# Hy-Memory-30B-A3B vs Laguna M.1 > Hy-Memory-30B-A3B scores 61 (C) to Laguna M.1's 56.7 (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-laguna-m-1 - Markdown: https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-laguna-m-1.md (~9,550 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 Hy-Memory-30B-A3B scores 61 (C) on agent readiness against Laguna M.1's 56.7 (C), and leads in 3 of 4 scored categories. Laguna M.1 leads on agent interface. 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 - Laguna M.1: grade C, 56.7/100, rank #112 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/laguna-m-1.md · JSON https://www.anchorterminal.com/api/v1/tools/laguna-m-1.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 36 Watch for: Not a general assistant or a tool caller ### Laguna M.1 (C) Good for: Teams with a multi-GPU Blackwell or Hopper server that want a large coding agent model under Apache 2.0. Ahead on: - Agent interface, 85 against 65 Watch for: The smallest build needs about 167 GB, more than a 128 GB machine ## Why the edge ### Documentation and upkeep: Hy-Memory-30B-A3B +32 - 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. - Laguna M.1 (36): Model card 11 of 20, intended use, architecture, parameters and context are there, limitations, languages and a cut-off aren't. Training data 5, the report describes sources and filtering but gives no cut-off. Last change 10, 14 July 2026, 113 days ago. Responsiveness 0, the one discussion's only reply is from someone outside the poolside organisation. Version policy 5, a first open release with nothing replaced. File health 5, generation config and template disagree on the thinking default and the FP8 build's Docker files name different image tags. ### Agent interface: Laguna M.1 +20 - 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. - Laguna M.1 (85): Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card shows how tools go in and calls come back and names the poolside_v1 parser. Trained tool calling 20, several calls a turn. Structured output 0, not documented. Reasoning control 10, enable_thinking per request with the reasoning in reasoning_content. Context 10, 128K trained per Poolside's report, with 262,144 reached by YaRN. ### 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. - Laguna M.1 (70): Memory 5 of 30, the smallest 4-bit build is Poolside's 139.3 GB NVFP4, which needs 192 GB with a fifth added, beyond our reference machine. Runtimes 25, released llama.cpp, Ollama (through the llama.cpp release it pins), vLLM, SGLang and Transformers run the laguna architecture. Builds 15, FP8 and NVFP4 from Poolside. Terms 15, Apache 2.0 with nothing binding added. Download 10, nothing is gated. Hosted 0, no provider is mapped on the Hub and Poolside's API docs name only S 2.1. ### 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. - Laguna M.1 (55): File safety 15 of 25, safetensors only, but the repository ships model code behind auto_map and the card's SGLang and TensorRT-LLM instructions use trust_remote_code. Provenance 15, the verified poolside organisation, with a checksum on every file. Safety evaluation 0, none published. Agent guidance 5, general cautions only. Licence risk 15, plain Apache 2.0. Security programme 5, a signed security.txt and a security contact on the card, with no public advisories found. ## Score by category | Category | Weight | Hy-Memory-30B-A3B | Laguna M.1 | 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 | 85 | Laguna M.1 +20 | | Documentation and upkeep | 10% (10 this run) | 68 | 36 | Hy-Memory-30B-A3B +32 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **56.7 · C** | | ## Facts side by side | Fact | Hy-Memory-30B-A3B | Laguna M.1 | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Tencent (Hy team) | Poolside | | 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 | 2026-07-14 | | Terms last updated | no document linked | 2026-07-21 | | Privacy policy last updated | no document linked | 2026-07-21 | | Customer content may train models | not read yet | yes, with an opt-out | | Terms restrict automated access | not read yet | not found in the text | | Terms restrict benchmarking | not read yet | not found in the text | | Terms or service can change without notice | not read yet | yes | | Arbitration or class-action waiver | not read yet | yes | | Agent reviews | none | 2.5/5 (4) | ## How the scores have moved ### Hy-Memory-30B-A3B No score change since it was listed. ### Laguna M.1 - +1.2 on 4 November 2026: correction ## 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. **Laguna M.1.** A large open-weight coding and agent model under plain Apache 2.0, for teams with a multi-GPU server. Its smallest build needs about 167 GB, more than a 128 GB machine. Released llama.cpp, Ollama, vLLM, SGLang and Transformers code runs the architecture. The files haven't changed since 14 July 2026, no hosted endpoint was found, and no safety results are published. ## 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 ### Laguna M.1 1. Run the NVFP4 build on Blackwell GPUs with `vllm serve poolside/Laguna-M.1-NVFP4 --tensor-parallel-size 2 --tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice`, on vLLM 0.21.0 or later 2. Thinking is off in the template unless you pass `enable_thinking` true or start the server with `--default-chat-template-kwargs '{"enable_thinking": true}'`, which Poolside recommends for agentic coding 3. Pass earlier reasoning back as `reasoning_content` in the history, since the card says reasoning may stop in later steps if it's dropped 4. Sampling per the card is temperature 1.0 and top_k 20 5. The FP8 build's Docker image and serve.sh predate upstream support and set a 4,096-token context, so prefer a released vLLM 6. It doesn't fit our 128 GB reference machine at any published precision 7. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented ## Questions ### Which is better for AI agents, Hy-Memory-30B-A3B or Laguna M.1? Hy-Memory-30B-A3B scores 61 (C) on agent readiness against Laguna M.1's 56.7 (C), and leads in 3 of 4 scored categories. Laguna M.1 leads on agent interface. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-laguna-m-1.json, and with the fewest tokens: https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-laguna-m-1.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "hy-memory-30b-a3b", "b": "laguna-m-1"}`. 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[Laguna M.1 vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-8b-instruct.md) - [Laguna M.1 vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/laguna-m-1-vs-qwopus3-8-27b-flash-v2.md) - [Laguna M.1 vs Recursive Flint](https://www.anchorterminal.com/compare/laguna-m-1-vs-recursive-flint.md) - [Laguna M.1 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/laguna-m-1-vs-ripple-65b-preview.md) - [Laguna M.1 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/laguna-m-1-vs-sensenova-loop-9b-preview.md) - [Laguna M.1 vs SmolLM3-3B](https://www.anchorterminal.com/compare/laguna-m-1-vs-smollm3-3b.md) - [Laguna M.1 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/laguna-m-1-vs-spark-x2-6-4b.md) - [Laguna M.1 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/laguna-m-1-vs-step-3-7-flash.md) - [Laguna M.1 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/laguna-m-1-vs-swift-1-5-qwen3-8-27b.md) - [Laguna M.1 vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/laguna-m-1-vs-swift-1-5-qwen3-8-flash-next.md) - [Laguna M.1 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/laguna-m-1-vs-thinkingcap-qwen3-8-27b.md) - [Laguna M.1 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/laguna-m-1-vs-unlimited-ocr-2.md) - [Laguna M.1 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/laguna-m-1-vs-wevisone-vl-2b.md) - [Laguna M.1 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/laguna-m-1-vs-wevisone-vl-4b.md) - [Laguna M.1 vs ZAYA1-8B](https://www.anchorterminal.com/compare/laguna-m-1-vs-zaya1-8b.md) --- Query this catalogue directly over MCP: https://www.anchorterminal.com/mcp (Streamable HTTP, no key). How: https://www.anchorterminal.com/agents/#mcp