{ "data": { "a": { "slug": "glm-5-3-flash", "name": "GLM-5.3-Flash", "vendor": "Z.ai", "vendorUrl": "https://z.ai", "kind": "weights", "category": "open-weights", "summary": "GLM-5.3-Flash is Z.ai's open-weight multimodal mixture-of-experts model, 320B parameters with 18B active and a 1,048,576-token context, published on Hugging Face in FP8 and BF16 under the MIT licence.", "url": "https://www.anchorterminal.com/tools/glm-5-3-flash", "markdownUrl": "https://www.anchorterminal.com/tools/glm-5-3-flash.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/glm-5-3-flash.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/glm-5-3-flash.json", "dns": "glm-5-3-flash.grade.anchorterminal.com", "repo": "https://huggingface.co/zai-org/GLM-5.3-Flash", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "zai-org/GLM-5.3-Flash" }, { "registry": "huggingface", "name": "zai-org/GLM-5.3-Flash-BF16" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither repository is gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the MIT licence. Z.ai also sells hosted access as glm-5.3-flash on its own API, which isn't graded here. Running the weights yourself costs the hardware, 328.4 GB for the FP8 files and 642.7 GB for BF16 (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-02" }, "docsUrl": "https://huggingface.co/zai-org/GLM-5.3-Flash", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "mit", "safetensors", "fp8", "self-hosted" ], "lastRelease": "2026-08-26", "graded": true, "anchor": { "graded": true, "score": 65.1, "grade": "B", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 13, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 75, "interface": 70, "reach": 60, "risk": 55, "upkeep": 58 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-11-02" }, "negative": 0, "verdict": "An MIT-licensed multimodal agent and coding model with a 1M-token context and several tool calls per turn, served by vLLM, SGLang, llama.cpp and Transformers. Z.ai's smallest build is 328 GB in FP8, so it needs a multi-GPU server. The main risk is that nothing on safety or prompt injection is published and there is no way to report a flaw.", "bestFor": "Teams with a multi-GPU server who want an MIT-licensed multimodal agent model with a 1M-token context, or agents that call it through a hosted provider", "strengths": [ "MIT weights with no use policy, downloadable with no account", "Model code in released llama.cpp b11768, vLLM 0.32.0, SGLang 0.5.22 and Transformers 5.16.1", "Safetensors only, no remote code, files in Z.ai's own organisation with a SHA-256 per shard", "1,048,576 tokens of native context and several tool calls per turn in the chat template", "Chat template changed as recently as 15 October 2026, and hosted by Z.ai and six other providers for agents without the hardware" ], "weaknesses": [ "Z.ai's smallest build is 328.4 GB in FP8, and no Ollama or LM Studio local build exists", "No safety evaluation, agent-use guidance or security contact", "Thinking is always on, and reasoning_effort only lowers it to low", "The tool-call format lives only in the chat template, with the parser named by the runtimes", "The card leaves out intended use, limitations, context length and knowledge cut-off, and no zai-org reply on discussions since 28 September" ], "agentNotes": [ "Start it with `vllm serve zai-org/GLM-5.3-Flash --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser glm47 --reasoning-parser glm47` (vLLM 0.32.0 or later, about 386 GB of GPU memory per vLLM's recipe)", "Use the glm47 tool-call and reasoning parsers in vLLM or SGLang; calls come back as tool_call blocks with arg_key and arg_value pairs", "Pass `chat_template_kwargs: {\"reasoning_effort\": \"low\"}` for short tasks; anything other than low or high falls back to max", "Don't rely on `thinking: false` from SGLang's cookbook, since the template has no such switch and always opens a think block", "Set `clear_thinking: true` in chat use, since the default keeps earlier reasoning in the prompt", "On a 128 GB machine only third-party low-bit builds come near fitting, none of them from Z.ai" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "B", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 65.1 } ], "editorialScores": { "capability": 75, "interface": 70, "reach": 60, "risk": 55, "upkeep": 58 }, "provenanceScore": 61, "checked": { "categories": { "capability": "2026-11-02", "interface": "2026-11-02", "reach": "2026-11-02", "risk": "2026-11-02", "upkeep": "2026-11-02" }, "last": "2026-11-02", "oldest": "2026-11-02", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "# vLLM 0.32.0 or later, per vLLM's recipe (FP8, about 386 GB of GPU memory)\nvllm serve zai-org/GLM-5.3-Flash --tensor-parallel-size 