{
  "data": {
    "a": {
      "slug": "kronos",
      "name": "Kronos",
      "vendor": "Yu Shi (shiyu-coder, NeoQuasar), Tsinghua University",
      "vendorUrl": "https://github.com/shiyu-coder/Kronos",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Kronos is an open-weight family of small forecasting models for financial candlestick (K-line) series, from Tsinghua University researchers. It reads open, high, low, close, volume and amount bars with timestamps and generates forecast bars, under MIT.",
      "url": "https://www.anchorterminal.com/tools/kronos",
      "markdownUrl": "https://www.anchorterminal.com/tools/kronos.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/kronos.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/kronos.json",
      "repo": "https://github.com/shiyu-coder/Kronos",
      "license": "MIT",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "NeoQuasar/Kronos-mini"
        },
        {
          "registry": "huggingface",
          "name": "NeoQuasar/Kronos-small"
        },
        {
          "registry": "huggingface",
          "name": "NeoQuasar/Kronos-base"
        },
        {
          "registry": "huggingface",
          "name": "NeoQuasar/Kronos-Tokenizer-base"
        },
        {
          "registry": "huggingface",
          "name": "NeoQuasar/Kronos-Tokenizer-2k"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The Hub reports gated false for all five NeoQuasar repositories, and the code is in a public GitHub repository (checked 2026-10-25).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under the MIT licence named in each card's metadata and in the GitHub repository's LICENSE. Nobody sells per-call access and no inference provider serves it. Running it costs the hardware, 0.03 GB for Kronos-mini with its tokeniser and 0.43 GB for Kronos-base with its tokeniser, in FP32 (checked 2026-10-25).",
      "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 2026-10-25).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": 43000,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-25"
      },
      "docsUrl": "https://github.com/shiyu-coder/Kronos",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "time-series",
        "forecasting",
        "finance",
        "candlestick",
        "k-line",
        "mit",
        "safetensors",
        "self-hosted"
      ],
      "lastRelease": "2026-04-13",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 55.2,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1210,
        "categoryRank": 68,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "interface": 60,
          "reach": 55,
          "risk": 50,
          "upkeep": 61
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-10-25"
        },
        "negative": 0,
        "verdict": "Forecasts the next price bars from a history of up to 512 candlestick bars. Every size fits in under 0.5 GB and runs on a CPU, but only through the publisher's own Python package. Nobody has benchmarked it independently, its quickstart data was deleted from the repository, and the publisher hasn't replied since November 2025.",
        "bestFor": "Agents that need a quick local forecast or synthetic sample of a candlestick series on modest hardware, for research or as one input among others.",
        "strengths": [
          "MIT with nothing added, ungated, safetensors only with SHA-256 hashes",
          "Under 0.5 GB for every public size, so it runs on a CPU, Apple MPS or a small GPU",
          "Input and output documented as plain DataFrames, with batch prediction over many series",
          "Training data listed by exchange with observation counts, filtering and a June 2024 end"
        ],
        "weaknesses": [
          "Runs only through the publisher's own PyTorch code from GitHub, in no named runtime and with no quantised build",
          "A 512-bar context for the small and base sizes, and the mean forecast only, without quantiles",
          "The sample data the quickstart reads was deleted on 13 April 2026, and the cards' licence link points at a missing branch",
          "No safety results, no security contact and no publisher reply in its Hugging Face discussions since November 2025",
          "The publisher's forecasting results come from its own paper, with no independent benchmark found"
        ],
        "agentNotes": [
          "Clone https://github.com/shiyu-coder/Kronos and `pip install -r requirements.txt`, then load `Kronos.from_pretrained(\"NeoQuasar/Kronos-small\")` and `KronosTokenizer.from_pretrained(\"NeoQuasar/Kronos-Tokenizer-base\")` and wrap them in KronosPredictor",
          "Pair Kronos-mini with Kronos-Tokenizer-2k and Kronos-small or Kronos-base with Kronos-Tokenizer-base, the cards' pairing",
          "The examples read examples/data/XSHG_5min_600977.csv, which no longer exists; point them at tests/data/regression_input.csv, 2,500 five-minute bars",
          "Keep the lookback at or under max_context, 512 bars, and pass the future timestamps you want as y_timestamp, since the calendar fields come from them",
