{ "data": { "a": { "slug": "ling-3-1-flash-1019", "name": "Ling-3.1-flash-1019", "vendor": "inclusionAI (Ant Group)", "vendorUrl": "https://www.inclusion-ai.org", "kind": "weights", "category": "open-weights", "summary": "Ling-3.1-flash-1019 is inclusionAI's open-weight hybrid reasoning mixture of experts with 560B parameters and 25B active, a post-trained update of Ling-3.1-flash, with switchable thinking, tool calling and a 262,144-token native context, published in FP8 under MIT.", "url": "https://www.anchorterminal.com/tools/ling-3-1-flash-1019", "markdownUrl": "https://www.anchorterminal.com/tools/ling-3-1-flash-1019.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ling-3-1-flash-1019.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ling-3-1-flash-1019.json", "dns": "ling-3-1-flash-1019.grade.anchorterminal.com", "repo": "https://huggingface.co/inclusionAI/Ling-3.1-flash-1019", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "inclusionAI/Ling-3.1-flash-1019" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under MIT (named in the card's metadata; the repository holds no LICENSE file). Running it yourself costs the hardware, 573.8 GB of FP8 files, which the card serves on 8 H200 or 4 GB300 GPUs (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/inclusionAI/Ling-3.1-flash-1019", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "hybrid-attention", "reasoning", "tool-calling", "long-context", "mit", "safetensors", "fp8", "self-hosted" ], "lastRelease": "2026-11-04", "graded": true, "anchor": { "graded": true, "score": 54.8, "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": 94, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 60, "reach": 60, "risk": 45, "upkeep": 56 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "inclusionAI's newest flagship, a 560B hybrid mixture of experts under plain MIT that vLLM, SGLang, Transformers and llama.cpp run in released versions. It ships only as 574 GB of FP8, so it needs a multi-GPU server. The card overstates the context, documents little about tool calls, and has no safety results.", "bestFor": "Teams with a multi-GPU server that want an MIT-licensed flagship for long agent runs and coding", "strengths": [ "Plain MIT, ungated, with a hash for every shard", "Released vLLM, SGLang, Transformers and llama.cpp all have the architecture", "A 262,144-token native context, and 1M with YaRN per SGLang's cookbook", "A training-content summary that names source categories, the size band and a collection date", "Updated a week before this check" ], "weaknesses": [ "574 GB in FP8 with no smaller build from inclusionAI, so no single machine under 690 GB of GPU memory runs it", "The card says 1M tokens of context where the files give 262,144", "The tool-call format is only in the template", "No safety results, no agent guidance and no security contact", "No inference provider mapped for this release, only for the earlier one", "Neither question on the repository has a reply" ], "agentNotes": [ "Serve it with SGLang 0.5.23 or later on 8 H200 or 4 GB300 GPUs, per the card, and use this repository's name as the model path, not the earlier one the card's command shows", "With vLLM pass `--trust-remote-code`, `--tool-call-parser ling3` and `--reasoning-parser ling3`", "Set `chat_template_kwargs` `enable_thinking` false for direct answers. Reasoning comes back in `reasoning_content` per SGLang's cookbook", "Plan on 262,144 tokens of context. The 1M figure needs the YaRN override in SGLang's cookbook", "Use temperature 1.0, top_p 0.95 and top_k 20, per the card", "Validate JSON output yourself, since structured output isn't documented" ], "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": 54.8 } ], "editorialScores": { "interface": 60, "reach": 60, "risk": 45, "upkeep": 56 }, "provenanceScore": 61, "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": "# with SGLang 0.5.23 or later, on 8 H200 GPUs:\npython3 -m sglang.launch_server --model-path inclusionAI/Ling-3.1-flash-1019 \\\n --tp 8 --ep 8 --speculative-algorithm NEXTN --host 0.0.0.0 --port 30000\n# or with vLLM 0.33.0, per the card's serve command:\nvllm serve inclusionAI/Ling-3.1-flash-1019 --trust-remote-code --tensor-parallel-size 8 \\\n --enable-prefix-caching --enable-expert-parallel --mamba-cache-mode align \\\n --enable-auto-tool-choice --tool-call-parser ling3 --reasoning-parser ling3\n# or, with llama.cpp and bartowski's GGUF, a third party's conversion:\nllama-server -hf bartowski/Ling-3.1-flash-1019-GGUF:Q4_K_M --jinja", "http": "curl http://localhost:30000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\": \"inclusionAI/Ling-3.1-flash-1019\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"chat_template_kwargs\": {\"enable_thinking\": false}}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/ling-3-1-flash-1019" }, "sameCompany": [ "ming-3-0-asr-tiny", "ming-3-0-audio-flash", "ming-3-0-audio-tiny", "ming-3-0-tts-tiny", "lbs-27b", "ling-3-0-tiny" ], "weights": { "trainedBy": "inclusionAI (Ant Group)", "builtOn": "inclusionAI/Ling-3.1-flash, post-trained further (named on the card; the metadata names no base model)", "params": "560B (25B active)", "context": 262144, "goodFor": "Large hybrid reasoning model for agents, office work and software engineering on a multi-GPU server, with thinking switchable per request. It needs about 690 GB of GPU memory in FP8.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "bartowski Ling-3.1-flash-1019-GGUF (Q4_K_M)", "url": "https://huggingface.co/bartowski/Ling-3.1-flash-1019-GGUF", "who": "bartowski", "precision": "q4_k_m", "sizeGB": 357.97, "memoryGB": 512, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From bartowski, a third party's conversion and not inclusionAI's. 357.97 GB of Q4_K_M weights, from the Hub's model information on 10 November 2026. Reach doesn't count it" }, { "name": "Ling-3.1-flash-1019", "url": "https://huggingface.co/inclusionAI/Ling-3.1-flash-1019", "who": "publisher", "precision": "fp8", "sizeGB": 573.78, "memoryGB": 1024, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ], "note": "The release itself, FP8 E4M3 weights in 128×128 blocks. inclusionAI publishes no smaller build" } ], "run": "python3 -m sglang.launch_server --model-path inclusionAI/Ling-3.1-flash-1019 --tp 8 --ep 8 --speculative-algorithm NEXTN", "runBuild": "Ling-3.1-flash-1019", "checked": "2026-11-10", "minMemoryGB": 512 }, "family": { "lab": "inclusionAI (Ant Group)", "family": "Ling 3", "url": "https://www.anchorterminal.com/open-weights/#inclusionai", "others": [ { "name": "Ling-3.0-flash", "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash", "params": "124B total, 5.1B