4 \\\n --enable-auto-tool-choice --tool-call-parser glm47 --reasoning-parser glm47\n# or, with llama.cpp and llama.cpp's own GGUF:\nllama-server -hf ggml-org/GLM-5.3-Flash-GGUF:Q4_K --jinja", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\": \"zai-org/GLM-5.3-Flash\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"chat_template_kwargs\": {\"reasoning_effort\": \"low\"}}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/glm-5-3-flash" }, "sameCompany": [ "glm-4-5-air", "glm-4-7-flash", "glm-5-3" ], "weights": { "trainedBy": "Z.ai", "params": "320B", "active": "18B", "context": 1048576, "goodFor": "Multimodal agent and coding model: text, images and video in, text out, with tool calling and always-on reasoning.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "GLM-5.3-Flash-FP4-Local", "url": "https://huggingface.co/nvidia/GLM-5.3-Flash-FP4-Local", "who": "NVIDIA", "precision": "nvfp4", "sizeGB": 177.5, "memoryGB": 256, "format": "safetensors", "note": "NVIDIA's own FP4 build (Model Optimizer), made for NVIDIA Blackwell GPUs such as DGX Spark and RTX PRO 6000; FP4 kernels don't run on the AMD reference machine. Not the publisher's build, so it doesn't count toward Reach's memory item. Size summed from the Hub's file list on 21 October 2026." }, { "name": "GLM-5.3-Flash-NVFP4", "url": "https://huggingface.co/nvidia/GLM-5.3-Flash-NVFP4", "who": "NVIDIA", "precision": "nvfp4", "sizeGB": 204.4, "memoryGB": 256, "format": "safetensors", "note": "NVIDIA's own FP4 build (Model Optimizer), made for NVIDIA Blackwell GPUs such as DGX Spark and RTX PRO 6000; FP4 kernels don't run on the AMD reference machine. Not the publisher's build, so it doesn't count toward Reach's memory item. Size summed from the Hub's file list on 21 October 2026." }, { "name": "ggml-org GLM-5.3-Flash-GGUF (Q4_K)", "url": "https://huggingface.co/ggml-org/GLM-5.3-Flash-GGUF", "who": "llama.cpp", "precision": "q4_k", "sizeGB": 182.03, "memoryGB": 256, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From ggml-org, the llama.cpp project's organisation. 181.37 GB of Q4_K weights plus the 0.65 GB Q8_0 vision projector, from the Hub's model information on 10 November 2026. The 2-bit files (Q2_K 133.8 GB, Q2_K_S 110.0 GB) are below the 4-bit line and aren't listed. Recorded after the last grading, so Reach doesn't count it yet" }, { "name": "GLM-5.3-Flash", "url": "https://huggingface.co/zai-org/GLM-5.3-Flash", "who": "publisher", "precision": "fp8", "sizeGB": 328.3, "memoryGB": 512, "format": "safetensors" }, { "name": "GLM-5.3-Flash-BF16", "url": "https://huggingface.co/zai-org/GLM-5.3-Flash-BF16", "who": "publisher", "precision": "bf16", "sizeGB": 642.7, "memoryGB": 1024, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "vllm serve zai-org/GLM-5.3-Flash --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser glm47 --reasoning-parser glm47", "runBuild": "GLM-5.3-Flash", "checked": "2026-10-21", "minMemoryGB": 256 }, "family": { "lab": "Z.ai (Zhipu AI)", "family": "GLM-5", "url": "https://www.anchorterminal.com/open-weights/#zai", "others": [ { "name": "GLM-5", "url": "https://huggingface.co/zai-org/GLM-5", "params": "744B-A40B" }, { "name": "GLM-5.1", "url": "https://huggingface.co/zai-org/GLM-5.1", "params": "744B-A40B" }, { "name": "GLM-5.2", "url": "https://huggingface.co/zai-org/GLM-5.2", "params": "744B-A40B" }, { "name": "GLM-5.3", "url": "https://huggingface.co/zai-org/GLM-5.3", "params": "744B-A40B", "listing": "https://www.anchorterminal.com/tools/glm-5-3" } ], "olderCount": 19, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Z.AI Co., Ltd (named in the licence's copyright line)", "domain": "z.ai", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/zai-org/GLM-5.3-Flash/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The LICENSE file reads Copyright (c) 2026 Z.AI Co., Ltd. The registration date of z.ai couldn't be read, since the RDAP service answered 429 on 21 October 2026.", "Nothing is hosted, so the MIT licence stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history. Early messages say only \"update\"; later ones say what changed (a chat template fix on 4 September, evaluation results on 7 September and 12 October, a chat template speed-up on 15 October).", "z.ai/.well-known/security.txt and docs.z.ai/.well-known/security.txt answered 404 on 21 October 2026." ], "score": 61 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/glm-5-3-flash.json", "live": { "slug": "glm-5-3-flash", "securityTxt": { "url": "https://z.