          "Raise sample_count for a steadier forecast, but expect only the mean back; call the lower-level auto_regressive_inference yourself if you need the paths",
          "Treat forecasts as model output to check, not signals; the README says its own backtest pipeline isn't a production trading system"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 55.2
          }
        ],
        "editorialScores": {
          "interface": 60,
          "reach": 55,
          "risk": 50,
          "upkeep": 61
        },
        "provenanceScore": 40,
        "checked": {
          "categories": {
            "interface": "2026-10-25",
            "reach": "2026-10-25",
            "risk": "2026-10-25",
            "upkeep": "2026-10-25"
          },
          "last": "2026-10-25",
          "oldest": "2026-10-25",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "git clone https://github.com/shiyu-coder/Kronos \u0026\u0026 cd Kronos\npip install -r requirements.txt   # Python 3.10+, PyTorch 2.0+\npython - \u003c\u003c'EOF'\nimport pandas as pd\nfrom model import Kronos, KronosTokenizer, KronosPredictor\ntok = KronosTokenizer.from_pretrained(\"NeoQuasar/Kronos-Tokenizer-base\")\nmodel = Kronos.from_pretrained(\"NeoQuasar/Kronos-small\")\np = KronosPredictor(model, tok, max_context=512)   # picks cuda, mps or cpu\ndf = pd.read_csv(\"tests/data/regression_input.csv\", parse_dates=[\"timestamps\"])\nx, y = df.iloc[:400], df[\"timestamps\"].iloc[400:520]\ncols = [\"open\", \"high\", \"low\", \"close\", \"volume\", \"amount\"]\nprint(p.predict(df=x[cols], x_timestamp=x[\"timestamps\"], y_timestamp=y, pred_len=120, T=1.0, top_p=0.9, sample_count=1).head())\nEOF"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/kronos"
      },
      "sameCompany": [
        "github-mcp-server",
        "github-copilot-cli",
        "ai-job-search",
        "talorys"
      ],
      "weights": {
        "trainedBy": "Yu Shi and co-authors, Tsinghua University",
        "params": "4.1M to 102.3M",
        "context": 512,
        "goodFor": "Forecasting the next bars of a financial candlestick series, or sampling synthetic ones, on any machine, from a short history of open, high, low, close, volume and amount.",
        "runtimes": [],
        "builds": [
          {
            "name": "Kronos-mini",
            "url": "https://huggingface.co/NeoQuasar/Kronos-mini",
            "who": "publisher",
            "precision": "fp32",
            "sizeGB": 0.03,
            "memoryGB": 8,
            "format": "safetensors",
            "runsOn": [
              "PyTorch"
            ],
            "note": "32,304,418 bytes with Kronos-Tokenizer-2k, which it needs (16,451,361 for the model repository and 15,853,057 for the tokeniser). 2,048-bar context per the card"
          },
          {
            "name": "Kronos-small",
            "url": "https://huggingface.co/NeoQuasar/Kronos-small",
            "who": "publisher",
            "precision": "fp32",
            "sizeGB": 0.11,
            "memoryGB": 8,
            "format": "safetensors",
            "runsOn": [
              "PyTorch"
            ],
            "note": "114,844,309 bytes with Kronos-Tokenizer-base (98,991,258 and 15,853,051). The model the card's and README's examples load"
          },
          {
            "name": "Kronos-base",
            "url": "https://huggingface.co/NeoQuasar/Kronos-base",
            "who": "publisher",
            "precision": "fp32",
            "sizeGB": 0.43,
            "memoryGB": 8,
            "format": "safetensors",
            "runsOn": [
              "PyTorch"
            ],
            "note": "425,127,647 bytes with Kronos-Tokenizer-base (409,274,596 and 15,853,051). The largest public size"
          }
        ],
        "run": "git clone https://github.com/shiyu-coder/Kronos \u0026\u0026 cd Kronos \u0026\u0026 pip install -r requirements.txt, then in Python: KronosPredictor(Kronos.from_pretrained(\"NeoQuasar/Kronos-small\"), KronosTokenizer.from_pretrained(\"NeoQuasar/Kronos-Tokenizer-base\"), max_context=512).predict(df=..., x_timestamp=..., y_timestamp=..., pred_len=120)",
        "runBuild": "Kronos-small",
        "checked": "2026-10-25",
        "minMemoryGB": 8
      },
      "area": "models",
      "provenance": {
        "legalEntity": "No company. Yu Shi publishes it from the GitHub account shiyu-coder and the Hugging Face user account NeoQuasar (named ShiYu), with co-authors at Tsinghua University per the paper",
        "domain": "github.com",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-25",
        "notes": [
          "NeoQuasar is a Hugging Face user account (fullname ShiYu), not an organisation. The GitHub README and the paper link it, so it's the publisher's own account.",
          "Nothing is hosted, so the MIT licence stands in for terms of service and no privacy policy is scored.",
          "No SECURITY.md, security contact or disclosure policy was found in the GitHub repository or on the cards.",
          "No changelog and no GitHub releases. The README's News section has three dated items, the last on 10 November 2025."
