{ "data": { "a": { "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" }, "answer": "Nemotron 3 Nano Omni 30B A3B Reasoning and Hy-Memory-30B-A3B score within a point of each other on agent readiness, 61.2 (C) and 61 (C).", "b": { "slug": "nemotron-3-nano-omni-30b-a3b-reasoning", "name": "Nemotron 3 Nano Omni 30B A3B Reasoning", "vendor": "NVIDIA", "vendorUrl": "https://developer.nvidia.com/nemotron", "kind": "weights", "category": "open-weights", "summary": "Nemotron 3 Nano Omni is NVIDIA's open-weight multimodal model that takes video, audio, images and text and returns text, a hybrid Mamba-Transformer mixture of experts with about 31B parameters and 3B active, published in BF16, FP8 and NVFP4.", "url": "https://www.anchorterminal.com/tools/nemotron-3-nano-omni-30b-a3b-reasoning", "markdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-omni-30b-a3b-reasoning.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-omni-30b-a3b-reasoning.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-omni-30b-a3b-reasoning.json", "dns": "nemotron-3-nano-omni-30b-a3b-reasoning.grade.anchorterminal.com", "repo": "https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16", "license": "NVIDIA Open Model Agreement (custom, commercial use allowed, not an OSI licence)", "transports": [], "packages": [ { "registry": "huggingface", "name": "nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16" }, { "registry": "huggingface", "name": "nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8" }, { "registry": "huggingface", "name": "nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-NVFP4" }, { "registry": "ollama", "name": "nemotron3" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The BF16, FP8 and NVFP4 repositories are not gated (checked 2026-10-21). The card's download section still shows an hf auth login step, which the ungated files don't need.", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the NVIDIA Open Model Agreement. Running it yourself costs the hardware, 66.1 GB for the BF16 files, 35.2 GB for FP8 and 22.4 GB for NVFP4, per the Hub's file lists. NVIDIA's own API also serves it (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": "2026-11-03" }, "docsUrl": "https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16", "capabilities": [ "speech.stt", "speech.batch", "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "omni", "vision", "audio", "video", "reasoning", "tool-calling", "speech-to-text", "safetensors", "fp8", "nvfp4", "self-hosted" ], "lastRelease": "2026-08-24", "graded": true, "anchor": { "graded": true, "score": 61.2, "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": 27, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 34, "interface": 90, "reach": 85, "risk": 60, "upkeep": 70 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-11-03" }, "negative": 0, "verdict": "A local multimodal model for video with speech, long documents, charts, screens and English transcription, with tool calling, a reasoning switch and a 262,144-token context. It needs about 27 GB for NVIDIA's NVFP4 build on Blackwell, or 42 GB for FP8. The main risks are the remote code it loads and a repository where nobody answers questions.", "bestFor": "Agents with a 32 to 48 GB NVIDIA GPU that need one local model to read video, audio, documents and screens and call tools", "strengths": [ "Video, audio, images and text in one model, with word-level timestamps for transcription per the card", "Released support in vLLM, SGLang, llama.cpp, Ollama and LM Studio, plus NVIDIA's own FP8 and NVFP4 builds", "Tool calling with a named parser and NVIDIA cookbooks, and thinking that turns off or takes a budget per request", "Training data listed by dataset with sample and token counts", "Commercial use under an irrevocable licence with no use policy or threshold, ungated, and served by NVIDIA's own API" ], "weaknesses": [ "Loads with trust_remote_code in every documented runtime command", "English only, no speaker diarisation, translation or streaming input, and video capped at 2 minutes", "No published safety results and no agent-specific guidance such as prompt-injection advice", "None of the 11 questions in its three repositories has a reply", "The NVFP4 build needs NVIDIA Blackwell, and SGLang supports only the BF16 build so far, per the card" ], "agentNotes": [ "Start it with `vllm serve nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8 --max-model-len 131072 --trust-remote-code --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder --kv-cache-dtype fp8` on vLLM 0.20.0 or later, after `pip install \"vllm[audio]\"` if any audio will be sent", "Or run `ollama run nemotron3`; the 33b-nvfp4 and 33b-mlx tags are MLX builds for Apple silicon, and the q4_K_M tag is 28 GB", "Set `--media-io-kwargs '{\"video\": {\"fps\": 2, \"num_frames\": 256}}'` at launch, since vLLM otherwise samples about 32 frames per video", "Send `chat_template_kwargs: {\"enable_thinking\": false}` for transcription and quick answers, and use temperature 1.0 with top_k 1 for ASR as the card recommends", "Render PDF pages to PNG and send them as images, since the API