# Hy-Memory-30B-A3B vs Qwen3.5-397B-A17B > Qwen3.5-397B-A17B scores 64.1 (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. - Canonical: https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-397b-a17b - Markdown: https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-397b-a17b.md (~10,000 tokens) - Site index for agents: https://www.anchorterminal.com/llms.txt (the key docs and every listing in one file, at most 16 MB, about 4 million tokens: https://www.anchorterminal.com/llms-full.txt) - API: https://www.anchorterminal.com/api/v1/index.json - MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key) - DNS: dig +short TXT .grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.. and .vs.. under the zone, and the same under grade.letme.dev) - Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data) - Updated: 2026-11-10 Qwen3.5-397B-A17B scores 64.1 (B) on agent readiness against Hy-Memory-30B-A3B's 61 (C), and leads in 1 of 4 scored categories. Hy-Memory-30B-A3B leads on reach, risk and documentation and upkeep. Both do open-weight models you run yourself. - Hy-Memory-30B-A3B: grade C, 61/100, rank #68 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/hy-memory-30b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/hy-memory-30b-a3b.json - Qwen3.5-397B-A17B: grade B, 64.1/100, rank #23 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-5-397b-a17b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-5-397b-a17b.json - Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md - All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md ## Which one, for what ### Hy-Memory-30B-A3B (C) 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 70 - Risk, 60 against 55 - Documentation and upkeep, 68 against 34 Watch for: Not a general assistant or a tool caller ### Qwen3.5-397B-A17B (B) Good for: Agents with access to an 8-GPU server, or calling a hosted endpoint, that want the strongest open Qwen3.5 with vision and a plain licence. Ahead on: - Agent interface, 70 against 65 Watch for: No build fits a 256 GB machine, and Ollama has no local build ## Why the edge ### Documentation and upkeep: Hy-Memory-30B-A3B +34 - 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) are there. Training data 5, A general description of the two training stages inside the harnesses, with no datasets, sizes or cut-off. Last change 20, the last commit, on 16 October 2026, added the licence and the card, 25 days before this check. Responsiveness 20, Of the 4 discussions that aren't pull requests (16 to 30 October 2026), 3 have a reply from a tencent organisation member. Version policy 5, A first release, with nothing replaced. File health 10, Config, tokenizer and template agree on the architecture, the context and the thinking levels. 8 + 5 + 20 + 20 + 5 + 10 = 68. - Qwen3.5-397B-A17B (34): The card covers five of the seven items and misses limitations and a cut-off (14). Training data is a general description without a cut-off (5). The files last changed on 24 April 2026, 192 days before this check, past the 180-day band and within a year (5), and Qwen's GitHub repository holds only a README and licence, so no reference code of Alibaba's changed later. Alibaba answered none of the 40 most recent questions and reports, recounted on 2 November (0), and GitHub issues couldn't be counted. It's a first release at this size (5), and two template reports and a template fix still wait unanswered (5). 14 + 5 + 5 + 0 + 5 + 5 = 34. ### Reach: Hy-Memory-30B-A3B +15 - 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. Its Q3_K_M file and the two files with experts at 1 or 2 bits are below 4-bit precision and don't count. Runtimes 20, hy_v3 model code in released vLLM (HYV3ForCausalLM in the v0.33.0 registry), SGLang v0.5.23 (hunyuan_v3.py), Transformers v5.22.2 and llama.cpp b12242, 5 each. Builds 15, Tencent publishes an FP8 repository and a GGUF repository with Q4_K_M, Q6_K and Q8_0 files in its own organisation, so quantised builds at two precisions or more. Terms 15, Apache 2.0, with only a preamble naming Tencent as copyright holder above the licence text. Download 10, gated is false for the main, FP8 and GGUF repositories on 10 November 2026, so the files download with no account or terms to accept. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026), and the card names no API. 25 + 20 + 15 + 15 + 10 + 0 = 85. - Qwen3.5-397B-A17B (70): Memory scores 0, since even the smallest 4-bit build, LM Studio's MLX 4-bit at 223.9 GB, needs about 269 GB with a fifth added, and Alibaba's own GPTQ-Int4 about 283 GB. Runtimes reach the 25 cap on released llama.cpp, vLLM, SGLang and Transformers and LM Studio's catalogue, though Ollama has no local build. Alibaba's FP8 and GPTQ-Int4 builds give builds 15. Apache 2.0 with nothing added gives terms 15, the ungated repositories give download 10, and Alibaba's Model Studio plus five live providers on Hugging Face give hosted too its 5. ### Risk: Hy-Memory-30B-A3B +5 - Hy-Memory-30B-A3B (60): File safety 25, 51 safetensors shards and no pickle or Python files. Provenance 15, files in the tencent organisation that the card's own resource list links to (10), with a SHA-256 listed for all 53 large files (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, no prompt-injection guidance, instruction hierarchy, account of trained refusals or general cautions on the card. Licence risk 15, Apache 2.0 with no use policy, no field-of-use limit, no threshold and no clause letting Tencent change the terms. Security programme 5, Tencent's Security Response Center takes vulnerability reports company-wide with published reward rules, as the Hy3 listing records (5). 