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-10T16:09:36.154640571Z" }, "domain": { "domain": "z.ai", "registered": "2017-12-15", "source": "https://rdap.identitydigital.services/rdap/domain/z.ai", "checkedAt": "2026-11-08T15:05:18.801433245Z" }, "pages": [ { "url": "https://huggingface.co/zai-org/GLM-5.3-Flash/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-11-10T16:24:02.708352819Z", "changedAt": "2026-11-10T16:24:02.708352819Z", "fingerprint": "108904f1a213" } ], "updatedAt": "2026-11-10T16:24:02.708352819Z" } }, "answer": "GLM-5.3-Flash scores 65.1 (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.", "b": { "slug": "hy-memory-30b-a3b", "name": "Hy-Memory-30B-A3B", "vendor": "Tencent (Hy team)", "vendorUrl": "https://aistudio.tencent.com", "kind": "weights", "category": "open-weights", "summary": "Hy-Memory-30B-A3B is Tencent Hunyuan's open-weight memory writer, a 30B mixture of experts with about 3B active, trained to decide what an agent's memory framework keeps and updates inside Mem0, MemOS and Hy-Memory, under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/hy-memory-30b-a3b", "markdownUrl": "https://www.anchorterminal.com/tools/hy-memory-30b-a3b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/hy-memory-30b-a3b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/hy-memory-30b-a3b.json", "dns": "hy-memory-30b-a3b.grade.anchorterminal.com", "repo": "https://huggingface.co/tencent/Hy-Memory-30B-A3B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "tencent/Hy-Memory-30B-A3B" }, { "registry": "huggingface", "name": "tencent/Hy-Memory-30B-A3B-GGUF" }, { "registry": "huggingface", "name": "tencent/Hy-Memory-30B-A3B-FP8" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main, FP8 and GGUF repositories are not gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, 60.1 GB for the BF16 files, 30.6 GB in FP8 or 18.2 GB for Tencent's Q4_K_M GGUF (checked 2026-11-10).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-10" }, "docsUrl": "https://huggingface.co/tencent/Hy-Memory-30B-A3B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "agent-memory", "specialist", "apache-2.0", "safetensors", "fp8", "gguf", "self-hosted" ], "lastRelease": "2026-10-16", "graded": true, "anchor": { "graded": true, "score": 61, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 54, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 65, "reach": 85, "risk": 60, "upkeep": 68 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "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.", "bestFor": "Agents with an external memory framework that want a local model for the write path on a 24 GB card", "strengths": [ "Made for one job, deciding what a memory framework stores and updates, and tested in four frameworks", "Fits a 24 GB card from Tencent's own Q4_K_M GGUF", "Runs in llama.cpp, vLLM, SGLang and Transformers from their releases", "Plain Apache 2.0, ungated, safetensors and GGUF only", "Tencent replied to three of the four discussions" ], "weaknesses": [ "Not a general assistant or a tool caller", "No limitations, safety results or guidance on untrusted text in what it reads", "Training data isn't described, and the card gives no context length", "The results are on a 20% subsample, run by Tencent", "No inference provider, so it has to be run locally" ], "agentNotes": [ "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_effort` as no_think in `chat_template_kwargs` and 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" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 65, "reach": 85, "risk": 60, "upkeep": 68 }, "provenanceScore": 75, "checked": { "categories": { "interface": "2026-11-10", "reach": "2026-11-10", "risk": "2026-11-10", "upkeep": "2026-11-10" }, "last": "2026-11-10", "oldest": "2026-11-10", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "llama-server -hf tencent/Hy-Memory-30B-A3B-GGUF:Q4_K_M --jinja\n# or, with vLLM, as the card names it:\nvllm serve tencent/Hy-Memory-30B-A3B --served-model-name hy-memory", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\": \"hy-memory\", \"temperature\": 0, \"messages\": [{\"role\": \"system\", \"content\": \"Extract explicit user facts and preferences. Return only a JSON object with a memories list of strings.\"}, {\"role\": \"user\", \"content\": \"I live in Seattle and I am vegetarian.\"}], \"chat_template_kwargs\": {\"reasoning_effort\": \"no_think\"}}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/hy-memory-30b-a3b" }, "sameCompany": [ "hy-memory", "tencentdb-agent-memory", "hunyuanocr", "hy3", "wevisone-vl-2b", "wevisone-vl-4b" ], "weights": { "trainedBy": "Tencent (Hunyuan)", "builtOn": "Not declared. The architecture is Tencent's hy_v3, as Hy3", "params": "30.1B", "active": "3B", "context": 262144, "goodFor": "The write path of an agent's memory framework. It reads a conversation and decides what to store and how to update it, in Mem0, MemOS or Hy-Memory, in place of a general model.