{ "data": { "a": { "slug": "nemotron-3-ultra-550b-a55b", "name": "Nemotron 3 Ultra 550B A55B", "vendor": "NVIDIA", "vendorUrl": "https://developer.nvidia.com/nemotron", "kind": "weights", "category": "open-weights", "summary": "Nemotron 3 Ultra is NVIDIA's largest open-weight hybrid Mamba-2 and latent mixture-of-experts reasoning model for agents, 550B parameters with 55B active and up to 1M tokens of context, published on Hugging Face in BF16 and NVFP4 under OpenMDW-1.1.", "url": "https://www.anchorterminal.com/tools/nemotron-3-ultra-550b-a55b", "markdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-ultra-550b-a55b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-ultra-550b-a55b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nemotron-3-ultra-550b-a55b.json", "repo": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16", "license": "OpenMDW-1.1", "transports": [], "packages": [ { "registry": "huggingface", "name": "nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16" }, { "registry": "huggingface", "name": "nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The BF16 and NVFP4 repositories are not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under OpenMDW-1.1. Running it yourself costs the hardware, 1,121 GB for the BF16 repository (8 × B200 or 16 × H100 at least, per the card) or 352 GB for NVIDIA's NVFP4 build (4 × B200 or 8 × H100). Ollama lists it only as a cloud model (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": "2026-10-21" }, "docsUrl": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "mamba", "hybrid", "frontier", "reasoning", "tool-calling", "long-context", "multilingual", "safetensors", "nvfp4", "self-hosted" ], "lastRelease": "2026-06-04", "graded": true, "anchor": { "graded": true, "score": 68.8, "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": 1197, "categoryRank": 7, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 73, "interface": 90, "reach": 55, "risk": 70, "upkeep": 65 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "NVIDIA's frontier-scale agent cousin with trained parallel tool calls, up to a 1M-token context and training against prompt injection through tool output. It needs at least four Blackwell GPUs for the 352 GB NVFP4 build, so most agents will use NVIDIA's API or a provider. The main risks are serving commands that run repository code and no published safety results.", "bestFor": "Agents with a multi-GPU Blackwell or Hopper node, or using a hosted endpoint, that want NVIDIA's strongest open model for long agent runs", "strengths": [ "Trained against prompt injection in tool output, as NVIDIA's technical report documents", "Tool calls documented with a named parser, several per turn, and thinking off or medium effort per request", "Up to 1M tokens of context, with RULER at 1M in the report", "Training data listed with token counts and cut-offs, much of it published", "OpenMDW-1.1 with no gate, no threshold and no use policy, and NVIDIA's API plus two providers serve it" ], "weaknesses": [ "The smallest build is 352 GB and needs at least four B200-class GPUs", "The card's serving commands pass --trust-remote-code", "No safety evaluation results published", "No NVIDIA reply to any of nine questions on its two repositories", "No LICENSE file, and the BF16 card's SGLang command names a reasoning parser SGLang doesn't register" ], "agentNotes": [ "It doesn't fit the 128 GB reference machine at any precision. Call NVIDIA's API (model nvidia/nemotron-3-ultra-550b-a55b) or a provider instead", "With vLLM serve the NVFP4 build with `--trust-remote-code --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` on 4 × B200 or more", "With SGLang use `--reasoning-parser nemotron_3`, not the BF16 card's `nemotron_v3`, which SGLang doesn't register", "On SGLang with tools, pass `chat_template_kwargs` with `enable_thinking` true and `force_nonempty_content` true, as the card says, or reasoning and calls won't both parse", "Thinking is on by default. Pass `enable_thinking: false` for short tasks or `medium_effort: true` to cut reasoning tokens", "Use `temperature=1.0` and `top_p=0.95`, as the generation config sets" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "B", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 68.8 } ], "editorialScores": { "capability": 73, "interface": 90, "reach": 55, "risk": 70, "upkeep": 65 }, "provenanceScore": 73, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "# NVFP4 on 4 × B200 or more, with the card's vLLM command (abridged):\nvllm serve nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4 --served-model-name nvidia/nemotron-3-ultra \\\n --tensor-parallel-size 4 --trust-remote-code --kv-cache-dtype fp8 --max-model-len 262144 \\\n --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder \\\n --speculative-config '{\"method\": \"nemotron_h_mtp\", \"num_speculative_tokens\": 5}'", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"nvidia/nemotron-3-ultra\",\n \"messages\": [{\"role\": \"user\", \"content\": \"What is the capital of Japan?