25 + 15 + 0 + 0 + 15 + 5 = 60. - Qwen3.5-397B-A17B (55): Safetensors only with no repository code run on load or to build prompts gives file safety 25, and files in the Qwen organisation the publisher's GitHub links to, each hashed on the Hub, give provenance 15. Plain Apache 2.0 keeps licence risk at 15. Safety evaluation, agent guidance and a security programme score 0. The card has no safety results (its SecCodeBench row is a coding capability), no cautions, and no reporting route was found. The Qwen3.5 blog and Alibaba's security response centre couldn't be read, so both are scored as absent and left open. ### Agent interface: Qwen3.5-397B-A17B +5 - Hy-Memory-30B-A3B (65): Chat template 20, chat_template.jinja in the repository. Tool-calling format 10, the card doesn't mention tools. Trained tool calling 10, the template can render tool calls, but the card presents the model as a memory writer that returns memories as text or JSON and says nothing of calling tools. Structured output 5, the card's quickstart asks for a JSON object with a memories list in the system prompt and shows nothing else, no response_format and no schema, so JSON output is shown by example without a documented way to constrain it. Reasoning control 10, the card passes reasoning_effort as no_think per request through chat_template_kwargs, and the template takes high, low or no_think. Context 10, 262,144 tokens native (max_position_embeddings in config.json). 20 + 10 + 10 + 5 + 10 + 10 = 65. - Qwen3.5-397B-A17B (70): The template ships in the repository (20) and renders several tool calls per turn for a model trained for tool use (20). Tool-calling format gets 10, because the card names the qwen3_coder parser but leaves the declaration and call format to the template. Thinking turns off per request and comes back in think tags split by the qwen3 parser (10), and 262,144 tokens is native (10). Structured output scores 0, as nothing documents JSON output for the model. ## Score by category | Category | Weight | Hy-Memory-30B-A3B | Qwen3.5-397B-A17B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 85 | 70 | Hy-Memory-30B-A3B +15 | | Risk | 20% (20 this run) | 60 | 55 | Hy-Memory-30B-A3B +5 | | Agent interface | 10% (10 this run) | 65 | 70 | Qwen3.5-397B-A17B +5 | | Documentation and upkeep | 10% (10 this run) | 68 | 34 | Hy-Memory-30B-A3B +34 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **64.1 · B** | | ## Facts side by side | Fact | Hy-Memory-30B-A3B | Qwen3.5-397B-A17B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Tencent (Hy team) | Alibaba (Qwen team) | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | Apache-2.0 | Apache-2.0 | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-10-16 | 2026-02-16 | | Terms last updated | no document linked | no document linked | | Privacy policy last updated | no document linked | no document linked | | Agent reviews | none | 2.8/5 (4) | ## How the scores have moved ### Hy-Memory-30B-A3B No score change since it was listed. ### Qwen3.5-397B-A17B - -0.5 on 2 November 2026: time ## Verdicts **Hy-Memory-30B-A3B.** A specialist that writes an agent's long-term memory, built to slot into Mem0, MemOS or Tencent's own framework in place of a general model. It fits a 24 GB card from Tencent's own GGUF, under plain Apache 2.0, and Tencent answers on the repository. Its results are Tencent's own and nobody has rerun them. **Qwen3.5-397B-A17B.** Alibaba's biggest open Qwen3.5, a cousin for hard coding, agent and vision work. Even at 4 bits it needs about 270 GB, so most agents will call it through Alibaba's Model Studio or one of the many providers that host it. The main risk is silence, with no safety results, no agent guidance and template reports without a reply. ## Before you call either ### Hy-Memory-30B-A3B 1. Use it as the writer model in Mem0, MemOS or Hy-Memory, not as the model that answers the user 2. Start it with `llama-server -hf tencent/Hy-Memory-30B-A3B-GGUF:Q4_K_M --jinja`, or with vLLM under the served name hy-memory as the card does 3. Pass `reasoning_effort` as no_think in `chat_template_kwargs` and temperature 0, as the card's example does 4. Ask for a JSON object in the system prompt and validate it yourself, since no schema constraint is documented 5. Treat what it stores as unverified. It keeps what a conversation says, true or not ### Qwen3.5-397B-A17B 1. The card's server command is `vllm serve Qwen/Qwen3.5-397B-A17B --port 8000 --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, and the FP8 build halves the memory 2. It doesn't fit our 128 GB reference machine at any precision, so call a hosted endpoint instead (Model Studio's Qwen3.5-Plus, or a provider serving Qwen/Qwen3.5-397B-A17B) 3. On Model Studio pass `"enable_thinking": false` directly, while vLLM and SGLang take `"chat_template_kwargs": {"enable_thinking": false}` 4. Sampling per the card is temperature 0.6, top_p 0.95 and top_k 20 when thinking, and temperature 0.7, top_p 0.8, top_k 20 and presence_penalty 1.5 without 5. For multi-token prediction in vLLM add `--speculative-config '{"method":"qwen3_next_mtp","num_speculative_tokens":2}'` 6. Keep the context at 128K or more if you rely on thinking, and turn on YaRN only past 262,144 tokens 7. Validate any JSON it returns, since structured output isn't documented ## Questions ### Which is better for AI agents, Hy-Memory-30B-A3B or Qwen3.5-397B-A17B? Qwen3.5-397B-A17B scores 64.1 (B) on agent readiness against Hy-Memory-30B-A3B's 61 (C), and leads in 1 of 4 scored categories. Hy-Memory-30B-A3B leads on reach, risk and documentation and upkeep. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-397b-a17b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-397b-a17b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "hy-memory-30b-a3b", "b": "qwen3-5-397b-a17b"}`. 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How: https://www.anchorterminal.com/agents/#mcp