active" }, { "name": "Ling-3.0-flash-base", "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-base", "params": "124B total, 5.1B active" }, { "name": "Ling-3.0-flash-base-30T", "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-base-30T", "params": "124B total, 5.1B active" }, { "name": "Ling-3.0-flash-base-midtrain", "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-base-midtrain", "params": "124B total, 5.1B active" }, { "name": "Ling-3.0-flash-Fin", "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-Fin", "params": "124B total, 5.1B active" }, { "name": "Ling-3.0-flash-VL", "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-VL", "params": "124B total, 5.5B active" }, { "name": "Ling-3.0-flash-dspark", "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-dspark", "params": "1.36B (draft)" }, { "name": "Ling-3.0-flash-singprobe", "url": "https://huggingface.co/inclusionAI/Ling-3.0-flash-singprobe", "params": "5.18M (probe)" }, { "name": "Ling-3.0-tiny", "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny", "params": "7.9B total, 1.3B active", "listing": "https://www.anchorterminal.com/tools/ling-3-0-tiny" }, { "name": "Ling-3.0-tiny-base", "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-base", "params": "7.9B total, 1.3B active" }, { "name": "Ling-3.0-tiny-base-30T", "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-base-30T", "params": "7.9B total, 1.3B active" }, { "name": "Ling-3.0-tiny-base-midtrain", "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-base-midtrain", "params": "7.9B total, 1.3B active" }, { "name": "Ling-3.0-tiny-singprobe", "url": "https://huggingface.co/inclusionAI/Ling-3.0-tiny-singprobe", "params": "3.22M (probe)" }, { "name": "Ling-3.1-flash", "url": "https://huggingface.co/inclusionAI/Ling-3.1-flash", "params": "560B total, 25B active" }, { "name": "Ling-3.1-flash-base", "url": "https://huggingface.co/inclusionAI/Ling-3.1-flash-base", "params": "560B total, 25B active" } ], "olderCount": 54 }, "area": "models", "provenance": { "legalEntity": "Ant Intelligence (Hang Zhou) Technology Co., Ltd (named as provider in the training-content summary); inclusionAI, Ant Group in the site footer of inclusion-ai.org", "domain": "inclusion-ai.org", "domainRegistered": "2025-04-03", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/inclusionAI/Ling-3.1-flash-1019/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "inclusion-ai.org links to huggingface.co/inclusionAI and github.com/inclusionAI. The domain registration date is carried from the RDAP check made for the Ming 3.0 listings on 21 October 2026.", "Nothing is hosted by the publisher, so the MIT licence stands in for terms of service and no privacy policy is scored.", "The changelog is the Hugging Face commit history, 8 commits from 27 October to 4 November 2026, the last three card edits.", "www.inclusion-ai.org/.well-known/security.txt answered 404 on 10 November 2026." ], "score": 61 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/ling-3-1-flash-1019.json" }, "answer": "Nemotron 3 Super 120B A12B scores 66.7 (B) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in every scored category.", "b": { "slug": "nemotron-3-super-120b-a12b", "name": "Nemotron 3 Super 120B A12B", "vendor": "NVIDIA", "vendorUrl": "https://developer.nvidia.com/nemotron", "kind": "weights", "category": "open-weights", "summary": "Nemotron 3 Super is NVIDIA's open-weight hybrid Mamba-2 and mixture-of-experts reasoning model for agents, 120B parameters with 12B active, published on Hugging Face in BF16, FP8 and NVFP4 under the NVIDIA Nemotron Open Model License.", "url": "https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b", "markdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nemotron-3-super-120b-a12b.json", "dns": "nemotron-3-super-120b-a12b.grade.anchorterminal.com", "repo": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16", "license": "NVIDIA Nemotron Open Model License", "transports": [], "packages": [ { "registry": "huggingface", "name": "nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16" }, { "registry": "huggingface", "name": "nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-FP8" }, { "registry": "huggingface", "name": "nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4" }, { "registry": "ollama", "name": "nemotron-3-super" } ], "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).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the NVIDIA Nemotron Open Model License. Running it yourself costs the hardware, 247.2 GB of BF16 files (8× H100-80GB minimum per the card), 80.3 GB for NVIDIA's NVFP4 build or 87 GB for Ollama's default build (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": "2026-11-01" }, "docsUrl": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "mamba", "hybrid", "reasoning", "tool-calling", "long-context", "safetensors", "fp8", "nvfp4", "self-hosted" ], "lastRelease": "2026-03-11", "graded": true, "anchor": { "graded": true, "score": 66.7, "grade": "B", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 11, "methodology": "0.7", "run": "2026-10-01", "scores": { "capability": 54, "interface": 90, "reach": 75, "risk": 65, "upkeep": 70 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-11-01" }, "negative": 0, "verdict": "A long-context agent cousin with trained parallel tool calls, a reasoning switch and a full account of its training data, under a free commercial licence. It needs about 100 GB of GPU memory, a 128 GB unified machine or Blackwell hardware for NVIDIA's NVFP4. The main risks are serving commands that run repository code and no published safety results.", "bestFor": "Agents on a 128 GB unified machine or Blackwell GPUs that want a long-context, tool-calling reasoning model with documented training data", "strengths": [ "Runs in released Transformers, vLLM, SGLang, llama.cpp and Ollama, with NVIDIA's own FP8 and NVFP4 builds", "Training data listed by dataset with token counts and cut-offs, much of it published", "Tool calls documented with a named parser, several per turn, and thinking off, low effort or budgeted per request", "Free commercial licence with no threshold or use policy, no gate, and NVIDIA's own API serves it", "Context of 262,144 tokens by default and up to 1M per the card" ], "weaknesses": [ "The card's serving commands pass --trust-remote-code", "No safety evaluation results published", "No NVIDIA reply in any of the repository's nine discussions", "Needs about 96 to 104 GB even at 4-bit, and NVIDIA's NVFP4 build needs Blackwell", "Structured output claimed but not documented" ], "agentNotes": [ "On the reference machine start it with `ollama run nemotron-3-super`, the 87 GB q4_K_M build, the only one that fits (about 104 GB with the