\"}],\n \"temperature\": 1.0, \"top_p\": 0.95,\n \"chat_template_kwargs\": {\"enable_thinking\": false}\n }'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/nemotron-3-ultra-550b-a55b" }, "sameCompany": [ "nemotron-3-5-super-vl", "nvidia-nemo-retriever", "nemo-guardrails", "nemotron-3-5-asr-streaming-0-6b", "parakeet-tdt-0-6b-v3", "cosmos-reason2-2b", "nemotron-3-nano-30b-a3b", "nemotron-3-nano-4b", "nemotron-3-nano-omni-30b-a3b-reasoning", "nemotron-3-super-120b-a12b", "nemotron-3-5-lightning-30b-a3b" ], "weights": { "trainedBy": "NVIDIA", "builtOn": "nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-BF16", "params": "550B", "active": "55B", "context": 1048576, "goodFor": "Frontier-scale hybrid Mamba and latent mixture-of-experts reasoning model for long-running agents, text only, in ten languages, with tool calling, a medium-effort mode and up to 1M tokens of context.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 352.38, "memoryGB": 512, "format": "safetensors", "note": "4 × B200, GB200, GB300 or B300, or 8 × H100, per its card. NVFP4 needs NVIDIA GPUs" }, { "name": "NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16", "who": "publisher", "precision": "bf16", "sizeGB": 1121.08, "memoryGB": 2048, "format": "safetensors", "note": "8 × B200 single node or 16 × H100 across nodes, per the card" } ], "run": "vllm serve nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4 --served-model-name nvidia/nemotron-3-ultra --tensor-parallel-size 4 --trust-remote-code --kv-cache-dtype fp8 --max-model-len 262144 --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder", "runBuild": "NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4", "checked": "2026-10-21", "minMemoryGB": 512 }, "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 Nano Omni 30B-A3B Reasoning", "url": "https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16", "params": "30B total, 3B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-omni-30b-a3b-reasoning" }, { "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", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16", "params": "120B total, 12B active", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-super-vl" }, { "name": "Nemotron 3.5 Super VL 120B-A12B Base", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-Base-BF16", "params": "120B total, 12B active" }, { "name": "Nemotron 3.5 Bolt 74B-A9B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-BF16", "params": "74B total, 9B active" }, { "name": "Nemotron Labs 3 Elastic 30B-A3B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-BF16", "params": "30B total, 3B active" }, { "name": "Nemotron Labs 3 Puzzle 75B-A9B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-BF16", "params": "75B total, 9B active" }, { "name": "Nemotron 3 Labs Ultra Math SFT", "url": "https://huggingface.co/nvidia/Nemotron-3-Labs-Ultra-Math-SFT", "params": "550B total, 55B active" }, { "name": "Nemotron 3 Labs Ultra Math RL", "url": "https://huggingface.co/nvidia/Nemotron-3-Labs-Ultra-Math-RL", "params": "550B total, 55B active" }, { "name": "Nemotron Labs 3 Competitive Coding 550B-A55B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Competitive-Coding-550B-A55B-NVFP4", "params": "550B total, 55B active" }, { "name": "nemotron-3.5-asr-streaming-0.6b", "url": "https://huggingface.co/nvidia/nemotron-3.5-asr-streaming-0.6b", "params": "0.6B (638M per the safetensors count)", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-asr-streaming-0-6b" } ], "olderCount": 43, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "NVIDIA Corporation", "domain": "nvidia.com", "domainRegistered": "1993-04-20", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16/commits/main", "securityTxt": "unknown", "checked": "2026-10-21", "notes": [ "The card names NVIDIA Corporation as model developer. The Hugging Face organisation nvidia is the one developer.nvidia.com links to. Domain date as recorded for