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Hy-Memory-30B-A3B-Q4_K_M", "url": "https://huggingface.co/tencent/Hy-Memory-30B-A3B-GGUF", "who": "publisher", "precision": "q4_k_m", "sizeGB": 18.24, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "The smallest file at 4 bits. Q3_K_M (14.45 GB) and the two files with experts at 1 or 2 bits are below the 4-bit line and are not listed" }, { "name": "Hy-Memory-30B-A3B-Q6_K", "url": "https://huggingface.co/tencent/Hy-Memory-30B-A3B-GGUF", "who": "publisher", "precision": "q6_k", "sizeGB": 24.71, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp" ] }, { "name": "Hy-Memory-30B-A3B-FP8", "url": "https://huggingface.co/tencent/Hy-Memory-30B-A3B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 30.58, "memoryGB": 48, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ] }, { "name": "Hy-Memory-30B-A3B-Q8_0", "url": "https://huggingface.co/tencent/Hy-Memory-30B-A3B-GGUF", "who": "publisher", "precision": "q8_0", "sizeGB": 31.99, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp" ] }, { "name": "Hy-Memory-30B-A3B", "url": "https://huggingface.co/tencent/Hy-Memory-30B-A3B", "who": "publisher", "precision": "bf16", "sizeGB": 60.13, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)", "Transformers" ] } ], "run": "llama-server -hf tencent/Hy-Memory-30B-A3B-GGUF:Q4_K_M --jinja", "runBuild": "Hy-Memory-30B-A3B-Q4_K_M", "checked": "2026-11-10", "minMemoryGB": 24 }, "family": { "lab": "Tencent (Hunyuan / Hy)", "family": "Hy-Memory", "url": "https://www.anchorterminal.com/open-weights/#tencent", "olderCount": 22 }, "area": "models", "provenance": { "legalEntity": "Tencent (named in the LICENSE file's copyright line)", "domain": "tencent.com", "domainRegistered": "1998-09-14", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/tencent/Hy-Memory-30B-A3B/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "The LICENSE file reads Copyright (C) 2026 Tencent above the Apache 2.0 text, as on Hy3.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history, five commits on 15 and 16 October 2026, the uploads, the card and the licence.", "The Hy3 listing found no security.txt on Tencent's domains and records Tencent's Security Response Center as the reporting route. unchecked on 10 November." ], "score": 75 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/hy-memory-30b-a3b.json" }, "edges": [ { "by": 25, "category": "reach", "leads": "hy-memory-30b-a3b", "name": "Reach", "reasons": { "a": "Memory earns nothing, since Z.ai's smallest build is the 328.4 GB FP8 one, about 394 GB with the fifth added, and neither Ollama nor LM Studio has a local build (both list it as cloud only). Four runtimes run it in a released version, llama.cpp, vLLM, SGLang and Transformers, for 20, while Ollama and LM Studio don't. Z.ai publishes one quantised build, FP8, for 10. The MIT licence with nothing added gives the full 15, the files download with no account or gate for 10, and Z.ai's own API plus six providers on Hugging Face serve it, for 5.", "b": "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." }, "scores": { "a": 60, "b": 85 } }, { "by": 10, "category": "upkeep", "leads": "hy-memory-30b-a3b", "name": "Documentation and upkeep", "reasons": { "a": "The card covers architecture, parameters and languages and misses four of the seven items, for 8. Training data is 5, a 30T-token corpus described with no sources or cut-off. The last change, a chat template speed-up on 15 October in both repositories, is within 30 days on 2 November, for 20; Z.ai's GLM-5 GitHub repository has changed only its docs since. Responsiveness is 10, as zai-org answered 14 of the 38 recent discussions others opened (26 August to 30 October), the last on 28 September, which is some but not most. Version policy is 5 for a first release, and file health is the full 10, with files and card agreeing and no broken-file report waiting as far as the research record shows.", "b": "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." }, "scores": { "a": 58, "b": 68 } }, { "by": 5, "category": "risk", "leads": "hy-memory-30b-a3b", "name": "Risk", "reasons": { "a": "File safety is full, with safetensors only, no Python and no remote code needed in Transformers 5.16.1 or later. Provenance is full, files in the zai-org organisation Z.ai links from GitHub with a SHA-256 for every shard. Safety evaluation and agent guidance are 0, as nothing is published on either. Licence risk is the full 15, since MIT adds no policy, threshold or changeable terms. The security programme is 0, with no security.txt on z.ai or docs.z.ai, no SECURITY.md on GitHub and no advisories