Head to head · Open-weight models you run yourself · October 2026 research run
Hy-Memory-30B-A3B vs Qwopus3.8-27B-Flash-V2
Hy-Memory-30B-A3B and Qwopus3.8-27B-Flash-V2 score within a point of each other on agent readiness, 61 (C) and 61 (C). 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 50
- Agent interface, 65 against 60
- Documentation and upkeep, 68 against 51
Watch for
Not a general assistant or a tool caller
Best for Hobbyist and research agents that want a Qwen3.8-27B tuned for shorter reasoning, for local coding loops on a 24 GB GPU.
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
Published from a personal account, with no organisation, security contact or safety evaluation
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 +17
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.
Qwopus3.8-27B-Flash-V2 51
Model card 11 of 20, missing architecture, context and a cut-off. Training data 5, a description with no cut-off. Last change 15, the files went up on 21 September 2026, 43 days ago, with no commit since. Responsiveness 10, Jackrong answered 2 of 8 discussions across the model's two repositories. Version policy 5, the first Flash release stays up without a dated note. File health 5, no LICENSE file, an experiment note in the template and open reports of looping and a grammar failure.
Risk Hy-Memory-30B-A3B +10
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.
Qwopus3.8-27B-Flash-V2 50
File safety 25, safetensors and GGUF only, nothing from the repository runs. Provenance 5 of 15, the files are on a personal account with no site or organisation behind it, though every file has a checksum. Safety evaluation 0, the card says none was done. Agent guidance 5, general cautions about loops and tool reliability. Licence risk 15, plain Apache 2.0. Security programme 0, no contact or policy.
Agent interface Hy-Memory-30B-A3B +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.
Qwopus3.8-27B-Flash-V2 60
Chat template 20, a Jinja file in the repository. Tool-calling format 10 of 25, only the template shows the Hermes-style JSON calls, which differ from the base's format. Trained tool calling 20, several calls a turn. Structured output 0, not documented. Reasoning control 0, the switches are documented but not where the reasoning comes back. Context 10, 262,144 tokens from the base.
Score by category
| Category | Weight this run | Hy-Memory-30B-A3B | Qwopus3.8-27B-Flash-V2 | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 85 | 85 | even |
| Risk | 20%20 | 60 | 50 | Hy-Memory-30B-A3B +10 |
| Agent interface | 10%10 | 65 | 60 | Hy-Memory-30B-A3B +5 |
| Documentation and upkeep | 10%10 | 68 | 51 | Hy-Memory-30B-A3B +17 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 61/100 |
Facts side by side
| Fact | Hy-Memory-30B-A3B | Qwopus3.8-27B-Flash-V2 |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Tencent (Hy team) | Jackrong |
| 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 (metadata only, no LICENSE file); built on Qwen3.8-27B under Apache 2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-16 | 2026-09-21 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 2.5/5 (4) |
How the scores have moved
Hy-Memory-30B-A3B
No score change since it was listed.
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.
Qwopus3.8-27B-Flash-V2
A Qwen3.8-27B cousin tuned to think less per turn in long agent loops, with an MTP head for speculative decoding in llama.cpp. It fits 24 GB with Jackrong's 4-bit GGUF. The risks are one person's experimental release with no safety work, training traces taken from Claude and GPT, and a tool format that differs from Qwen's.
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
Qwopus3.8-27B-Flash-V2
- Quickest start is
llama-server -hf Jackrong/Qwopus3.8-27B-Flash-V2-GGUF:Q4_K_M --jinja, about 17 GB, and add mmproj-F32.gguf for images - The card's tests used Q5_K_M with the bundled MTP head and
--spec-type draft-mtpin llama.cpp - The template asks for tool calls as JSON inside tool_call tags, so use a Hermes-style parser (vLLM's hermes), not Qwen3.8's qwen3_coder
- For coding the card recommends enable_thinking true, reasoning_effort xhigh and temperature 0.3, which you must pass yourself, since generation_config.json says 1.0
- reasoning_effort takes xhigh (default), medium or low, and any other value makes the template raise an error
- Earlier reasoning stays in the context unless preserve_thinking is false
- Cap max_tokens, since a report says it loops more than V1
Questions
Which is better for AI agents, Hy-Memory-30B-A3B or Qwopus3.8-27B-Flash-V2?
Hy-Memory-30B-A3B and Qwopus3.8-27B-Flash-V2 score within a point of each other on agent readiness, 61 (C) and 61 (C).
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- Qwen3-Coder-Next vs Qwopus3.8-27B-Flash-V2
- Qwen3-Omni-30B-A3B-Instruct vs Qwopus3.8-27B-Flash-V2
- Qwen3-VL-2B-Instruct vs Qwopus3.8-27B-Flash-V2
- Qwen3-VL-30B-A3B-Instruct vs Qwopus3.8-27B-Flash-V2
- Qwen3-VL-32B-Instruct vs Qwopus3.8-27B-Flash-V2
- Qwen3-VL-4B-Instruct vs Qwopus3.8-27B-Flash-V2
- Qwen3-VL-8B-Instruct vs Qwopus3.8-27B-Flash-V2
- Qwopus3.8-27B-Flash-V2 vs Recursive Flint
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- Qwopus3.8-27B-Flash-V2 vs SmolLM3-3B
- Qwopus3.8-27B-Flash-V2 vs Spark-X2.6-4B
- Qwopus3.8-27B-Flash-V2 vs Swift 1.5 Qwen3.8-27B
- Qwopus3.8-27B-Flash-V2 vs Swift 1.5 Qwen3.8-Flash-Next
- Qwopus3.8-27B-Flash-V2 vs ThinkingCap (Qwen3.8 27B)
- Qwopus3.8-27B-Flash-V2 vs Baidu Unlimited-OCR-2
Machine-readable
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/compare/hy-memory-30b-a3b-vs-qwopus3-8-27b-flash-v2.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/qwopus3-8-27b-flash-v2.json - From a terminal
anchor compare hy-memory-30b-a3b qwopus3-8-27b-flash-v2(the CLI) - Over MCP
compare_tools {"a": "hy-memory-30b-a3b", "b": "qwopus3-8-27b-flash-v2"}at/mcp, no key