takes no raw PDF", "Treat transcripts and text read from images or video as untrusted input before acting on them" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61.2 } ], "editorialScores": { "capability": 34, "interface": 90, "reach": 85, "risk": 60, "upkeep": 70 }, "provenanceScore": 72, "checked": { "categories": { "capability": "2026-11-03", "interface": "2026-11-03", "reach": "2026-11-03", "risk": "2026-11-03", "upkeep": "2026-11-03" }, "last": "2026-11-03", "lastMoved": "2026-11-03", "oldest": "2026-11-03", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run nemotron3\n# or, with vLLM 0.20.0 or later as the card gives it:\npip install \"vllm[audio]\"\nvllm serve nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8 --max-model-len 131072 \\\n --trust-remote-code --reasoning-parser nemotron_v3 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_coder --kv-cache-dtype fp8", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello\"}], \"chat_template_kwargs\": {\"enable_thinking\": false}}'" }, "letme": { "capability": "https://letme.dev/speech.stt", "tool": "https://letme.dev/nemotron-3-nano-omni-30b-a3b-reasoning" }, "sameCompany": [ "nemotron-3-5-super-vl", "nvidia-nemo-retriever", "nemo-guardrails", "nemotron-3-5-asr-streaming-0-6b", "nemotron-3-5-transcribe-30b-a4b", "parakeet-tdt-0-6b-v3", "parakeet-unified-en-0-6b", "nemotron-3-5-parse", "cosmos-reason2-2b", "nemotron-3-5-bolt-e4b-4gb", "nemotron-3-nano-30b-a3b", "nemotron-3-nano-4b", "nemotron-3-super-120b-a12b", "nemotron-3-ultra-550b-a55b", "nemotron-3-5-lightning-30b-a3b" ], "weights": { "trainedBy": "NVIDIA", "builtOn": "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16", "params": "31B", "active": "3B", "context": 262144, "goodFor": "Multimodal understanding of video with speech, documents, charts and screens, and English transcription, with tool calling and a reasoning budget.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "Nemotron-3-Nano-Omni-30B-A3B-Reasoning-NVFP4", "url": "https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 22.4, "memoryGB": 32, "format": "safetensors", "note": "NVFP4 needs NVIDIA Blackwell; the card's minimum is one RTX 5090 32 GB" }, { "name": "ollama nemotron3:33b-nvfp4", "url": "https://ollama.com/library/nemotron3:33b-nvfp4", "who": "ollama", "precision": "nvfp4", "sizeGB": 21, "memoryGB": 32, "format": "ollama", "runsOn": [ "Ollama" ], "note": "MLX build (digest f263b909bfad, the same as 33b-mlx), so Apple silicon only. Size as Ollama's tags page shows it, rounded to whole GB" }, { "name": "LM Studio nvidia/nemotron-3-nano-omni Q4_K_M", "url": "https://huggingface.co/lmstudio-community/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-GGUF", "who": "lmstudio", "precision": "q4_k_m", "sizeGB": 26.1, "memoryGB": 32, "format": "gguf", "runsOn": [ "LM Studio", "llama.cpp" ], "note": "Q4_K_M GGUF of 24.52 GB plus the 1.59 GB BF16 vision projector, the build behind LM Studio's catalogue entry" }, { "name": "Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8", "url": "https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 35.2, "memoryGB": 48, "format": "safetensors", "note": "The card says SGLang support for FP8 is still to come" }, { "name": "ollama nemotron3:33b-q4_K_M", "url": "https://ollama.com/library/nemotron3:33b-q4_K_M", "who": "ollama", "precision": "q4_k_m", "sizeGB": 28, "memoryGB": 48, "format": "ollama", "runsOn": [ "Ollama" ], "note": "digest baa676a14e13, text and image input per the tags page. Size as Ollama's tags page shows it, rounded to whole GB" }, { "name": "Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16", "url": "https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16", "who": "publisher", "precision": "bf16", "sizeGB": 66.1, "memoryGB": 96, "format": "safetensors", "note": "Includes 7.3 MB of sample media; loads with trust_remote_code" } ], "run": "vllm serve nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8 --max-model-len 131072 --trust-remote-code --video-pruning-rate 0.5 --media-io-kwargs '{\"video\": {\"fps\": 2, \"num_frames\": 256}}' --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder --kv-cache-dtype fp8", "runBuild": "Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8", "checked": "2026-10-21", "minMemoryGB": 32 }, "family": { "lab": "NVIDIA (Nemotron)", "family": "Nemotron 3", "url": "https://www.anchorterminal.com/open-weights/#nvidia", "others": [ { "name": "Nemotron 3 Nano 30B-A3B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16", "params": "30B total, 3B active (31.6B per the safetensors index)", "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b" }, { "name": "Nemotron 3 Nano 30B-A3B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16", "params": "30B total, 3B active" }, { "name": "Nemotron 3 Super 120B-A12B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16", "params": "120B total, 12B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b" }, { "name": "Nemotron 3 Super 120B-A12B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16", "params": "120B total, 12B active" }, { "name": "Nemotron 3 Nano 