Head to head · Open-weight models you run yourself · October 2026 research run
Hy-Memory-30B-A3B vs Qwen3.5-4B
Qwen3.5-4B and Hy-Memory-30B-A3B score within a point of each other on agent readiness, 61.2 (C) and 61 (C). Hy-Memory-30B-A3B leads on risk and documentation and upkeep. Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 4969 open weights comparisons
Which one, for what
Best for Agents with an external memory framework that want a local model for the write path on a 24 GB card
Ahead on
- Risk, 60 against 55
- Documentation and upkeep, 68 against 34
Watch for
Not a general assistant or a tool caller
Best for Agents that want a cheap local helper for tool calls, short reasoning and reading images on a laptop or small GPU
Ahead on
- Agent interface, 70 against 65
Watch for
No quantised build from Alibaba for this size
Why the edge
Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.
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.
Qwen3.5-4B 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 2 March 2026, 245 days before 2 November, which is within a year (5). No Qwen member has answered any of the 21 discussions that aren't pull requests, four of them opened since 21 October (0), and the GitHub issues couldn't be counted. Earlier 4B models stay in their own repositories without dated notes (5), and two pull requests fixing the template and adding generation_config.json wait unanswered (5). 14 + 5 + 5 + 0 + 5 + 5 = 34.
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.
Qwen3.5-4B 55
Safetensors only with no remote code gives file safety its 25, and files in the Qwen organisation the publisher's GitHub links to, each with a hash on the Hub, give provenance 15. The Apache 2.0 licence carries none of the deductions, so licence risk keeps 15. Safety evaluation, agent guidance and a security programme score 0, since the card has no safety results or cautions and no reporting route was found. The Qwen3.5 blog and Alibaba's security response centre couldn't be read, so those two are scored as absent and left open.
Agent interface Qwen3.5-4B +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.
Qwen3.5-4B 70
The template ships in the repository (20) and the model was trained for tool use with several calls per turn in the template (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 this run | Hy-Memory-30B-A3B | Qwen3.5-4B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 85 | 85 | even |
| Risk | 20%20 | 60 | 55 | Hy-Memory-30B-A3B +5 |
| Agent interface | 10%10 | 65 | 70 | Qwen3.5-4B +5 |
| Documentation and upkeep | 10%10 | 68 | 34 | Hy-Memory-30B-A3B +34 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 61.2/100 |
Facts side by side
| Fact | Hy-Memory-30B-A3B | Qwen3.5-4B |
|---|---|---|
| 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 | 2026-03-02 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 3/5 (4) |
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.
Qwen3.5-4B
A small vision-language cousin for tool calls, light agent steps and image or video reading on almost any machine, with switchable thinking. Ollama's 3.3 GB build or the 9.3 GB BF16 files run in every major runtime under plain Apache 2.0. The main risks are no safety results, no structured-output guarantee and no answers from Alibaba since March.
Before you call either
Hy-Memory-30B-A3B
- Use it as the writer model in Mem0, MemOS or Hy-Memory, not as the model that answers the user
- 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 - Pass
reasoning_effortas no_think inchat_template_kwargsand temperature 0, as the card's example does - Ask for a JSON object in the system prompt and validate it yourself, since no schema constraint is documented
- Treat what it stores as unverified. It keeps what a conversation says, true or not
Qwen3.5-4B
- Smallest start is
ollama run qwen3.5:4b(Q4_K_M, about 4 GB with context) - Full precision
vllm serve Qwen/Qwen3.5-4B --port 8000 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder - Turn thinking off with
"chat_template_kwargs": {"enable_thinking": false}in the request body, since it's on by default and/thinkand/nothinkaren't supported - There's no generation_config.json, so pass the card's sampling yourself, for example
temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5without thinking - Keep the context at 128K or more if you rely on thinking, and turn on YaRN only past 262,144 tokens
- Validate any JSON it returns, since structured output isn't documented
Questions
Which is better for AI agents, Hy-Memory-30B-A3B or Qwen3.5-4B?
Qwen3.5-4B and Hy-Memory-30B-A3B score within a point of each other on agent readiness, 61.2 (C) and 61 (C). Hy-Memory-30B-A3B leads on risk and documentation and upkeep.
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Machine-readable
- This page as Markdown
/compare/hy-memory-30b-a3b-vs-qwen3-5-4b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/hy-memory-30b-a3b.json·/api/v1/tools/qwen3-5-4b.json - From a terminal
anchor compare hy-memory-30b-a3b qwen3-5-4b(the CLI) - Over MCP
compare_tools {"a": "hy-memory-30b-a3b", "b": "qwen3-5-4b"}at/mcp, no key