found.", "b": "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." }, "scores": { "a": 55, "b": 60 } }, { "by": 5, "category": "interface", "leads": "glm-5-3-flash", "name": "Agent interface", "reasons": { "a": "A Jinja chat template sits in both repositories, for 20. The tool-calling format is 10, because it is defined only by the template and the glm47 parser is named by vLLM and SGLang rather than by Z.ai. The template takes several calls in one turn, for 20. Structured output is 0, with nothing on the card and only the hosted API documented for JSON. Reasoning control is 10, a per-request budget through reasoning_effort, with the reasoning coming back in a think block, though thinking can't be turned off. Context is 10, 1,048,576 tokens native with no rotary scaling.", "b": "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." }, "scores": { "a": 70, "b": 65 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Z.ai", "b": "Tencent (Hy team)", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "MIT", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-08-26", "b": "2026-10-16", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "2.8/5 (4)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "GLM-5.3-Flash scores 65.1 (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.", "question": "Which is better for AI agents, GLM-5.3-Flash or Hy-Memory-30B-A3B?" } ], "goodFor": [ { "aheadOn": [ "Agent interface, 70 against 65" ], "also": null, "goodFor": "Teams with a multi-GPU server who want an MIT-licensed multimodal agent model with a 1M-token context, or agents that call it through a hosted provider", "slug": "glm-5-3-flash", "watchFor": "Z.ai's smallest build is 328.4 GB in FP8, and no Ollama or LM Studio local build exists" }, { "aheadOn": [ "Reach, 85 against 60", "Risk, 60 against 55", "Documentation and upkeep, 68 against 58" ], "also": null, "goodFor": "Agents with an external memory framework that want a local model for the write path on a 24 GB card", "slug": "hy-memory-30b-a3b", "watchFor": "Not a general assistant or a tool caller" } ], "history": null, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": 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"https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-4b-instruct.json", "title": "Hy-Memory-30B-A3B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-8b-instruct.json", "title": "Hy-Memory-30B-A3B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwopus3-8-27b-flash-v2.json", "title": "Hy-Memory-30B-A3B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-recursive-flint.json", "title": "Hy-Memory-30B-A3B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ripple-65b-preview.json", "title": "Hy-Memory-30B-A3B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-sensenova-loop-9b-preview.json", "title": "Hy-Memory-30B-A3B vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-smollm3-3b.json", "title": "Hy-Memory-30B-A3B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-spark-x2-6-4b.json", "title": "Hy-Memory-30B-A3B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-step-3-7-flash.json", "title": "Hy-Memory-30B-A3B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-swift-1-5-qwen3-8-27b.json", "title": "Hy-Memory-30B-A3B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Hy-Memory-30B-A3B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-thinkingcap-qwen3-8-27b.json", "title": "Hy-Memory-30B-A3B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-unlimited-ocr-2.json", "title": "Hy-Memory-30B-A3B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-wevisone-vl-2b.json", "title": "Hy-Memory-30B-A3B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-wevisone-vl-4b.json", "title": "Hy-Memory-30B-A3B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-zaya1-8b.json", "title": "Hy-Memory-30B-A3B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 25, "edge": "hy-memory-30b-a3b", "glm-5-3-flash": 60, "hy-memory-30b-a3b": 85, "key": "reach", "name": "Reach", "weight": 25 }, { "by": 5, "edge": "hy-memory-30b-a3b", "glm-5-3-flash": 55, "hy-memory-30b-a3b": 60, "key": "risk", "name": "Risk", "weight": 20 }, { "by": 5, "edge": "glm-5-3-flash", "glm-5-3-flash": 70, "hy-memory-30b-a3b": 65, "key": "interface", "name": "Agent interface", "weight": 10 }, { "by": 10, "edge": "hy-memory-30b-a3b", "glm-5-3-flash": 58, "hy-memory-30b-a3b": 68, "key": "upkeep", "name": "Documentation and upkeep", "weight": 10 } ], "summary": "GLM-5.3-Flash scores 65.1 (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.", "tasks": null, "tutorials": [], "verdicts": { "glm-5-3-flash": "An MIT-licensed multimodal agent and coding model with a 1M-token context and several tool calls per turn, served by vLLM, SGLang, llama.cpp and Transformers. Z.ai's smallest build is 328 GB in FP8, so it needs a multi-GPU server. The main risk is that nothing on safety or prompt injection is published and there is no way to report a flaw.", "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." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-hy-memory-30b-a3b", "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-hy-memory-30b-a3b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-hy-memory-30b-a3b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-hy-memory-30b-a3b.min.md" }, "markdown": "GLM-5.3-Flash scores 65.1 (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.\n\n- GLM-5.3-Flash: grade B, 65.1/100, rank #18 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/glm-5-3-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/glm-5-3-flash.json\n- 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\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### GLM-5.3-Flash (B)\n\nGood for: Teams with a multi-GPU server who want an MIT-licensed multimodal agent model with a 1M-token context, or agents that call it through a hosted provider\n\nAhead on:\n- Agent interface, 70 against 65\n\nWatch for: Z.ai's smallest build is 328.4 GB in FP8, and no Ollama or LM Studio local build exists\n\n### Hy-Memory-30B-A3B (C)\n\nGood for: Agents with an external memory framework that want a local model for the write path on a 24 GB card\n\nAhead on:\n- Reach, 85 against 60\n- Risk, 60 against 55\n- Documentation and upkeep, 68 against 58\n\nWatch for: Not a general assistant or a tool caller\n\n\n## Why the edge\n\n### Reach: Hy-Memory-30B-A3B +25\n\n- GLM-5.3-Flash (60): Memory earns nothing, since Z.ai's smallest build is the 328.4 GB FP8 one, about 394 GB with the fifth added, and neither Ollama nor LM Studio has a local build (both list it as cloud only). Four runtimes run it in a released version, llama.cpp, vLLM, SGLang and Transformers, for 20, while Ollama and LM Studio don't. Z.ai publishes one quantised build, FP8, for 10. The MIT licence with nothing added gives the full 15, the files download with no account or gate for 10, and Z.ai's own API plus six providers on Hugging Face serve it, for 5.\n- 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.\n\n### Documentation and upkeep: Hy-Memory-30B-A3B +10\n\n- GLM-5.3-Flash (58): The card covers architecture, parameters and languages and misses four of the seven items, for 8. Training data is 5, a 30T-token corpus described with no sources or cut-off. The last change, a chat template speed-up on 15 October in both repositories, is within 30 days on 2 November, for 20; Z.ai's GLM-5 GitHub repository has changed only its docs since. Responsiveness is 10, as zai-org answered 14 of the 38 recent discussions others opened (26 August to 30 October), the last on 28 September, which is some but not most. Version policy is 5 for a first release, and file health is the full 10, with files and card agreeing and no broken-file report waiting as far as the research record shows.\n- 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.\n\n### Risk: Hy-Memory-30B-A3B +5\n\n- GLM-5.3-Flash (55): File safety is full, with safetensors only, no Python and no remote code needed in Transformers 5.16.1 or later. Provenance is full, files in the zai-org organisation Z.ai links from GitHub with a SHA-256 for every shard. Safety evaluation and agent guidance are 0, as nothing is published on either. Licence risk is the full 15, since MIT adds no policy, threshold or changeable terms. The security programme is 0, with no security.txt on z.ai or docs.z.ai, no SECURITY.md on GitHub and no advisories found.\n- 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.\n\n### Agent interface: GLM-5.3-Flash +5\n\n- GLM-5.3-Flash (70): A Jinja chat template sits in both repositories, for 20. The tool-calling format is 10, because it is defined only by the template and the glm47 parser is named by vLLM and SGLang rather than by Z.ai. The template takes several calls in one turn, for 20. Structured output is 0, with nothing on the card and only the hosted API documented for JSON. Reasoning control is 10, a per-request budget through reasoning_effort, with the reasoning coming back in a think block, though thinking can't be turned off. Context is 10, 1,048,576 tokens native with no rotary scaling.\n- 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.