4B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16", "params": "4B", "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-4b" }, { "name": "Nemotron 3 Ultra 550B-A55B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16", "params": "550B total, 55B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-ultra-550b-a55b" }, { "name": "Nemotron 3 Ultra 550B-A55B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-BF16", "params": "550B total, 55B active" }, { "name": "Nemotron 3 Ultra 550B-A55B GenRM", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-GenRM", "params": "550B total, 55B active" }, { "name": "Nemotron 3.5 Lightning 30B-A3B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16", "params": "30B total, 3B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-lightning-30b-a3b" }, { "name": "Nemotron 3.5 Lightning 30B-A3B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-Base-BF16", "params": "30B total, 3B active" }, { "name": "Nemotron 3.5 Super VL 120B-A12B", 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"title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-swift-1-5-qwen3-8-27b.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-thinkingcap-qwen3-8-27b.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-wevisone-vl-2b.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-wevisone-vl-4b.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-zaya1-8b.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-zaya1-8b" }, { "json": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "Cohere Transcribe 03-2026 vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "Granite Speech 5.0 2B vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/granite-speech-5-0-2b-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "Ming-3.0-asr-tiny vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "Ming-3.0-audio-flash vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "Ming-3.0-audio-tiny vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "Nemotron 3.5 ASR Streaming 0.6B vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-transcribe-30b-a4b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "Nemotron 3.5 Transcribe 30B-A4B vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-transcribe-30b-a4b-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-parakeet-tdt-0-6b-v3.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Parakeet TDT 0.6B v3", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-parakeet-tdt-0-6b-v3" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-parakeet-unified-en-0-6b.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Parakeet Unified EN 0.6B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-parakeet-unified-en-0-6b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-asr-0-6b.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3-ASR-0.6B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-asr-0-6b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-asr-1-7b.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3-ASR-1.7B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-asr-1-7b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-underdog-bark-3.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Underdog Bark 3.0", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-underdog-bark-3" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-voxtral-mini-4b-realtime-2602.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Voxtral Mini 4B Realtime 2602", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-voxtral-mini-4b-realtime-2602" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 0, "edge": "", "hy-memory-30b-a3b": 85, "key": "reach", "name": "Reach", "nemotron-3-nano-omni-30b-a3b-reasoning": 85, "weight": 25 }, { "by": 0, "edge": "", "hy-memory-30b-a3b": 60, "key": "risk", "name": "Risk", "nemotron-3-nano-omni-30b-a3b-reasoning": 60, "weight": 20 }, { "by": 25, "edge": "nemotron-3-nano-omni-30b-a3b-reasoning", "hy-memory-30b-a3b": 65, "key": "interface", "name": "Agent interface", "nemotron-3-nano-omni-30b-a3b-reasoning": 90, "weight": 10 }, { "by": 2, "edge": "nemotron-3-nano-omni-30b-a3b-reasoning", "hy-memory-30b-a3b": 68, "key": "upkeep", "name": "Documentation and upkeep", "nemotron-3-nano-omni-30b-a3b-reasoning": 70, "weight": 10 } ], "summary": "Nemotron 3 Nano Omni 30B A3B Reasoning and Hy-Memory-30B-A3B score within a point of each other on agent readiness, 61.2 (C) and 61 (C). Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "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.", "nemotron-3-nano-omni-30b-a3b-reasoning": "A local multimodal model for video with speech, long documents, charts, screens and English transcription, with tool calling, a reasoning switch and a 262,144-token context. It needs about 27 GB for NVIDIA's NVFP4 build on Blackwell, or 42 GB for FP8. The main risks are the remote code it loads and a repository where nobody answers questions." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-nano-omni-30b-a3b-reasoning", "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.min.md" }, "markdown": "Nemotron 3 Nano Omni 30B A3B Reasoning and Hy-Memory-30B-A3B score within a point of each other on agent readiness, 61.2 (C) and 61 (C). Both do open-weight models you run yourself.