margin)", "Raise the Strix Halo GPU memory limit first with `amd-ttm --set 100`, since the default of about 62.8 GiB is far below what it needs", "With vLLM serve it with `--reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` (SGLang uses `--reasoning-parser nemotron_3`)", "Use `temperature=1.0` and `top_p=0.95` for every task, as the card says", "Thinking is on by default. Pass `enable_thinking: false` or `low_effort: true` in `chat_template_kwargs` for short tasks", "For coding agents pass `force_nonempty_content: true` in `chat_template_kwargs`, so a cut-off reasoning trace still returns content", "Raise context past 262,144 only with the card's flags (`VLLM_ALLOW_LONG_MAX_MODEL_LEN=1`, `--max-model-len 1048576`)" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "B", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 66.7 } ], "editorialScores": { "capability": 54, "interface": 90, "reach": 75, "risk": 65, "upkeep": 70 }, "provenanceScore": 76, "checked": { "categories": { "capability": "2026-11-01", "interface": "2026-11-01", "reach": "2026-11-01", "risk": "2026-11-01", "upkeep": "2026-11-01" }, "last": "2026-11-01", "lastMoved": "2026-11-01", "oldest": "2026-11-01", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run nemotron-3-super\n# or, with the card's vLLM command (abridged):\npip install vllm==0.18.1\nvllm serve nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16 --served-model-name nvidia/nemotron-3-super \\\n --tensor-parallel-size 8 --max-model-len 262144 --kv-cache-dtype fp8 --mamba-ssm-cache-dtype float32 \\\n --trust-remote-code --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder\n# or, with llama.cpp and LM Studio's GGUF:\nllama-server -hf lmstudio-community/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF:Q4_K_M --jinja", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"nvidia/nemotron-3-super\",\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-super-120b-a12b" }, "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-nano-omni-30b-a3b-reasoning", "nemotron-3-ultra-550b-a55b", "nemotron-3-5-lightning-30b-a3b" ], "weights": { "trainedBy": "NVIDIA", "builtOn": "nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16", "params": "120B", "active": "12B", "context": 1048576, "goodFor": "Hybrid Mamba and mixture-of-experts reasoning model for agents, text only, with tool calling, a low-effort mode and a reasoning budget.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "unsloth NVIDIA-Nemotron-3-Super-120B-A12B-GGUF (UD-Q4_K_S)", "url": "https://huggingface.co/unsloth/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF", "who": "unsloth", "precision": "q4_k_s", "sizeGB": 79.02, "memoryGB": 96, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From Unsloth, a third party's conversion and not NVIDIA's. 79.02 GB of UD-Q4_K_S weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it" }, { "name": "NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 80.3, "memoryGB": 128, "format": "safetensors", "note": "Pre-trained in NVFP4; runs on one B200 or DGX Spark per its card. NVFP4 needs NVIDIA Blackwell" }, { "name": "ollama nemotron-3-super:latest", "url": "https://ollama.com/library/nemotron-3-super:latest", "who": "ollama", "precision": "q4_k_m", "sizeGB": 87, "memoryGB": 128, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Also the smallest Ollama build. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant" }, { "name": "lmstudio-community NVIDIA-Nemotron-3-Super-120B-A12B-GGUF (Q4_K_M)", "url": "https://huggingface.co/lmstudio-community/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF", "who": "lmstudio", "precision": "q4_k_m", "sizeGB": 86.05, "memoryGB": 128, "format": "gguf", "runsOn": [ "LM Studio", "llama.cpp" ], "note": "From lmstudio-community, LM Studio's organisation on the Hub. 86.05 GB of Q4_K_M weights, from the Hub's model information on 10 November 2026. LM Studio's own catalogue page for this model wasn't checked. Recorded after the last grading, so Reach doesn't count it yet" }, { "name": "NVIDIA-Nemotron-3-Super-120B-A12B-FP8", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 128.4, "memoryGB": 192, "format": "safetensors" }, { "name": "lmstudio-community NVIDIA-Nemotron-3-Super-120B-A12B-GGUF (Q6_K)", "url": "https://huggingface.co/lmstudio-community/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF", "who": "lmstudio", "precision": "q6_k", "sizeGB": 112.92, "memoryGB": 192, "format": "gguf", "runsOn": [ "LM Studio", "llama.cpp" ], "note": "From lmstudio-community, LM Studio's organisation on the Hub. 112.92 GB of Q6_K weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet" }, { "name": "lmstudio-community NVIDIA-Nemotron-3-Super-120B-A12B-GGUF (Q8_0)", "url": "https://huggingface.co/lmstudio-community/NVIDIA-Nemotron-3-Super-120B-A12B-GGUF", "who": "lmstudio", "precision": "q8_0", "sizeGB": 128.47, "memoryGB": 192, "format": "gguf", "runsOn": [ "LM Studio", "llama.cpp" ], "note": "From lmstudio-community, LM Studio's organisation on the Hub. 128.47 GB of Q8_0 weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet" }, { "name": "NVIDIA-Nemotron-3-Super-120B-A12B-BF16", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16", "who": "publisher", "precision": "bf16", "sizeGB": 247.2, "memoryGB": 512, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "vllm serve nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4 --served-model-name nvidia/nemotron-3-super --max-model-len 262144 --kv-cache-dtype fp8 --mamba-ssm-cache-dtype float32 --trust-remote-code --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder", "runBuild": "NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4", "checked": "2026-10-21", "minMemoryGB": 96 }, "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 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", "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", "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 3.5 Bolt E4B 4GB", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB", "params": "8B total, 4B effective", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-bolt-e4b-4gb" }, { "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" }, { "name": "Nemotron 3.5 Parse", "url": "https://huggingface.co/nvidia/Nemotron-3.5-Parse", "params": "0.631B (630,800,496 trainable parameters per the card)", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-parse" }, { "name": "Nemotron 3.5 Transcribe 30B-A4B", "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B", "params": "30B total, about 4.3B active (33,697,781,029 by the Hub's safetensors count)", "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-transcribe-30b-a4b" } ], "olderCount": 