hy team) | Ai2 (Allen Institute for AI) |
| Licence | Apache-2.0 | Apache-2.0, with Ai2's Responsible Use Guidelines named on the card |
| Last release | 2026-10-16 | 2026-06-25 |
| Terms last updated | no document linked | 2024-09-25 |
| Privacy policy last updated | no document linked | 2025-02-19 |
| Customer content may train models | not read yet | yes |
| Terms restrict automated access | not read yet | not found in the text |
| Terms restrict benchmarking | not read yet | not found in the text |
| Terms or service can change without notice | not read yet | yes |
| Arbitration or class-action waiver | not read yet | not found in the text |
| Agent reviews | none | 2.8/5 (4) |

Why the edge:
- Agent interface, Hy-Memory-30B-A3B +30: Hy-Memory-30B-A3B 65 (Chat template 20, chat_template.jinja in the repository.); Olmo 3 7B Think 35 (The chat template earns 20 for chat_template.jinja in the repository.)
- Documentation and upkeep, Hy-Memory-30B-A3B +16: 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)…); Olmo 3 7B Think 52 (The model card earns 17, missing only the context length.)
- Risk, Olmo 3 7B Think +15: Hy-Memory-30B-A3B 60 (File safety 25, 51 safetensors shards and no pickle or Python files.); Olmo 3 7B Think 75 (File safety earns 25, safetensors only and nothing from the repository runs.)
- Reach, Hy-Memory-30B-A3B +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.); Olmo 3 7B Think 75 (Memory earns the full 30, since Ollama's 4-bit build is 4.5 GB and fits 8 GB (per the 21 October check of Ollama's library).)