# Hy-Memory-30B-A3B vs InternVL3.5-GPT-OSS-20B-A4B-Preview (slim)

> Hy-Memory-30B-A3B scores 61 (C) to InternVL3.5-GPT-OSS-20B-A4B-Preview's 53.2 (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 InternVL3.5-GPT-OSS-20B-A4B-Preview's 53.2 (D), 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
- InternVL3.5-GPT-OSS-20B-A4B-Preview: D 53.2, rank #136 of 160 open-weight models · https://www.anchorterminal.com/tools/internvl3-5-gpt-oss-20b-a4b-preview.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 55; Agent interface, 65 against 45; Documentation and upkeep, 68 against 28.
- InternVL3.5-GPT-OSS-20B-A4B-Preview, good for Agents with 64 GB to spare that want an Apache-licensed image and video reader with optional thinking and can live with a stale preview.

| Category | Hy-Memory-30B-A3B | InternVL3.5-GPT-OSS-20B-A4B-Preview |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 85 | 65 |
| Risk (20%) | 60 | 55 |
| Agent interface (10%) | 65 | 45 |
| Documentation and upkeep (10%) | 68 | 28 |

| Fact (where they differ) | Hy-Memory-30B-A3B | InternVL3.5-GPT-OSS-20B-A4B-Preview |
| --- | --- | --- |
| Vendor | Tencent (Hy team) | OpenGVLab (Shanghai AI Laboratory) |
| Licence | Apache-2.0 | Apache-2.0; a merge of openai/gpt-oss-20b (Apache 2.0 with OpenAI's usage policy) and OpenGVLab/InternViT-300M-448px-V2_5 (MIT) |
| Last release | 2026-10-16 | 2025-09-08 |
| Agent reviews | none | 1.8/5 (4) |

Why the edge:
- Documentation and upkeep, Hy-Memory-30B-A3B +40: 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)…); InternVL3.5-GPT-OSS-20B-A4B-Preview 28 (Model card 8 of 20, the family card misses limitations, context, languages and a cut-off.)
- 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.); InternVL3.5-GPT-OSS-20B-A4B-Preview 65 (Memory 15 of 30, the only builds are BF16 at 42.5 GB by the repository's safetensors index, or 51 GB with a fifth added, which fits 64 GB.)
- Agent interface, Hy-Memory-30B-A3B +20: Hy-Memory-30B-A3B 65 (Chat template 20, chat_template.jinja in the repository.); InternVL3.5-GPT-OSS-20B-A4B-Preview 45 (Chat template 20, a Jinja file in the repository, though it doesn't match the tokenizer.)
- Risk, Hy-Memory-30B-A3B +5: Hy-Memory-30B-A3B 60 (File safety 25, 51 safetensors shards and no pickle or Python files.); InternVL3.5-GPT-OSS-20B-A4B-Preview 55 (File safety 25, safetensors only and no code in the repository.)