47, "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-Super-120B-A12B-BF16/commits/main", "securityTxt": "unknown", "checked": "2026-10-21", "notes": [ "The card names NVIDIA Corporation as model developer, and the licence's notice reads Licensed by NVIDIA Corporation. The Hugging Face organisation nvidia is verified and linked from developer.nvidia.com. RDAP gives 20 April 1993 for nvidia.com.", "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 10 March 2026 only the card has changed, with some messages that say what (Update BF16 configs, Update KV Cache/Mamba dtype, Update for MTPv2) and several that read Update README.md.", "www.nvidia.com/.well-known/security.txt answered 403 to our reader on 21 October 2026, so it is counted as unchecked rather than absent. The card links NVIDIA's vulnerability submission page." ], "score": 76 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b.json", "live": { "slug": "nemotron-3-super-120b-a12b", "securityTxt": { "url": "https://nvidia.com/.well-known/security.txt", "state": "unknown", "checkedAt": "2026-11-10T16:08:00.271598775Z" }, "domain": { "domain": "nvidia.com", "registered": "1993-04-20", "source": "https://rdap.verisign.com/com/v1/domain/nvidia.com", "checkedAt": "2026-11-08T15:03:11.895084282Z" }, "pages": [ { "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-11-10T16:23:20.620593124Z", "changedAt": "2026-11-10T16:23:20.620593124Z", "fingerprint": "b8fce0f674d8" } ], "updatedAt": "2026-11-10T16:23:20.620593124Z" } }, "edges": [ { "by": 30, "category": "interface", "leads": "nemotron-3-super-120b-a12b", "name": "Agent interface", "reasons": { "a": "Chat template 20, chat_template.jinja in the repository (6,042 characters), which handles tools, the thinking switch and preserved reasoning. Tool-calling format 10, only the template says how tools are declared (JSON in \u003ctools\u003e) and how calls come back (\u003ctool_call\u003ename with \u003carg_key\u003e and \u003carg_value\u003e pairs). Trained tool calling 20, the template tells the model it may call one or more functions and loops over every entry in an assistant message's tool_calls, and the card reports gains on agentic benchmarks, AA-Briefcase and AutomationBench-AA among them. Structured output 0, the card doesn't document JSON or JSON Schema output. Reasoning control 0, the card documents turning thinking on or off per request with chat_template_kwargs enable_thinking, and names the ling3 reasoning parser, but doesn't say where the reasoning text comes back. Context 10, 262,144 tokens natively in config.json (max_position_embeddings 262144, rope_scaling null) and per SGLang's cookbook. 20 + 10 + 20 + 0 + 0 + 10 = 60.", "b": "The chat template earns 20, a Jinja file in the repository. The tool-calling format earns 25, since NVIDIA documents the format, names the qwen3_coder parser for three runtimes and shows a tool call in its cookbook. Trained tool calling earns 20, with tool-use reinforcement learning and several calls per turn in the template. Structured output earns 5, claimed from training with no instructions. Reasoning control earns 10, thinking off, low effort or a budget per request with the trace returned as reasoning_content. Context earns 10 for 262,144 tokens in the config and up to 1M per the card." }, "scores": { "a": 60, "b": 90 } }, { "by": 20, "category": "risk", "leads": "nemotron-3-super-120b-a12b", "name": "Risk", "reasons": { "a": "File safety 15, safetensors only, no .bin, .pt or .pth files (25), less 10 because the card's documented vLLM command passes --trust-remote-code, which runs the repository's configuration_bailing_moe_v3.py, since vLLM 0.33.0 registers no bailing_hybrid config of its own. Provenance 15, files in the inclusionAI organisation that inclusion-ai.org links to, per the Ling-3.0-tiny listing (10). Safety evaluation 0, no safety results for the model on its card or in the training-content summary. Agent guidance 0, the card has nothing on prompt injection, refusals or safeguards, and inclusionAI publishes no guardrail probe for this model as it did for Ling 3.0. Licence risk 15, MIT with no use policy, no field-of-use limits, no threshold and no clause letting inclusionAI change or revoke it. Security programme 0, www.inclusion-ai.org answers 404 for security.txt on 10 November 2026, and the Ling-3.0-tiny listing found no SECURITY.md or disclosure policy in the inclusionAI .github repository. 15 + 15 + 0 + 0 + 15 + 0 = 45.", "b": "File safety earns 15, safetensors only but with 10 off because the documented serving commands pass trust-remote-code and the repository ships model and parser code. Provenance earns 15, files in NVIDIA's linked organisation with a hash for every file. Safety evaluation earns 0, since NVIDIA says it evaluated content safety but publishes no results. Agent guidance earns 10, a subcard that names prompt injection and says what safeguards to add, without an instruction hierarchy or concrete mitigations. Licence risk keeps all 15. The security programme earns 10 for the vulnerability submission route on the card and NVIDIA's public bulletins." }, "scores": { "a": 45, "b": 65 } }, { "by": 15, "category": "reach", "leads": "nemotron-3-super-120b-a12b", "name": "Reach", "reasons": { "a": "Memory 0, inclusionAI's only build is the FP8 release at 573.78 GB, × 1.2 = 688.5 GB, above 256 GB, and no runtime's library has a build. Runtimes 25, Released model code in Transformers 5.22.2 (bailing_hybrid), vLLM 0.33.0 (BailingMoeV3ForCausalLM, with ling3 tool and reasoning parsers), SGLang 0.5.23 (bailing_moe_v3.py, its bailing_hybrid config and MTP module) and llama.cpp b12242 (bailingmoe3), and Ollama, credited through its pinned llama.cpp as on the Ling-3.0-tiny listing: v0.40.16 sets LLAMA_CPP_VERSION to b12177. Builds 10, the release is FP8, the one quantised precision inclusionAI publishes for it. Terms 15, MIT in the card's metadata, with no use policy or other terms added on the card or in the repository. Download 10, the Hub API's model info gives gated false on 10 November 2026, so the files download with no account and no terms to accept. Hosted 0, the Hub API's provider mapping is empty for this repository on 10 November 2026. 