\n\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- Nemotron 3 Nano Omni 30B A3B Reasoning: grade C, 61.2/100, rank #36 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-nano-omni-30b-a3b-reasoning.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-omni-30b-a3b-reasoning.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- Best speech-to-text APIs for AI agents: https://www.anchorterminal.com/best/speech-to-text/index.md\n- All 386 stt comparisons: https://www.anchorterminal.com/compare/speech-to-text/index.md\n\n## Which one, for what\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\nWatch for: Not a general assistant or a tool caller\n\n### Nemotron 3 Nano Omni 30B A3B Reasoning (C)\n\nGood for: Agents with a 32 to 48 GB NVIDIA GPU that need one local model to read video, audio, documents and screens and call tools\n\nAhead on:\n- Agent interface, 90 against 65\n\nWatch for: Loads with trust_remote_code in every documented runtime command\n\n\n## Why the edge\n\n### Agent interface: Nemotron 3 Nano Omni 30B A3B Reasoning +25\n\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- Nemotron 3 Nano Omni 30B A3B Reasoning (90): A Jinja template ships in the repository (20). The card names the qwen3_coder parser and NVIDIA's cookbooks show tools declared with the OpenAI schema and tool_calls read back (25). The template renders several calls per turn and the card says it supports tool calling (20). JSON output is claimed with no word on how to ask for it (5). Thinking turns off per request, a budget caps it and the parser returns the reasoning apart (10). The context is 262,144 tokens, trained in (10).\n\n## Score by category\n\n| Category | Weight | Hy-Memory-30B-A3B | Nemotron 3 Nano Omni 30B A3B Reasoning | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 85 | 85 | even |\n| Risk | 20% (20 this run) | 60 | 60 | even |\n| Agent interface | 10% (10 this run) | 65 | 90 | Nemotron 3 Nano Omni 30B A3B Reasoning +25 |\n| Documentation and upkeep | 10% (10 this run) | 68 | 70 | Nemotron 3 Nano Omni 30B A3B Reasoning +2 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **61.2 · C** | |\n\n## Facts side by side\n\n| Fact | Hy-Memory-30B-A3B | Nemotron 3 Nano Omni 30B A3B Reasoning |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Tencent (Hy team) | NVIDIA |\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 | Apache-2.0 | NVIDIA Open Model Agreement (custom, commercial use allowed, not an OSI licence) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-16 | 2026-08-24 |\n| Terms last updated | no document linked | no date given |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | not read yet | not found in the text |\n| Terms restrict automated access | not read yet | not found in the text |\n| Terms restrict benchmarking | not read yet | not found in the text |\n| Terms or service can change without notice | not read yet | not found in the text |\n| Arbitration or class-action waiver | not read yet | not found in the text |\n| Agent reviews | none | 3/5 (4) |\n\n## How the scores have moved\n\n### Hy-Memory-30B-A3B\n\nNo score change since it was listed.\n\n### Nemotron 3 Nano Omni 30B A3B Reasoning\n\n- +0.5 on 3 November 2026: correction\n\n## Verdicts\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**Nemotron 3 Nano Omni 30B A3B Reasoning.** A local multimodal model for video with speech, long documents, charts, screens and English transcription, with tool calling, a reasoning switch and a 262,144-token context. It needs about 27 GB for NVIDIA's NVFP4 build on Blackwell, or 42 GB for FP8. The main risks are the remote code it loads and a repository where nobody answers questions.\n\n## Before you call either\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### Nemotron 3 Nano Omni 30B A3B Reasoning\n\n1. Start it with `vllm serve nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8 --max-model-len 131072 --trust-remote-code --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder --kv-cache-dtype fp8` on vLLM 0.20.0 or later, after `pip install \"vllm[audio]\"` if any audio will be sent\n2. Or run `ollama run nemotron3`; the 33b-nvfp4 and 33b-mlx tags are MLX builds for Apple silicon, and the q4_K_M tag is 28 GB\n3. Set `--media-io-kwargs '{\"video\": {\"fps\": 2, \"num_frames\": 256}}'` at launch, since vLLM otherwise samples about 32 frames per video\n4. Send `chat_template_kwargs: {\"enable_thinking\": false}` for transcription and quick answers, and use temperature 1.0 with top_k 1 for ASR as the card recommends\n5. Render PDF pages to PNG and send them as images, since the API takes no raw PDF\n6. Treat transcripts and text read from images or video as untrusted input before acting on them\n\n## Questions\n\n### Which is better for AI agents, Hy-Memory-30B-A3B or Nemotron 3 Nano Omni 30B A3B Reasoning?\n\nNemotron 3 Nano Omni 30B A3B Reasoning and Hy-Memory-30B-A3B score within a point of each other on agent readiness, 61.2 (C) and 61 (C).