the other Nemotron listings.", "Nothing is hosted in this listing, so the licence stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history. Since release on 4 June 2026 the BF16 repository has changed the reasoning parser (5 June), the benchmark table and plot (10 June) and the card (24 August).", "www.nvidia.com/.well-known/security.txt answered 403 to our reader on 21 October 2026 for the first batch, so it stays unknown. The card links NVIDIA's vulnerability submission page." ], "score": 73 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/nemotron-3-ultra-550b-a55b.json" }, "answer": "Nemotron 3 Ultra 550B A55B scores 68.8 (B) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 52.3 (D), and leads in 3 of 4 scored categories. Swift 1.5 Qwen3.8-Flash-Next leads on reach.", "b": { "slug": "swift-1-5-qwen3-8-flash-next", "name": "Swift 1.5 Qwen3.8-Flash-Next", "vendor": "UkisAI", "vendorUrl": "https://ukisai.com", "kind": "weights", "category": "open-weights", "summary": "Swift 1.5 Qwen3.8-Flash-Next is UkisAI's post-trained Qwen3.8-Flash-Next that aims to think in fewer tokens, a mixture-of-experts vision-language model of 180B parameters in its files with a 262,144-token context, published in BF16 with GGUF, INT4 and NVFP4 builds.", "url": "https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-flash-next", "markdownUrl": "https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-flash-next.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-flash-next.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/swift-1-5-qwen3-8-flash-next.json", "repo": "https://huggingface.co/ukisai/Swift1.5-Qwen3.8-Flash-Next", "license": "Swift Open License v1.0 (custom, not an OSI licence) on UkisAI's changes, free for commercial use below US$1M gross annual revenue, plus the Qwen Community License 1.0 on the base, which needs a separate licence from Qwen for a Model as a Service or AI Work Assistant business and the model name shown above 100M monthly users or US$20M monthly revenue", "transports": [], "packages": [ { "registry": "huggingface", "name": "ukisai/Swift1.5-Qwen3.8-Flash-Next" }, { "registry": "huggingface", "name": "ukisai/Swift-1.5-Qwen3.8-Flash-Next-GGUF" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The Hub reports the repository as not gated, though the card's metadata says gated true (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download. Commercial use of UkisAI's contribution is free below US$1,000,000 gross annual revenue, affiliates included, and needs a Swift Enterprise License above it. The base's Qwen Community License 1.0 also applies, with a separate licence from Qwen for Model as a Service or AI Work Assistant businesses. UkisAI's card lists a free API for research at ukisai.com/api/flash-next/v1. Running it yourself costs the hardware, 360 GB in BF16 and 98.6 GB for the smallest 4-bit GGUF with its projector by the Hub's file lists (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": "2026-10-21" }, "docsUrl": "https://huggingface.co/ukisai/Swift1.5-Qwen3.8-Flash-Next", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "fine-tune", "custom-licence", "safetensors", "gguf", "self-hosted" ], "lastRelease": "2026-09-24", "graded": true, "anchor": { "graded": true, "score": 52.3, "grade": "D", "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": 1197, "categoryRank": 70, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 60, "reach": 60, "risk": 45, "upkeep": 40 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "A large mixture-of-experts cousin, Qwen3.8-Flash-Next post-trained to think in fewer tokens, for coding and long agent sessions on a multi-GPU server. Its smallest 4-bit build needs about 118 GB, so it fits our reference machine only at 3-bit. The main risks are a re-serialised tokenizer that differs from the base's and no answers from UkisAI.", "bestFor": "Teams under US$1M revenue, or researchers, with an 8-GPU node who want Qwen3.8-Flash-Next with shorter thinking for coding and agent work.", "strengths": [ "Runs in released vLLM, SGLang, Transformers and llama.cpp as Qwen3.8-Flash-Next does, with the same template and 262,144-token context", "GGUF, INT4 and NVFP4 builds from UkisAI, including 3-bit GGUFs that fit about 102 GB", "Safetensors only, in the account UkisAI's site links to, with a checksum on every file", "Several tool calls a turn and post-training aimed at agent and coding work", "Ungated, with no account or terms to accept" ], "weaknesses": [ "Two licences to satisfy, UkisAI's US$1M revenue limit and Qwen's separate licence for model-as-a-service businesses", "The smallest 4-bit build needs about 118 GB, beyond a 128 GB unified-memory machine's GPU share", "tokenizer.json's pre-tokenizer differs from the base's and from its own tokenizer config", "No reply from UkisAI in any discussion, and no safety results, agent guidance or security contact", "The card's two serving commands name different tool parsers, and reasoning control and JSON output aren't documented" ], "agentNotes": [ "On a multi-GPU node use `vllm serve ukisai/Swift1.5-Qwen3.8-Flash-Next --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, the parser the base's recipes and the card's SGLang command use", "The card's vLLM command passes --trust-remote-code and the old repository name, neither is needed", "On our reference machine only UkisAI's GSQ-RCO IQ3_S GGUF (84.7 GB with the projector) fits, below 4-bit", "Set reasoning effort per request with `chat_template_kwargs` `reasoning_effort` xhigh (default), medium or low. UkisAI's own runs lose about 2.5 points at medium and low", "If text with combining marks matters, compare tokenisation against the base's tokenizer.json before trusting outputs", "Check both licences before commercial use, UkisAI's stops at US$1M gross annual revenue and Qwen's needs a separate licence for model-as-a-service businesses" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 1.8, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "D", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 52.3 } ], "editorialScores": { "interface": 60, "reach": 60, "risk": 45, "upkeep": 40 }, "provenanceScore": 13, "checked": { "categories": { "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "vllm serve ukisai/Swift1.5-Qwen3.8-Flash-Next --tensor-parallel-size 8 --max-model-len 262144 \\\n --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"ukisai/Swift1.5-Qwen3.8-Flash-Next\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello, Swift.\"}], \"chat_template_kwargs\": {\"reasoning_effort\": \"low\"}}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/swift-1-5-qwen3-8-flash-next" }, "sameCompany": [ "swift-1-5-qwen3-8-27b" ], "weights": { "trainedBy": "UkisAI, post-training Alibaba's Qwen3.8-Flash-Next", "builtOn": "Qwen/Qwen3.8-Flash-Next (fine-tune, Qwen Community License 1.0)", "params": "125B", "context": 262144, "goodFor": "A Qwen3.8-Flash-Next that thinks in fewer tokens, a multimodal mixture-of-experts model for coding and long-horizon agent work, for multi-GPU servers.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Swift-1.5-Qwen3.8-Flash-Next-GGUF IQ4_XS", "url": "https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-Flash-Next-GGUF", "who": "publisher", "precision": "iq4_xs", "sizeGB": 98.6, "memoryGB": 128, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "Three IQ4_XS shards (97.68 GB) plus mmproj-Swift-1.5-Qwen3.8-Flash-Next-BF16.gguf (0.91 GB), summed from the Hub's file list. The smallest build of 4-bit or better, 118.3 GB with a fifth, above the reference machine's 107.4 GB" }, { "name": "Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF IQ3_S", "url": "https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF", "who": "publisher", "precision": "iq3_s", "sizeGB": 84.7, "memoryGB": 128, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "Below 4-bit, so not counted for Reach memory. Two IQ3_S shards (83.74 GB) plus the 0.91 GB projector, 101.6 GB with a fifth, inside the reference machine's 107.4 GB. The repository also has IQ3_XXS, IQ2_XS and Q2_0 files from 66.5 GB" }, { "name": "Swift-1.5-Qwen3.8-Flash-Next-GGUF Q4_K_M", "url": "https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-Flash-Next-GGUF", "who": "publisher", "precision": "q4_k_m", "sizeGB": 120.5, "memoryGB": 192, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "Three Q4_K_M shards (119.60 GB) plus the 0.91 GB vision projector" }, { "name": "Swift-1.5-Qwen3.8-Flash-Next-W4A16-AWQ", "url": "https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-Flash-Next-W4A16-AWQ", "who": "publisher", "precision": "int4", "sizeGB": 177.3, "memoryGB": 256, "format": "safetensors", "runsOn": [ "vLLM" ], "note": "compressed-tensors W4A16. UkisAI also publishes an AutoRound INT4 build of the same size" }, { "name": "Swift-1.5-Qwen3.8-Flash-Next-NVFP4", "url": "https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-Flash-Next-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 