\n\n## Score by category\n\n| Category | Weight | GLM-5.3-Flash | Hy-Memory-30B-A3B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 60 | 85 | Hy-Memory-30B-A3B +25 |\n| Risk | 20% (20 this run) | 55 | 60 | Hy-Memory-30B-A3B +5 |\n| Agent interface | 10% (10 this run) | 70 | 65 | GLM-5.3-Flash +5 |\n| Documentation and upkeep | 10% (10 this run) | 58 | 68 | Hy-Memory-30B-A3B +10 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **65.1 · B** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | GLM-5.3-Flash | Hy-Memory-30B-A3B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Z.ai | Tencent (Hy team) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | MIT | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-08-26 | 2026-10-16 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 2.8/5 (4) | none |\n\n## Verdicts\n\n**GLM-5.3-Flash.** An MIT-licensed multimodal agent and coding model with a 1M-token context and several tool calls per turn, served by vLLM, SGLang, llama.cpp and Transformers. Z.ai's smallest build is 328 GB in FP8, so it needs a multi-GPU server. The main risk is that nothing on safety or prompt injection is published and there is no way to report a flaw.\n\n**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.\n\n## Before you call either\n\n### GLM-5.3-Flash\n\n1. Start it with `vllm serve zai-org/GLM-5.3-Flash --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser glm47 --reasoning-parser glm47` (vLLM 0.32.0 or later, about 386 GB of GPU memory per vLLM's recipe)\n2. Use the glm47 tool-call and reasoning parsers in vLLM or SGLang; calls come back as tool_call blocks with arg_key and arg_value pairs\n3. Pass `chat_template_kwargs: {\"reasoning_effort\": \"low\"}` for short tasks; anything other than low or high falls back to max\n4. Don't rely on `thinking: false` from SGLang's cookbook, since the template has no such switch and always opens a think block\n5. Set `clear_thinking: true` in chat use, since the default keeps earlier reasoning in the prompt\n6. On a 128 GB machine only third-party low-bit builds come near fitting, none of them from Z.ai\n\n### Hy-Memory-30B-A3B\n\n1. Use it as the writer model in Mem0, MemOS or Hy-Memory, not as the model that answers the user\n2. 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\n3. Pass `reasoning_effort` as no_think in `chat_template_kwargs` and temperature 0, as the card's example does\n4. Ask for a JSON object in the system prompt and validate it yourself, since no schema constraint is documented\n5. Treat what it stores as unverified. It keeps what a conversation says, true or not\n\n## Questions\n\n### Which is better for AI agents, GLM-5.3-Flash or Hy-Memory-30B-A3B?\n\nGLM-5.3-Flash scores 65.1 (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.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/glm-5-3-flash-vs-hy-memory-30b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-5-3-flash-vs-hy-memory-30b-a3b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"glm-5-3-flash\", \"b\": \"hy-memory-30b-a3b\"}`. From a terminal: `anchor compare glm-5-3-flash hy-memory-30b-a3b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/glm-5-3-flash.json and https://www.anchorterminal.com/api/v1/tools/hy-memory-30b-a3b.json\n\n## Other comparisons with GLM-5.3-Flash or Hy-Memory-30B-A3B\n\n- [Agents-A1-4B vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-5-3-flash.md)\n- [Agents-A1-4B vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-hy-memory-30b-a3b.md)\n- [Apertus v1.5 8B vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-5-3-flash.md)\n- [Apertus v1.5 8B vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-hy-memory-30b-a3b.md)\n- [Apodex-1.1-mini vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-5-3-flash.md)\n- [Apodex-1.1-mini vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-hy-memory-30b-a3b.md)\n- [Arete-27B vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/arete-27b-vs-glm-5-3-flash.md)\n- [Arete-27B vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/arete-27b-vs-hy-memory-30b-a3b.md)\n- [BigBang-v1 vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-5-3-flash.md)\n- [BigBang-v1 vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/bigbang-v1-vs-hy-memory-30b-a3b.md)\n- [Carbon-X vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/carbon-x-vs-glm-5-3-flash.md)\n- [Carbon-X vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/carbon-x-vs-hy-memory-30b-a3b.md)\n- [Cola2 vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/cola2-vs-glm-5-3-flash.md)\n- [Cola2 vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/cola2-vs-hy-memory-30b-a3b.md)\n- [Cosmos-Reason2-2B vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-glm-5-3-flash.md)\n- [Cosmos-Reason2-2B vs 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Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-hy-memory-30b-a3b.md)\n- [Devstral Small 2 vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/devstral-small-2-vs-glm-5-3-flash.md)\n- [Devstral