0 + 25 + 10 + 15 + 10 + 0 = 60.", "b": "Memory earns 10, because the smallest 4-bit build, NVIDIA's 80.3 GB NVFP4, needs about 96 GB with the fifth added, which fits the reference machine but not 64 GB, and Ollama's 87 GB build lands in the same band. Runtimes earn the full 25 from released Transformers, vLLM, SGLang, llama.cpp and Ollama. Builds earn 15 for NVIDIA's own FP8 and NVFP4. Terms earn 10, since the Nemotron licence is custom even though it's free for commercial use with no threshold or use policy. Download earns 10, with no gate or account. Hosted earns 5, since NVIDIA's own API serves it." }, "scores": { "a": 60, "b": 75 } }, { "by": 14, "category": "upkeep", "leads": "nemotron-3-super-120b-a12b", "name": "Documentation and upkeep", "reasons": { "a": "Model card 11, Intended use (complex reasoning, multi-step tasks and agentic workflows), architecture, parameters (560B total, 25B active) and context (1M claimed) are on the card. Training data 15, the training-content summary the card links names the source categories (Common Crawl and other public datasets, commercially licensed data, the Ant Spider crawler, synthetic data), the size band (over 10 trillion text tokens), data collected up to about July 2026 and the filtering, without listing datasets with sizes. Last change 20, the newest commit, on 4 November 2026, edited the card, six days before this check, and the files went up on 27 October. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (both opened on 27 October 2026) has a reply from an inclusionAI member. Version policy 5, the update went into a repository of its own and Ling-3.1-flash and Ling 3.0 stay in theirs. File health 5, the card states a 1M-token context while config.json gives 262,144 with no rope scaling, and its serve commands name the earlier repository as the model path. 11 + 15 + 20 + 0 + 5 + 5 = 56.", "b": "The model card earns 20, all seven items with limitations in the linked subcard. Training data earns 20, datasets listed with sources and token counts and the corpora published. Last change earns 20, counting NVIDIA's own inference code for the model, as on Nemotron 3 Ultra and Nano. The repositories last changed with a card edit on 25 August 2026, but NVIDIA's TensorRT-LLM, which the card's serving commands use, changed its Nemotron-H model code on 14 October, 18 days ago. Responsiveness earns 0, with no NVIDIA reply in any of the nine discussions, re-counted on 1 November. Version policy earns 5 as a first release. File health earns 5, since a generation report waits without a reply and one card link is dead. 20+20+20+0+5+5 = 70." }, "scores": { "a": 56, "b": 70 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "inclusionAI (Ant Group)", "b": "NVIDIA", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "MIT", "b": "NVIDIA Nemotron Open Model License", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-11-04", "b": "2026-03-11", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "none", "b": "2.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Nemotron 3 Super 120B A12B scores 66.7 (B) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in every scored category.", "question": "Which is better for AI agents, Ling-3.1-flash-1019 or Nemotron 3 Super 120B A12B?" } ], "goodFor": [ { "aheadOn": null, "also": null, "goodFor": "Teams with a multi-GPU server that want an MIT-licensed flagship for long agent runs and coding", "slug": "ling-3-1-flash-1019", "watchFor": "574 GB in FP8 with no smaller build from inclusionAI, so no single machine under 690 GB of GPU memory runs it" }, { "aheadOn": [ "Reach, 75 against 60", "Risk, 65 against 45", "Agent interface, 90 against 60", "Documentation and upkeep, 70 against 56" ], "also": null, "goodFor": "Agents on a 128 GB unified machine or Blackwell GPUs that want a long-context, tool-calling reasoning model with documented training data", "slug": "nemotron-3-super-120b-a12b", "watchFor": "The card's serving commands pass --trust-remote-code" } ], "history": { "ling-3-1-flash-1019": null, "nemotron-3-super-120b-a12b": [ { "causes": [ "correction" ], "date": "2026-11-01", "delta": 0.5, "from": 66.2, "fromGrade": "B", "to": 66.7, "toGrade": "B" } ] }, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ling-3-1-flash-1019.json", "title": "Agents-A1-4B vs Ling-3.1-flash-1019", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ling-3-1-flash-1019" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-super-120b-a12b.json", "title": "Agents-A1-4B vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ling-3-1-flash-1019.json", "title": "Apertus v1.5 8B vs Ling-3.1-flash-1019", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ling-3-1-flash-1019" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-super-120b-a12b.json", "title": "Apertus v1.5 8B vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ling-3-1-flash-1019.json", "title": "Apodex-1.1-mini vs Ling-3.1-flash-1019", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ling-3-1-flash-1019" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-super-120b-a12b.json", "title": "Apodex-1.1-mini vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-ling-3-1-flash-1019.json", "title": "Arete-27B vs Ling-3.1-flash-1019", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-ling-3-1-flash-1019" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-nemotron-3-super-120b-a12b.json", "title": "Arete-27B vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ling-3-1-flash-1019.json", "title": "BigBang-v1 vs Ling-3.1-flash-1019", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ling-3-1-flash-1019" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-super-120b-a12b.json", "title": "BigBang-v1 vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-ling-3-1-flash-1019.json", "title": "Carbon-X vs Ling-3.1-flash-1019", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-ling-3-1-flash-1019" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-nemotron-3-super-120b-a12b.json", "title": "Carbon-X vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-ling-3-1-flash-1019.json", "title": "Cola2 vs Ling-3.1-flash-1019", "url": "https://www.anchorterminal.com/compare/cola2-vs-ling-3-1-flash-1019" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-nemotron-3-super-120b-a12b.json", "title": "Cola2 vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/cola2-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ling-3-1-flash-1019.json", "title": "Cosmos-Reason2-2B vs Ling-3.1-flash-1019", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ling-3-1-flash-1019" }, { "json": 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"https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwopus3-8-27b-flash-v2.json", "title": "Nemotron 3 Super 120B A12B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-recursive-flint.json", "title": "Nemotron 3 Super 120B A12B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ripple-65b-preview.json", "title": "Nemotron 3 Super 120B A12B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-sensenova-loop-9b-preview.json", "title": "Nemotron 3 Super 120B A12B vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-smollm3-3b.json", "title": "Nemotron 