        ],
        "score": 40
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/kronos.json",
      "live": {
        "slug": "kronos",
        "securityTxt": {
          "url": "https://github.com/.well-known/security.txt",
          "state": "valid",
          "expires": "2026-11-24T15:28:23z",
          "checkedAt": "2026-10-25T15:28:23.736926714Z"
        },
        "updatedAt": "2026-10-25T15:28:23.736926714Z"
      }
    },
    "answer": "Nemotron 3 Super 120B A12B scores 66.2 (B) on agent readiness against Kronos's 55.2 (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",
      "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": "2026-10-21"
      },
      "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": 66.2,
        "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": 1210,
        "categoryRank": 12,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 54,
          "interface": 90,
          "reach": 75,
          "risk": 65,
          "upkeep": 65
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-21"
        },
        "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": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 66.2
          }
        ],
        "editorialScores": {
          "capability": 54,
          "interface": 90,
          "reach": 75,
          "risk": 65,
          "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": "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",
        "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",
        "parakeet-tdt-0-6b-v3",
        "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": "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": "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": "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": 128
      },
      "family": {
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          {
            "name": "Nemotron 3 Nano 4B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16",
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            "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-4b"
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            "name": "Nemotron 3 Nano Omni 30B-A3B Reasoning",
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            "params": "550B total, 55B active",
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          {
            "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",
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          {
            "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"
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          {
            "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"
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            "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"
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          {
            "name": "Nemotron Labs 3 Puzzle 75B-A9B",
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            "params": "75B total, 9B active"
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          {
            "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"
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          {
            "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"
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        ],
        "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": 73
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b.json",
      "live": {
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          "url": "https://nvidia.com/.well-known/security.txt",
          "state": "unknown",
          "checkedAt": "2026-10-25T15:27:25.017341078Z"
        },
        "domain": {
          "domain": "nvidia.com",
          "registered": "1993-04-20",
          "source": "https://rdap.verisign.com/com/v1/domain/nvidia.com",
          "checkedAt": "2026-10-25T13:52:20.255128079Z"
        },
        "pages": [
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            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16/commits/main",
            "kind": "changelog",
            "status": 200,
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            "fingerprint": "6bdf8bc440ca"
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        "updatedAt": "2026-10-25T15:27:25.017341078Z"
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        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Yu Shi (shiyu-coder, NeoQuasar), Tsinghua University",
        "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": "2026-04-13",
        "b": "2026-03-11",
        "name": "Last release"
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      {
        "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": "",
        "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": "43k stars",
        "b": "none",
        "name": "Popularity"
      },
      {
        "a": "none",
        "b": "2.8/5 (4)",
        "name": "Agent reviews"
      }
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    "faq": [
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        "answer": "Nemotron 3 Super 120B A12B scores 66.2 (B) on agent readiness against Kronos's 55.2 (C), and leads in every scored category.",
        "question": "Which is better for AI agents, Kronos or Nemotron 3 Super 120B A12B?"
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    ],
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        "aheadOn": null,
        "also": null,
        "goodFor": "Agents that need a quick local forecast or synthetic sample of a candlestick series on modest hardware, for research or as one input among others.",
        "slug": "kronos",
        "watchFor": "Runs only through the publisher's own PyTorch code from GitHub, in no named runtime and with no quantised build"
      },
      {
        "aheadOn": [
          "Reach, 75 against 55",
          "Risk, 65 against 50",
          "Agent interface, 90 against 60"
        ],
        "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"
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      "name": "Open-weight models you run yourself"
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        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-super-120b-a12b.json",
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        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kronos.json",
        "title": "Apertus v1.5 8B vs Kronos",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kronos"
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        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-super-120b-a12b.json",
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        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-kronos.json",
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        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-kronos.json",
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-kronos"
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        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nemotron-3-super-120b-a12b.json",
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nemotron-3-super-120b-a12b"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-kronos.json",
        "title": "Gemma 4 12B IT vs Kronos",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-super-120b-a12b.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nemotron-3-super-120b-a12b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nemotron-3-super-120b-a12b"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-kronos.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-super-120b-a12b.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nemotron-3-super-120b-a12b.json",
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        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-super-120b-a12b.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-super-120b-a12b"
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-kronos"
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        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-kronos"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-nemotron-3-super-120b-a12b.json",
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        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-nemotron-3-super-120b-a12b"
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        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-kronos.json",
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        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-kronos"
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        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-nemotron-3-super-120b-a12b.json",