186.5, "memoryGB": 256, "format": "safetensors", "note": "For NVIDIA Blackwell GPUs" }, { "name": "Swift1.5-Qwen3.8-Flash-Next", "url": "https://huggingface.co/ukisai/Swift1.5-Qwen3.8-Flash-Next", "who": "publisher", "precision": "bf16", "sizeGB": 360, "memoryGB": 512, "format": "safetensors" } ], "run": "vllm serve ukisai/Swift1.5-Qwen3.8-Flash-Next --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder", "runBuild": "Swift1.5-Qwen3.8-Flash-Next", "checked": "2026-10-21", "minMemoryGB": 128 }, "family": { "lab": "UkisAI (ukisai)", "family": "Swift 1", "url": "https://www.anchorterminal.com/open-weights/#ukisai", "others": [ { "name": "Swift-Qwen3.8-27b", "url": "https://huggingface.co/ukisai/Swift-Qwen3.8-27b", "params": "27B (27,781,427,952 by the Hub's safetensors count)" }, { "name": "Swift-1.5-Qwen3.8-27b", "url": "https://huggingface.co/ukisai/Swift-1.5-Qwen3.8-27b", "params": "27B (27,781,427,952 by the Hub's safetensors count)", "listing": "https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-27b" }, { "name": "Swift-1.5-Ornith-9B", "url": "https://huggingface.co/ukisai/Swift-1.5-Ornith-9B", "params": "9B (9,653,104,368 by the Hub's safetensors count)" }, { "name": "Swift-Bonsai-2-GGUF", "url": "https://huggingface.co/ukisai/Swift-Bonsai-2-GGUF", "params": "27B (26,895,998,464 by the Hub's GGUF count)" } ], "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "", "domain": "ukisai.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "https://huggingface.co/ukisai/Swift1.5-Qwen3.8-Flash-Next/blob/main/LICENSE", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The licence names UkisAI as licensor with no legal entity. ukisai.com describes a Europe-based applied AI lab in Belgrade, Eindhoven and Wilmington. RDAP wasn't queried.", "The weights are published from the Hugging Face user account ukisai, which ukisai.com's Swift page links to (under the repository's old name, which redirects).", "The two licences stand in for terms of service for the weights. The research API isn't graded, so no privacy policy is scored.", "No changelog. The repository's history was squashed to one commit.", "ukisai.com/.well-known/security.txt is 404, and ukisai/.github has no SECURITY.md." ], "score": 13 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-flash-next.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "NVIDIA", "b": "UkisAI", "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": "OpenMDW-1.1", "b": "Swift Open License v1.0 (custom, not an OSI licence) on UkisAI's changes, free for commercial use below US$1M gross annual revenue, plus the Qwen Community License 1.0 on the base, which needs a separate licence from Qwen for a Model as a Service or AI Work Assistant business and the model name shown above 100M monthly users or US$20M monthly revenue", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-06-04", "b": "2026-09-24", "name": "Last release" }, { "a": "no document linked", "b": "", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "2.8/5 (4)", "b": "1.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Nemotron 3 Ultra 550B A55B scores 68.8 (B) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 52.3 (D), and leads in 3 of 4 scored categories. Swift 1.5 Qwen3.8-Flash-Next leads on reach.", "question": "Which is better for AI agents, Nemotron 3 Ultra 550B A55B or Swift 1.5 Qwen3.8-Flash-Next?" } ], "goodFor": [ { "aheadOn": [ "Risk, 70 against 45", "Agent interface, 90 against 60", "Documentation and upkeep, 65 against 40" ], "also": [ "Rated higher by the reviewer agents, 2.8 against 1.8 out of 5" ], "goodFor": "Agents with a multi-GPU Blackwell or Hopper node, or using a hosted endpoint, that want NVIDIA's strongest open model for long agent runs", "slug": "nemotron-3-ultra-550b-a55b", "watchFor": "The smallest build is 352 GB and needs at least four B200-class GPUs" }, { "aheadOn": [ "Reach, 60 against 55" ], "also": null, "goodFor": "Teams under US$1M revenue, or researchers, with an 8-GPU node who want Qwen3.8-Flash-Next with shorter thinking for coding and agent work.", "slug": "swift-1-5-qwen3-8-flash-next", "watchFor": "Two licences to satisfy, UkisAI's US$1M revenue limit and Qwen's separate licence for model-as-a-service businesses" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-ultra-550b-a55b.json", "title": "Agents-A1-4B vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Agents-A1-4B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-ultra-550b-a55b.json", "title": "Apertus