3 Super 120B A12B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-spark-x2-6-4b.json", "title": "Nemotron 3 Super 120B A12B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-step-3-7-flash.json", "title": "Nemotron 3 Super 120B A12B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-swift-1-5-qwen3-8-27b.json", "title": "Nemotron 3 Super 120B A12B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Nemotron 3 Super 120B A12B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-thinkingcap-qwen3-8-27b.json", "title": "Nemotron 3 Super 120B A12B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-unlimited-ocr-2.json", "title": "Nemotron 3 Super 120B A12B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-wevisone-vl-2b.json", "title": "Nemotron 3 Super 120B A12B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-wevisone-vl-4b.json", "title": "Nemotron 3 Super 120B A12B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-zaya1-8b.json", "title": "Nemotron 3 Super 120B A12B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 15, "edge": "nemotron-3-super-120b-a12b", "key": "reach", "ling-3-1-flash-1019": 60, "name": "Reach", "nemotron-3-super-120b-a12b": 75, "weight": 25 }, { "by": 20, "edge": "nemotron-3-super-120b-a12b", "key": "risk", "ling-3-1-flash-1019": 45, "name": "Risk", "nemotron-3-super-120b-a12b": 65, "weight": 20 }, { "by": 30, "edge": "nemotron-3-super-120b-a12b", "key": "interface", "ling-3-1-flash-1019": 60, "name": "Agent interface", "nemotron-3-super-120b-a12b": 90, "weight": 10 }, { "by": 14, "edge": "nemotron-3-super-120b-a12b", "key": "upkeep", "ling-3-1-flash-1019": 56, "name": "Documentation and upkeep", "nemotron-3-super-120b-a12b": 70, "weight": 10 } ], "summary": "Nemotron 3 Super 120B A12B scores 66.7 (B) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in every scored category. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "ling-3-1-flash-1019": "inclusionAI's newest flagship, a 560B hybrid mixture of experts under plain MIT that vLLM, SGLang, Transformers and llama.cpp run in released versions. It ships only as 574 GB of FP8, so it needs a multi-GPU server. The card overstates the context, documents little about tool calls, and has no safety results.", "nemotron-3-super-120b-a12b": "A long-context agent cousin with trained parallel tool calls, a reasoning switch and a full account of its training data, under a free commercial licence. It needs about 100 GB of GPU memory, a 128 GB unified machine or Blackwell hardware for NVIDIA's NVFP4. The main risks are serving commands that run repository code and no published safety results." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-nemotron-3-super-120b-a12b", "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-nemotron-3-super-120b-a12b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-nemotron-3-super-120b-a12b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-nemotron-3-super-120b-a12b.min.md" }, "markdown": "Nemotron 3 Super 120B A12B scores 66.7 (B) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in every scored category. Both do open-weight models you run yourself.\n\n- Ling-3.1-flash-1019: grade C, 54.8/100, rank #126 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/ling-3-1-flash-1019.md · JSON https://www.anchorterminal.com/api/v1/tools/ling-3-1-flash-1019.json\n- Nemotron 3 Super 120B A12B: grade B, 66.7/100, rank #15 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-super-120b-a12b.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Ling-3.1-flash-1019 (C)\n\nGood for: Teams with a multi-GPU server that want an MIT-licensed flagship for long agent runs and coding\n\nWatch for: 574 GB in FP8 with no smaller build from inclusionAI, so no single machine under 690 GB of GPU memory runs it\n\n### Nemotron 3 Super 120B A12B (B)\n\nGood for: Agents on a 128 GB unified machine or Blackwell GPUs that want a long-context, tool-calling reasoning model with documented training data\n\nAhead on:\n- Reach, 75 against 60\n- Risk, 65 against 45\n- Agent interface, 90 against 60\n- Documentation and upkeep, 70 against 56\n\nWatch for: The card's serving commands pass --trust-remote-code\n\n\n## Why the edge\n\n### Agent interface: Nemotron 3 Super 120B A12B +30\n\n- Ling-3.1-flash-1019 (60): Chat template 20, chat_template.jinja in the repository (6,042 characters), which handles tools, the thinking switch and preserved reasoning. Tool-calling format 10, only the template says how tools are declared (JSON in \u003ctools\u003e) and how calls come back (\u003ctool_call\u003ename with \u003carg_key\u003e and \u003carg_value\u003e pairs). Trained tool calling 20, the template tells the model it may call one or more functions and loops over every entry in an assistant message's tool_calls, and the card reports gains on agentic benchmarks, AA-Briefcase and AutomationBench-AA among them. Structured output 0, the card doesn't document JSON or JSON Schema output. Reasoning control 0, the card documents turning thinking on or off per request with chat_template_kwargs enable_thinking, and names the ling3 reasoning parser, but doesn't say where the reasoning text comes back. Context 10, 262,144 tokens natively in config.json (max_position_embeddings 262144, rope_scaling null) and per SGLang's cookbook. 20 + 10 + 20 + 0 + 0 + 10 = 60.\n- Nemotron 3 Super 120B A12B (90): The chat template earns 20, a Jinja file in the repository. The tool-calling format earns 25, since NVIDIA documents the format, names the qwen3_coder parser for three runtimes and shows a tool call in its cookbook. Trained tool calling earns 20, with tool-use reinforcement learning and several calls per turn in the template. Structured output earns 5, claimed from training with no instructions. Reasoning control earns 10, thinking off, low effort or a budget per request with the trace returned as reasoning_content. Context earns 10 for 262,144 tokens in the config and up to 1M per the card.\n\n### Risk: Nemotron 3 Super 120B A12B +20\n\n- Ling-3.1-flash-1019 (45): File safety 15, safetensors only, no .bin, .pt or .pth files (25), less 10 because the card's documented vLLM command passes --trust-remote-code, which runs the repository's configuration_bailing_moe_v3.py, since vLLM 0.33.0 registers no bailing_hybrid config of its own. Provenance 15, files in the inclusionAI organisation that inclusion-ai.org links to, per the Ling-3.0-tiny listing (10). Safety evaluation 0, no safety results for the model on its card or in the training-content summary. Agent guidance 0, the card has nothing on prompt injection, refusals or safeguards, and inclusionAI publishes no guardrail probe for this model as it did for Ling 3.0. Licence risk 15, MIT with no use policy, no field-of-use limits, no threshold and no clause letting inclusionAI change or revoke it. Security programme 0, www.inclusion-ai.org answers 404 for security.txt on 10 November 2026, and the Ling-3.0-tiny listing found no SECURITY.md or disclosure policy in the inclusionAI .github repository. 