Small 2 vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/devstral-small-2-vs-hy-memory-30b-a3b.md)\n- [DiffusionGemma 26B A4B IT vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-glm-5-3-flash.md)\n- [DiffusionGemma 26B A4B IT vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-hy-memory-30b-a3b.md)\n- [EXAONE 4.5 33B vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-glm-5-3-flash.md)\n- [EXAONE 4.5 33B vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-hy-memory-30b-a3b.md)\n- [Gemma 4 12B IT vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-5-3-flash.md)\n- [Gemma 4 12B IT vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-hy-memory-30b-a3b.md)\n- [Gemma 4 26B A4B IT vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-5-3-flash.md)\n- [Gemma 4 26B A4B IT vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-hy-memory-30b-a3b.md)\n- [Gemma 4 31B IT vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-5-3-flash.md)\n- [Gemma 4 31B IT vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-hy-memory-30b-a3b.md)\n- [Gemma 4 E2B IT vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-glm-5-3-flash.md)\n- [Gemma 4 E2B IT vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-hy-memory-30b-a3b.md)\n- [Gemma 4 E4B IT vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-glm-5-3-flash.md)\n- [Gemma 4 E4B IT vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-hy-memory-30b-a3b.md)\n- [GLM-4.5-Air vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/glm-4-5-air-vs-glm-5-3-flash.md)\n- [GLM-4.5-Air vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-hy-memory-30b-a3b.md)\n- [GLM-4.7-Flash vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-glm-5-3-flash.md)\n- [GLM-4.7-Flash vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-hy-memory-30b-a3b.md)\n- [GLM-5.3-Flash vs gpt-oss-120b](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-gpt-oss-120b.md)\n- [GLM-5.3-Flash vs gpt-oss-20b](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-gpt-oss-20b.md)\n- [GLM-5.3-Flash vs Granite 4.2 30B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-granite-4-2-30b.md)\n- [GLM-5.3-Flash vs Granite 4.2 3B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-granite-4-2-3b.md)\n- 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M.1](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-laguna-m-1.md)\n- [GLM-5.3-Flash vs Laguna S 2.1](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-laguna-s-2-1.md)\n- [GLM-5.3-Flash vs Laguna XS 2.1](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-laguna-xs-2-1.md)\n- [GLM-5.3-Flash vs LBS-27B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-lbs-27b.md)\n- [GLM-5.3-Flash vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-lfm2-5-2-6b.md)\n- [GLM-5.3-Flash vs LFM3-1.7B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-lfm3-1-7b.md)\n- [GLM-5.3-Flash vs LFM3-Audio-2B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-lfm3-audio-2b.md)\n- [GLM-5.3-Flash vs LFM3-Omni-2B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-lfm3-omni-2b.md)\n- [GLM-5.3-Flash vs LFM3-VL-2B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-lfm3-vl-2b.md)\n- [GLM-5.3-Flash vs 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MiniCPM5-2B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-minicpm5-2b.md)\n- [GLM-5.3-Flash vs MiniMax-M2.5](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-minimax-m2-5.md)\n- [GLM-5.3-Flash vs MiniMax-M2.7](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-minimax-m2-7.md)\n- [GLM-5.3-Flash vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-minimax-m3-1-flash.md)\n- [GLM-5.3-Flash vs Mistral Large 4](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-mistral-large-4.md)\n- [GLM-5.3-Flash vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-muse-glimmer-30b.md)\n- [GLM-5.3-Flash vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nanbeige-4-2-3b.md)\n- [GLM-5.3-Flash vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nanbeige-4-3-33b.md)\n- [GLM-5.3-Flash vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [GLM-5.3-Flash vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [GLM-5.3-Flash vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-nano-30b-a3b.md)\n- [GLM-5.3-Flash vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-nano-4b.md)\n- [GLM-5.3-Flash vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [GLM-5.3-Flash vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-super-120b-a12b.md)\n- [GLM-5.3-Flash vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-ultra-550b-a55b.md)\n- [GLM-5.3-Flash vs North Mini Code 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