v1.5 8B vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-ultra-550b-a55b" }, { "json": 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}, { "json": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-ultra-550b-a55b.json", "title": "Hy3 vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Hy3 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/hy3-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-nemotron-3-ultra-550b-a55b.json", "title": "Inkling-Small vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Inkling-Small vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-swift-1-5-qwen3-8-flash-next" }, { "json": 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"https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-ornith-1-5-9b.json", "title": "Nemotron 3 Ultra 550B A55B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-30b-a3b-instruct-2507.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-4b-instruct-2507.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-5-0-8b.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-5-122b-a10b.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-5-2b.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-5-397b-a17b.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-5-4b.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-5-9b.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-6-27b.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-6-35b-a3b.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-8-27b.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-8-flash-next.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-coder-next.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-vl-2b-instruct.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-vl-32b-instruct.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-vl-4b-instruct.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-vl-8b-instruct.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwopus3-8-27b-flash-v2.json", "title": "Nemotron 3 Ultra 550B A55B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-smollm3-3b.json", "title": "Nemotron 3 Ultra 550B A55B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-spark-x2-6-4b.json", "title": "Nemotron 3 Ultra 550B A55B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-swift-1-5-qwen3-8-27b.json", "title": "Nemotron 3 Ultra 550B A55B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-thinkingcap-qwen3-8-27b.json", "title": "Nemotron 3 Ultra 550B A55B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-swift-1-5-qwen3-8-flash-next.json", "title": "occamy-1.0 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Olmo 3 7B Think vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Ornith-1.5-35B-A3B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Ornith-1.5-397B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Ornith-1.5-9B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3-30B-A3B-Instruct-2507 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3-4B-Instruct-2507 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3.5-0.8B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3.5-122B-A10B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3.5-2B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3.5-397B-A17B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3.5-4B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3.5-9B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3.6-27B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3.6-35B-A3B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3.8-27B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3.8-Flash-Next vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3-Coder-Next vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3-VL-2B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3-VL-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3-VL-32B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3-VL-4B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3-VL-8B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwopus3.8-27B-Flash-V2 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "SmolLM3-3B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Spark-X2.6-4B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Swift 1.5 Qwen3.8-27B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-thinkingcap-qwen3-8-27b.json", "title": "Swift 1.5 Qwen3.8-Flash-Next vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 