15 + 15 + 0 + 0 + 15 + 0 = 45.\n- Nemotron 3 Super 120B A12B (65): File safety earns 15, safetensors only but with 10 off because the documented serving commands pass trust-remote-code and the repository ships model and parser code. Provenance earns 15, files in NVIDIA's linked organisation with a hash for every file. Safety evaluation earns 0, since NVIDIA says it evaluated content safety but publishes no results. Agent guidance earns 10, a subcard that names prompt injection and says what safeguards to add, without an instruction hierarchy or concrete mitigations. Licence risk keeps all 15. The security programme earns 10 for the vulnerability submission route on the card and NVIDIA's public bulletins.\n\n### Reach: Nemotron 3 Super 120B A12B +15\n\n- Ling-3.1-flash-1019 (60): Memory 0, inclusionAI's only build is the FP8 release at 573.78 GB, × 1.2 = 688.5 GB, above 256 GB, and no runtime's library has a build. Runtimes 25, Released model code in Transformers 5.22.2 (bailing_hybrid), vLLM 0.33.0 (BailingMoeV3ForCausalLM, with ling3 tool and reasoning parsers), SGLang 0.5.23 (bailing_moe_v3.py, its bailing_hybrid config and MTP module) and llama.cpp b12242 (bailingmoe3), and Ollama, credited through its pinned llama.cpp as on the Ling-3.0-tiny listing: v0.40.16 sets LLAMA_CPP_VERSION to b12177. Builds 10, the release is FP8, the one quantised precision inclusionAI publishes for it. Terms 15, MIT in the card's metadata, with no use policy or other terms added on the card or in the repository. Download 10, the Hub API's model info gives gated false on 10 November 2026, so the files download with no account and no terms to accept. Hosted 0, the Hub API's provider mapping is empty for this repository on 10 November 2026. 0 + 25 + 10 + 15 + 10 + 0 = 60.\n- Nemotron 3 Super 120B A12B (75): Memory earns 10, because the smallest 4-bit build, NVIDIA's 80.3 GB NVFP4, needs about 96 GB with the fifth added, which fits the reference machine but not 64 GB, and Ollama's 87 GB build lands in the same band. Runtimes earn the full 25 from released Transformers, vLLM, SGLang, llama.cpp and Ollama. Builds earn 15 for NVIDIA's own FP8 and NVFP4. Terms earn 10, since the Nemotron licence is custom even though it's free for commercial use with no threshold or use policy. Download earns 10, with no gate or account. Hosted earns 5, since NVIDIA's own API serves it.\n\n### Documentation and upkeep: Nemotron 3 Super 120B A12B +14\n\n- Ling-3.1-flash-1019 (56): Model card 11, Intended use (complex reasoning, multi-step tasks and agentic workflows), architecture, parameters (560B total, 25B active) and context (1M claimed) are on the card. Training data 15, the training-content summary the card links names the source categories (Common Crawl and other public datasets, commercially licensed data, the Ant Spider crawler, synthetic data), the size band (over 10 trillion text tokens), data collected up to about July 2026 and the filtering, without listing datasets with sizes. Last change 20, the newest commit, on 4 November 2026, edited the card, six days before this check, and the files went up on 27 October. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (both opened on 27 October 2026) has a reply from an inclusionAI member. Version policy 5, the update went into a repository of its own and Ling-3.1-flash and Ling 3.0 stay in theirs. File health 5, the card states a 1M-token context while config.json gives 262,144 with no rope scaling, and its serve commands name the earlier repository as the model path. 11 + 15 + 20 + 0 + 5 + 5 = 56.\n- Nemotron 3 Super 120B A12B (70): The model card earns 20, all seven items with limitations in the linked subcard. Training data earns 20, datasets listed with sources and token counts and the corpora published. Last change earns 20, counting NVIDIA's own inference code for the model, as on Nemotron 3 Ultra and Nano. The repositories last changed with a card edit on 25 August 2026, but NVIDIA's TensorRT-LLM, which the card's serving commands use, changed its Nemotron-H model code on 14 October, 18 days ago. Responsiveness earns 0, with no NVIDIA reply in any of the nine discussions, re-counted on 1 November. Version policy earns 5 as a first release. File health earns 5, since a generation report waits without a reply and one card link is dead. 20+20+20+0+5+5 = 70.\n\n## Score by category\n\n| Category | Weight | Ling-3.1-flash-1019 | Nemotron 3 Super 120B A12B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 60 | 75 | Nemotron 3 Super 120B A12B +15 |\n| Risk | 20% (20 this run) | 45 | 65 | Nemotron 3 Super 120B A12B +20 |\n| Agent interface | 10% (10 this run) | 60 | 90 | Nemotron 3 Super 120B A12B +30 |\n| Documentation and upkeep | 10% (10 this run) | 56 | 70 | Nemotron 3 Super 120B A12B +14 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **54.8 · C** | **66.7 · B** | |\n\n## Facts side by side\n\n| Fact | Ling-3.1-flash-1019 | Nemotron 3 Super 120B A12B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | inclusionAI (Ant Group) | 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 | MIT | NVIDIA Nemotron Open Model License |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-11-04 | 2026-03-11 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | none | 2.8/5 (4) |\n\n## How the scores have moved\n\n### Ling-3.1-flash-1019\n\nNo score change since it was listed.\n\n### Nemotron 3 Super 120B A12B\n\n- +0.5 on 1 November 2026: correction\n\n## Verdicts\n\n**Ling-3.1-flash-1019.** inclusionAI's newest flagship, a 560B hybrid mixture of experts under plain MIT that vLLM, SGLang, Transformers and llama.cpp run in released versions. It ships only as 574 GB of FP8, so it needs a multi-GPU server. The card overstates the context, documents little about tool calls, and has no safety results.\n\n**Nemotron 3 Super 120B A12B.** A long-context agent cousin with trained parallel tool calls, a reasoning switch and a full account of its training data, under a free commercial licence. It needs about 100 GB of GPU memory, a 128 GB unified machine or Blackwell hardware for NVIDIA's NVFP4. The main risks are serving commands that run repository code and no published safety results.