Head to head · Open-weight models you run yourself · October 2026 research run
Apodex-1.1-mini vs Hy-Memory-30B-A3B
Apodex-1.1-mini and Hy-Memory-30B-A3B score within a point of each other on agent readiness, 61 (C) and 61 (C). Hy-Memory-30B-A3B leads on 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 Long-running agent tasks with tools on a 32 GB GPU or Mac, or through Apodex's API, under a plain licence
Ahead on
- Risk, 65 against 60
- Agent interface, 85 against 65
Watch for
No safety results, and agent guidance only from the harness
Best for Agents with an external memory framework that want a local model for the write path on a 24 GB card
Ahead on
- Documentation and upkeep, 68 against 38
Watch for
Not a general assistant or a tool caller
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 +30
Apodex-1.1-mini 38
Model card 8, it covers intended use, context and languages but not the architecture, parameters, limitations or a knowledge cut-off. Training data 5, a general description with no cut-off. Last change 15, the GGUF build went up on 10 September 2026, 54 days before 3 November. Responsiveness 0, Apodex answered none of the four discussions, unchanged on 3 November 2026. Version policy 5, 1.0-mini stays in its own repository without a note. File health 5, the files agree, but the card's agent example is broken and reports wait without a reply.
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.
Agent interface Apodex-1.1-mini +20
Apodex-1.1-mini 85
Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card says how tools go in and come back and names the parser for SGLang and vLLM, with a full example. Trained tool calling 20, trained for native function calling with parallel calls. Structured output 0. Reasoning control 10, the template turns thinking off per request and the card says the reasoning comes back in reasoning_content. Context 10, 262,144 tokens.
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.
Risk Apodex-1.1-mini +5
Apodex-1.1-mini 65
File safety 25, safetensors and GGUF only and nothing from the repositories runs. Provenance 15, the files are in the apodex organisation its site links to, with a SHA-256 for every weight file and a SHA256SUMS file for the GGUF. Safety evaluation 0, none published. Agent guidance 5, the recommended harness documents approval gates and a sandbox boundary, but nothing on prompt injection or refusals for the model. Licence risk 15, Apache 2.0 from Qwen3.5-35B-A3B through to this model with no conditions added. Security programme 5, the harness has a disclosure policy with a private address, with no route shown for the weights.
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.
Score by category
| Category | Weight this run | Apodex-1.1-mini | Hy-Memory-30B-A3B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 85 | 85 | even |
| Risk | 20%20 | 65 | 60 | Apodex-1.1-mini +5 |
| Agent interface | 10%10 | 85 | 65 | Apodex-1.1-mini +20 |
| Documentation and upkeep | 10%10 | 38 | 68 | Hy-Memory-30B-A3B +30 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 61/100 |
Facts side by side
| Fact | Apodex-1.1-mini | Hy-Memory-30B-A3B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Apodex AI | Tencent (Hy 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 | 2026-08-25 | 2026-10-16 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 3.3/5 (4) | none |
Verdicts
Apodex-1.1-mini
An agent cousin fine-tuned from Qwen3.5-35B-A3B for long tasks with files, code and tools, with parallel tool calling and about 3B active parameters. It needs 32 GB for Apodex's GGUF, NVFP4 or GPTQ-Int4 builds or about 86 GB in BF16, and Apodex's API serves it too. The main risks are no safety results and no replies from the publisher.
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.
Before you call either
Apodex-1.1-mini
- Serve it with
vllm serve apodex/Apodex-1.1-mini --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser qwen3, or the same parsers in SGLang, so tool calls and reasoning come back parsed - For llama.cpp,
llama-server -hf apodex/Apodex-1.1-mini-GGUF:Q4_K_M --jinja --reasoning-format deepseek, and the card passes the repository's chat_template.jinja with --chat-template-file - Pass tools through the API's tools parameter rather than in the system prompt, as the card asks
- The card recommends temperature 1.0, top_p 0.95, repetition_penalty 1.05 and max_tokens up to 32,768
- Send your own system prompt, or set identity_mode, since the template otherwise adds an Apodex identity block
- The template keeps all earlier reasoning in the context unless
preserve_thinkingis false, which grows long agent sessions - Thinking is on by default, send
chat_template_kwargswithenable_thinkingfalse for short tasks, a switch the card doesn't mention - The card's Python example has a broken line where it runs the tool (available_toolsname), so fix it to
available_tools[name](**args)before running - Validate JSON output yourself, since structured output isn't documented
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
Questions
Which is better for AI agents, Apodex-1.1-mini or Hy-Memory-30B-A3B?
Apodex-1.1-mini and Hy-Memory-30B-A3B score within a point of each other on agent readiness, 61 (C) and 61 (C). Hy-Memory-30B-A3B leads on documentation and upkeep.
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Machine-readable
- This page as Markdown
/compare/apodex-1-1-mini-vs-hy-memory-30b-a3b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/apodex-1-1-mini.json·/api/v1/tools/hy-memory-30b-a3b.json - From a terminal
anchor compare apodex-1-1-mini hy-memory-30b-a3b(the CLI) - Over MCP
compare_tools {"a": "apodex-1-1-mini", "b": "hy-memory-30b-a3b"}at/mcp, no key