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json, and with the fewest tokens: https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"hy-memory-30b-a3b\", \"b\": \"nemotron-3-nano-omni-30b-a3b-reasoning\"}`. From a terminal: `anchor compare hy-memory-30b-a3b nemotron-3-nano-omni-30b-a3b-reasoning`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/hy-memory-30b-a3b.json and https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-omni-30b-a3b-reasoning.json\n\n## Other comparisons with Hy-Memory-30B-A3B or Nemotron 3 Nano Omni 30B A3B Reasoning\n\n- [Agents-A1-4B vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-hy-memory-30b-a3b.md)\n- [Agents-A1-4B vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.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- [Apertus v1.5 8B vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.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- [Apodex-1.1-mini vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Arete-27B vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/arete-27b-vs-hy-memory-30b-a3b.md)\n- [Arete-27B vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/arete-27b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [BigBang-v1 vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/bigbang-v1-vs-hy-memory-30b-a3b.md)\n- [BigBang-v1 vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Carbon-X vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/carbon-x-vs-hy-memory-30b-a3b.md)\n- [Carbon-X vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/carbon-x-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Cola2 vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/cola2-vs-hy-memory-30b-a3b.md)\n- [Cola2 vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/cola2-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Cosmos-Reason2-2B vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-hy-memory-30b-a3b.md)\n- [Cosmos-Reason2-2B vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Darwin-36B-Opus vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-hy-memory-30b-a3b.md)\n- [Darwin-36B-Opus vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [DeepSeek-V3.2 vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-hy-memory-30b-a3b.md)\n- [DeepSeek-V3.2 vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [DeepSeek V4.1 Flash vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-hy-memory-30b-a3b.md)\n- [DeepSeek V4.1 Flash vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [DeepSeek-V4-Flash-0731 vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-hy-memory-30b-a3b.md)\n- [DeepSeek-V4-Flash-0731 vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nemotron-3-nano-omni-30b-a3b-reasoning.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- [Devstral Small 2 vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/devstral-small-2-vs-nemotron-3-nano-omni-30b-a3b-reasoning.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- [DiffusionGemma 26B A4B IT vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nemotron-3-nano-omni-30b-a3b-reasoning.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- [EXAONE 4.5 33B vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.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 12B IT vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-nano-omni-30b-a3b-reasoning.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 26B A4B IT vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nemotron-3-nano-omni-30b-a3b-reasoning.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 31B IT vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-nano-omni-30b-a3b-reasoning.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 E2B IT vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-nemotron-3-nano-omni-30b-a3b-reasoning.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- [Gemma 4 E4B IT vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nemotron-3-nano-omni-30b-a3b-reasoning.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-4.5-Air vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-nano-omni-30b-a3b-reasoning.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-4.7-Flash vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [GLM-5.3-Flash vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-hy-memory-30b-a3b.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 vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/glm-5-3-vs-hy-memory-30b-a3b.md)\n- [GLM-5.3 vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/glm-5-3-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [gpt-oss-120b vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-hy-memory-30b-a3b.md)\n- [gpt-oss-120b vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [gpt-oss-20b vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-hy-memory-30b-a3b.md)\n- [gpt-oss-20b vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Granite 4.2 30B vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-hy-memory-30b-a3b.md)\n- [Granite 4.2 30B vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Granite 4.2 3B vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-hy-memory-30b-a3b.md)\n- [Granite 4.2 3B vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Granite 4.2 8B vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-hy-memory-30b-a3b.md)\n- [Granite 4.2 8B vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Hy-Memory-30B-A3B vs Hy3](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-hy3.md)\n- [Hy-Memory-30B-A3B vs Inkling](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-inkling.md)\n- [Hy-Memory-30B-A3B vs Inkling-Small](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-inkling-small.md)\n- [Hy-Memory-30B-A3B vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Hy-Memory-30B-A3B vs Kimi K3](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-kimi-k3.md)\n- [Hy-Memory-30B-A3B vs Kimi K3.1 Pro](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-kimi-k3-1-pro.md)\n- [Hy-Memory-30B-A3B vs Kolibri-1](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-kolibri-1.md)\n- [Hy-Memory-30B-A3B vs Kronos](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-kronos.md)\n- [Hy-Memory-30B-A3B vs Laguna M.1](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-laguna-m-1.md)\n- [Hy-Memory-30B-A3B vs Laguna S 2.1](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-laguna-s-2-1.md)\n- [Hy-Memory-30B-A3B vs Laguna XS 2.1](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-laguna-xs-2-1.md)\n- [Hy-Memory-30B-A3B vs LBS-27B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-lbs-27b.md)\n- [Hy-Memory-30B-A3B vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-lfm2-5-2-6b.md)\n- [Hy-Memory-30B-A3B vs LFM3-1.7B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-lfm3-1-7b.md)\n- [Hy-Memory-30B-A3B vs LFM3-Audio-2B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-lfm3-audio-2b.md)\n- [Hy-Memory-30B-A3B vs LFM3-Omni-2B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-lfm3-omni-2b.md)\n- [Hy-Memory-30B-A3B vs LFM3-VL-2B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-lfm3-vl-2b.md)\n- [Hy-Memory-30B-A3B vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ling-3-0-tiny.md)\n- [Hy-Memory-30B-A3B vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ling-3-1-flash-1019.md)\n- [Hy-Memory-30B-A3B vs LongCat-2.5](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-longcat-2-5.md)\n- [Hy-Memory-30B-A3B vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-mimo-v2-6-distill-qwen-9b.md)\n- [Hy-Memory-30B-A3B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-mimo-v2-6-flash-rl.md)\n- [Hy-Memory-30B-A3B vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-minicpm-o-4-5.md)\n- [Hy-Memory-30B-A3B vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-minicpm-v-4-7-1b.md)\n- [Hy-Memory-30B-A3B vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-minicpm-v-4-7-35b-a3b.md)\n- [Hy-Memory-30B-A3B vs MiniCPM5-2B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-minicpm5-2b.md)\n- [Hy-Memory-30B-A3B vs MiniMax-M2.5](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-minimax-m2-5.md)\n- [Hy-Memory-30B-A3B vs MiniMax-M2.7](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-minimax-m2-7.md)\n- [Hy-Memory-30B-A3B vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-minimax-m3-1-flash.md)\n- [Hy-Memory-30B-A3B vs Mistral Large 4](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-mistral-large-4.md)\n- [Hy-Memory-30B-A3B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-muse-glimmer-30b.md)\n- [Hy-Memory-30B-A3B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nanbeige-4-2-3b.md)\n- [Hy-Memory-30B-A3B vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nanbeige-4-3-33b.md)\n- [Hy-Memory-30B-A3B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Hy-Memory-30B-A3B vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Hy-Memory-30B-A3B vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-nano-30b-a3b.md)\n- [Hy-Memory-30B-A3B vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-nano-4b.md)\n- [Hy-Memory-30B-A3B vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-super-120b-a12b.md)\n- [Hy-Memory-30B-A3B vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-ultra-550b-a55b.md)\n- [Hy-Memory-30B-A3B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-north-mini-code-1-0.md)\n- [Hy-Memory-30B-A3B vs occamy-1.0](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-occamy-1-0.md)\n- [Hy-Memory-30B-A3B vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-olmo-3-7b-think.md)\n- [Hy-Memory-30B-A3B vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ornith-1-5-122b.md)\n- [Hy-Memory-30B-A3B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ornith-1-5-35b-a3b.md)\n- [Hy-Memory-30B-A3B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ornith-1-5-397b.md)\n- [Hy-Memory-30B-A3B vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ornith-1-5-9b.md)\n- [Hy-Memory-30B-A3B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-otel-2-0-llm-31b-it.md)\n- [Hy-Memory-30B-A3B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen-agentworld-35b-a3b.md)\n- [Hy-Memory-30B-A3B vs 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run yourself, 61.2 and 61 out of 100. 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