\n\n## Before you call either\n\n### Ling-3.1-flash-1019\n\n1. Serve it with SGLang 0.5.23 or later on 8 H200 or 4 GB300 GPUs, per the card, and use this repository's name as the model path, not the earlier one the card's command shows\n2. With vLLM pass `--trust-remote-code`, `--tool-call-parser ling3` and `--reasoning-parser ling3`\n3. Set `chat_template_kwargs` `enable_thinking` false for direct answers. Reasoning comes back in `reasoning_content` per SGLang's cookbook\n4. Plan on 262,144 tokens of context. The 1M figure needs the YaRN override in SGLang's cookbook\n5. Use temperature 1.0, top_p 0.95 and top_k 20, per the card\n6. Validate JSON output yourself, since structured output isn't documented\n\n### Nemotron 3 Super 120B A12B\n\n1. On the reference machine start it with `ollama run nemotron-3-super`, the 87 GB q4_K_M build, the only one that fits (about 104 GB with the margin)\n2. Raise the Strix Halo GPU memory limit first with `amd-ttm --set 100`, since the default of about 62.8 GiB is far below what it needs\n3. With vLLM serve it with `--reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` (SGLang uses `--reasoning-parser nemotron_3`)\n4. Use `temperature=1.0` and `top_p=0.95` for every task, as the card says\n5. Thinking is on by default. Pass `enable_thinking: false` or `low_effort: true` in `chat_template_kwargs` for short tasks\n6. For coding agents pass `force_nonempty_content: true` in `chat_template_kwargs`, so a cut-off reasoning trace still returns content\n7. Raise context past 262,144 only with the card's flags (`VLLM_ALLOW_LONG_MAX_MODEL_LEN=1`, `--max-model-len 1048576`)\n\n## Questions\n\n### Which is better for AI agents, Ling-3.1-flash-1019 or Nemotron 3 Super 120B A12B?\n\nNemotron 3 Super 120B A12B scores 66.7 (B) on agent readiness against Ling-3.1-flash-1019's 54.8 (C), and leads in every scored category.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-nemotron-3-super-120b-a12b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-nemotron-3-super-120b-a12b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"ling-3-1-flash-1019\", \"b\": \"nemotron-3-super-120b-a12b\"}`. From a terminal: `anchor compare ling-3-1-flash-1019 nemotron-3-super-120b-a12b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/ling-3-1-flash-1019.json and https://www.anchorterminal.com/api/v1/tools/nemotron-3-super-120b-a12b.json\n\n## Other comparisons with Ling-3.1-flash-1019 or Nemotron 3 Super 120B A12B\n\n- [Agents-A1-4B vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/agents-a1-4b-vs-ling-3-1-flash-1019.md)\n- [Agents-A1-4B vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-super-120b-a12b.md)\n- [Apertus v1.5 8B vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ling-3-1-flash-1019.md)\n- [Apertus v1.5 8B vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-super-120b-a12b.md)\n- [Apodex-1.1-mini vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ling-3-1-flash-1019.md)\n- 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120B A12B](https://www.anchorterminal.com/compare/cola2-vs-nemotron-3-super-120b-a12b.md)\n- [Cosmos-Reason2-2B vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ling-3-1-flash-1019.md)\n- [Cosmos-Reason2-2B vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-super-120b-a12b.md)\n- [Darwin-36B-Opus vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ling-3-1-flash-1019.md)\n- [Darwin-36B-Opus vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-super-120b-a12b.md)\n- [DeepSeek-V3.2 vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ling-3-1-flash-1019.md)\n- [DeepSeek-V3.2 vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-super-120b-a12b.md)\n- [DeepSeek V4.1 Flash vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ling-3-1-flash-1019.md)\n- [DeepSeek V4.1 Flash vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-super-120b-a12b.md)\n- [DeepSeek-V4-Flash-0731 vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ling-3-1-flash-1019.md)\n- [DeepSeek-V4-Flash-0731 vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nemotron-3-super-120b-a12b.md)\n- [Devstral Small 2 vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/devstral-small-2-vs-ling-3-1-flash-1019.md)\n- [Devstral Small 2 vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/devstral-small-2-vs-nemotron-3-super-120b-a12b.md)\n- [DiffusionGemma 26B A4B IT vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ling-3-1-flash-1019.md)\n- [DiffusionGemma 26B A4B IT vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nemotron-3-super-120b-a12b.md)\n- [EXAONE 4.5 33B vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ling-3-1-flash-1019.md)\n- [EXAONE 4.5 33B vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-nemotron-3-super-120b-a12b.md)\n- [Gemma 4 12B IT vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ling-3-1-flash-1019.md)\n- [Gemma 4 12B IT vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-super-120b-a12b.md)\n- [Gemma 4 26B A4B IT vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ling-3-1-flash-1019.md)\n- [Gemma 4 26B A4B IT vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nemotron-3-super-120b-a12b.md)\n- [Gemma 4 31B IT vs 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120B A12B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-nemotron-3-super-120b-a12b.md)\n- [gpt-oss-20b vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ling-3-1-flash-1019.md)\n- [gpt-oss-20b vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-nemotron-3-super-120b-a12b.md)\n- [Granite 4.2 30B vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-ling-3-1-flash-1019.md)\n- [Granite 4.2 30B vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-nemotron-3-super-120b-a12b.md)\n- [Granite 4.2 3B vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ling-3-1-flash-1019.md)\n- [Granite 4.2 3B vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-nemotron-3-super-120b-a12b.md)\n- [Granite 4.2 8B vs 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A12B](https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-super-120b-a12b.md)\n- [Kolibri-1 vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/kolibri-1-vs-ling-3-1-flash-1019.md)\n- [Kolibri-1 vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/kolibri-1-vs-nemotron-3-super-120b-a12b.md)\n- [Kronos vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/kronos-vs-ling-3-1-flash-1019.md)\n- [Kronos vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/kronos-vs-nemotron-3-super-120b-a12b.md)\n- [Laguna M.1 vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/laguna-m-1-vs-ling-3-1-flash-1019.md)\n- [Laguna M.1 vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-super-120b-a12b.md)\n- [Laguna S 2.1 vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ling-3-1-flash-1019.md)\n- [Laguna S 2.1 vs Nemotron 3 Super 120B 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