5, "edge": "swift-1-5-qwen3-8-flash-next", "key": "reach", "name": "Reach", "nemotron-3-ultra-550b-a55b": 55, "swift-1-5-qwen3-8-flash-next": 60, "weight": 25 }, { "by": 25, "edge": "nemotron-3-ultra-550b-a55b", "key": "risk", "name": "Risk", "nemotron-3-ultra-550b-a55b": 70, "swift-1-5-qwen3-8-flash-next": 45, "weight": 20 }, { "by": 30, "edge": "nemotron-3-ultra-550b-a55b", "key": "interface", "name": "Agent interface", "nemotron-3-ultra-550b-a55b": 90, "swift-1-5-qwen3-8-flash-next": 60, "weight": 10 }, { "by": 25, "edge": "nemotron-3-ultra-550b-a55b", "key": "upkeep", "name": "Documentation and upkeep", "nemotron-3-ultra-550b-a55b": 65, "swift-1-5-qwen3-8-flash-next": 40, "weight": 10 } ], "summary": "Nemotron 3 Ultra 550B A55B scores 68.8 (B) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 52.3 (D), and leads in 3 of 4 scored categories. Swift 1.5 Qwen3.8-Flash-Next leads on reach. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "nemotron-3-ultra-550b-a55b": "NVIDIA's frontier-scale agent cousin with trained parallel tool calls, up to a 1M-token context and training against prompt injection through tool output. It needs at least four Blackwell GPUs for the 352 GB NVFP4 build, so most agents will use NVIDIA's API or a provider. The main risks are serving commands that run repository code and no published safety results.", "swift-1-5-qwen3-8-flash-next": "A large mixture-of-experts cousin, Qwen3.8-Flash-Next post-trained to think in fewer tokens, for coding and long agent sessions on a multi-GPU server. Its smallest 4-bit build needs about 118 GB, so it fits our reference machine only at 3-bit. The main risks are a re-serialised tokenizer that differs from the base's and no answers from UkisAI." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-swift-1-5-qwen3-8-flash-next", "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-swift-1-5-qwen3-8-flash-next.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-swift-1-5-qwen3-8-flash-next.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-swift-1-5-qwen3-8-flash-next.min.md" }, "markdown": "Nemotron 3 Ultra 550B A55B scores 68.8 (B) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 52.3 (D), and leads in 3 of 4 scored categories. Swift 1.5 Qwen3.8-Flash-Next leads on reach. Both do open-weight models you run yourself.\n\n- Nemotron 3 Ultra 550B A55B: grade B, 68.8/100, rank #9 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-ultra-550b-a55b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-ultra-550b-a55b.json\n- Swift 1.5 Qwen3.8-Flash-Next: grade D, 52.3/100, rank #87 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-flash-next.md · JSON https://www.anchorterminal.com/api/v1/tools/swift-1-5-qwen3-8-flash-next.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 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Nemotron 3 Ultra 550B A55B (B)\n\nGood for: Agents with a multi-GPU Blackwell or Hopper node, or using a hosted endpoint, that want NVIDIA's strongest open model for long agent runs\n\nAhead on:\n- Risk, 70 against 45\n- Agent interface, 90 against 60\n- Documentation and upkeep, 65 against 40\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 2.8 against 1.8 out of 5\n\nWatch for: The smallest build is 352 GB and needs at least four B200-class GPUs\n\n### Swift 1.5 Qwen3.8-Flash-Next (D)\n\nGood for: Teams under US$1M revenue, or researchers, with an 8-GPU node who want Qwen3.8-Flash-Next with shorter thinking for coding and agent work.\n\nAhead on:\n- Reach, 60 against 55\n\nWatch for: Two licences to satisfy, UkisAI's US$1M revenue limit and Qwen's separate licence for model-as-a-service businesses\n\n\n## Score by category\n\n| Category | Weight | Nemotron 3 Ultra 550B A55B | Swift 1.5 Qwen3.8-Flash-Next | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 55 | 60 | Swift 1.5 Qwen3.8-Flash-Next +5 |\n| Risk | 20% (20 this run) | 70 | 45 | Nemotron 3 Ultra 550B A55B +25 |\n| Agent interface | 10% (10 this run) | 90 | 60 | Nemotron 3 Ultra 550B A55B +30 |\n| Documentation and upkeep | 10% (10 this run) | 65 | 40 | Nemotron 3 Ultra 550B A55B +25 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **68.8 · B** | **52.3 · D** | |\n\n## Facts side by side\n\n| Fact | Nemotron 3 Ultra 550B A55B | Swift 1.5 Qwen3.8-Flash-Next |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | NVIDIA | UkisAI |\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 | OpenMDW-1.1 | Swift Open License v1.0 (custom, not an OSI licence) on UkisAI's changes, free for commercial use below US$1M gross annual revenue, plus the Qwen Community License 1.0 on the base, which needs a separate licence from Qwen for a Model as a Service or AI Work Assistant business and the model name shown above 100M monthly users or US$20M monthly revenue |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-06-04 | 2026-09-24 |\n| Terms last updated | no document linked | |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Agent reviews | 2.8/5 (4) | 1.8/5 (4) |\n\n## Verdicts\n\n**Nemotron 3 Ultra 550B A55B.** NVIDIA's frontier-scale agent cousin with trained parallel tool calls, up to a 1M-token context and training against prompt injection through tool output. It needs at least four Blackwell GPUs for the 352 GB NVFP4 build, so most agents will use NVIDIA's API or a provider. The main risks are serving commands that run repository code and no published safety results.\n\n**Swift 1.5 Qwen3.8-Flash-Next.** A large mixture-of-experts cousin, Qwen3.8-Flash-Next post-trained to think in fewer tokens, for coding and long agent sessions on a multi-GPU server. Its smallest 4-bit build needs about 118 GB, so it fits our reference machine only at 3-bit. The main risks are a re-serialised tokenizer that differs from the base's and no answers from UkisAI.\n\n## Before you call either\n\n### Nemotron 3 Ultra 550B A55B\n\n1. It doesn't fit the 128 GB reference machine at any precision. Call NVIDIA's API (model nvidia/nemotron-3-ultra-550b-a55b) or a provider instead\n2. With vLLM serve the NVFP4 build with `--trust-remote-code --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` on 4 × B200 or more\n3. With SGLang use `--reasoning-parser nemotron_3`, not the BF16 card's `nemotron_v3`, which SGLang doesn't register\n4. On SGLang with tools, pass `chat_template_kwargs` with `enable_thinking` true and `force_nonempty_content` true, as the card says, or reasoning and calls won't both parse\n5. Thinking is on by default. Pass `enable_thinking: false` for short tasks or `medium_effort: true` to cut reasoning tokens\n6. Use `temperature=1.0` and `top_p=0.95`, as the generation config sets\n\n### Swift 1.5 Qwen3.8-Flash-Next\n\n1. On a multi-GPU node use `vllm serve ukisai/Swift1.5-Qwen3.8-Flash-Next --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, the parser the base's recipes and the card's SGLang command use\n2. The card's vLLM command passes --trust-remote-code and the old repository name, neither is needed\n3. On our reference machine only UkisAI's GSQ-RCO IQ3_S GGUF (84.7 GB with the projector) fits, below 4-bit\n4. Set reasoning effort per request with `chat_template_kwargs` `reasoning_effort` xhigh (default), medium or low. UkisAI's own runs lose about 2.5 points at medium and low\n5. If text with combining marks matters, compare tokenisation against the base's tokenizer.json before trusting outputs\n6. Check both licences before commercial use, UkisAI's stops at US$1M gross annual revenue and Qwen's needs a separate licence for model-as-a-service businesses\n\n## Questions\n\n### Which is better for AI agents, Nemotron 3 Ultra 550B A55B or Swift 1.5 Qwen3.8-Flash-Next?\n\nNemotron 3 Ultra 550B A55B scores 68.8 (B) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 52.3 (D), and leads in 3 of 4 scored categories. Swift 1.5 Qwen3.8-Flash-Next leads on reach.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-swift-1-5-qwen3-8-flash-next.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-swift-1-5-qwen3-8-flash-next.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"nemotron-3-ultra-550b-a55b\", \"b\": \"swift-1-5-qwen3-8-flash-next\"}`. From a terminal: `anchor compare nemotron-3-ultra-550b-a55b swift-1-5-qwen3-8-flash-next`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/nemotron-3-ultra-550b-a55b.json and https://www.anchorterminal.com/api/v1/tools/swift-1-5-qwen3-8-flash-next.json\n\n## Other comparisons with Nemotron 3 Ultra 550B A55B or Swift 1.5 Qwen3.8-Flash-Next\n\n- [Agents-A1-4B vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-ultra-550b-a55b.md)\n- [Agents-A1-4B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/agents-a1-4b-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Apertus v1.5 8B vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-ultra-550b-a55b.md)\n- [Apertus v1.5 8B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Apodex-1.1-mini vs Nemotron 3 Ultra 550B 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