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        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-kronos.json",
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        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-nemotron-3-super-120b-a12b.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-kronos.json",
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        "url": "https://www.anchorterminal.com/compare/inkling-vs-kronos"
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        "json": "https://www.anchorterminal.com/compare/inkling-vs-nemotron-3-super-120b-a12b.json",
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        "url": "https://www.anchorterminal.com/compare/inkling-vs-nemotron-3-super-120b-a12b"
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        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-kronos.json",
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        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-nemotron-3-super-120b-a12b.json",
        "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Nemotron 3 Super 120B A12B",
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        "title": "Kimi K3 vs Kronos",
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-super-120b-a12b.json",
        "title": "Kimi K3 vs Nemotron 3 Super 120B A12B",
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        "title": "Kolibri-1 vs Kronos",
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        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-nemotron-3-super-120b-a12b.json",
        "title": "Kolibri-1 vs Nemotron 3 Super 120B A12B",
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-laguna-xs-2-1.json",
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-lfm2-5-2-6b.json",
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        "url": "https://www.anchorterminal.com/compare/kronos-vs-lfm2-5-2-6b"
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      {
        "json": "https://www.anchorterminal.com/compare/kronos-vs-ling-3-0-tiny.json",
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        "url": "https://www.anchorterminal.com/compare/kronos-vs-ling-3-0-tiny"
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-mimo-v2-6-distill-qwen-9b.json",
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-minicpm-o-4-5.json",
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        "url": "https://www.anchorterminal.com/compare/kronos-vs-minicpm-o-4-5"
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      {
        "json": "https://www.anchorterminal.com/compare/kronos-vs-minicpm5-2b.json",
        "title": "Kronos vs MiniCPM5-2B",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-minicpm5-2b"
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-minimax-m2-5.json",
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        "url": "https://www.anchorterminal.com/compare/kronos-vs-minimax-m2-5"
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-minimax-m2-7.json",
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        "url": "https://www.anchorterminal.com/compare/kronos-vs-minimax-m2-7"
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      {
        "json": "https://www.anchorterminal.com/compare/kronos-vs-minimax-m3-1-flash.json",
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        "url": "https://www.anchorterminal.com/compare/kronos-vs-minimax-m3-1-flash"
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-muse-glimmer-30b.json",
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-nemotron-3-5-bolt-e4b-4gb.json",
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        "url": "https://www.anchorterminal.com/compare/kronos-vs-nemotron-3-5-bolt-e4b-4gb"
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-nemotron-3-5-lightning-30b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-nemotron-3-nano-30b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/kronos-vs-nemotron-3-nano-30b-a3b"
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-nemotron-3-nano-4b.json",
        "title": "Kronos vs Nemotron 3 Nano 4B",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-nemotron-3-nano-4b"
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
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        "url": "https://www.anchorterminal.com/compare/kronos-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-nemotron-3-ultra-550b-a55b.json",
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        "url": "https://www.anchorterminal.com/compare/kronos-vs-nemotron-3-ultra-550b-a55b"
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-olmo-3-7b-think.json",
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        "url": "https://www.anchorterminal.com/compare/kronos-vs-ornith-1-5-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-ornith-1-5-397b.json",
        "title": "Kronos vs Ornith-1.5-397B",
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-ornith-1-5-9b.json",
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        "url": "https://www.anchorterminal.com/compare/kronos-vs-ornith-1-5-9b"
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-30b-a3b-instruct-2507.json",
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        "url": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-30b-a3b-instruct-2507"
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-4b-instruct-2507.json",
        "title": "Kronos vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-4b-instruct-2507"
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-5-0-8b.json",
        "title": "Kronos vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-5-0-8b"
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      {
        "json": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-5-122b-a10b.json",
        "title": "Kronos vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-5-122b-a10b"
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-5-2b.json",
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        "url": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-5-2b"
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-5-397b-a17b.json",
        "title": "Kronos vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-5-397b-a17b"
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-5-4b.json",
        "title": "Kronos vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-5-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-5-9b.json",
        "title": "Kronos vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-5-9b"
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-6-27b.json",
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        "url": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-6-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-6-35b-a3b.json",
        "title": "Kronos vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-6-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-8-27b.json",
        "title": "Kronos vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-8-27b"
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        "title": "Kronos vs Qwen3.8-Flash-Next",
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "Kronos vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-coder-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-coder-next.json",
        "title": "Kronos vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-coder-next"
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      {
        "json": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Kronos vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-omni-30b-a3b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-vl-2b-instruct.json",
        "title": "Kronos vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-vl-2b-instruct"
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Kronos vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-vl-30b-a3b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-vl-32b-instruct.json",
        "title": "Kronos vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-vl-32b-instruct"
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-vl-4b-instruct.json",
        "title": "Kronos vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-vl-4b-instruct"
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-vl-8b-instruct.json",
        "title": "Kronos vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-vl-8b-instruct"
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-qwopus3-8-27b-flash-v2.json",
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        "url": "https://www.anchorterminal.com/compare/kronos-vs-qwopus3-8-27b-flash-v2"
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      {
        "json": "https://www.anchorterminal.com/compare/kronos-vs-recursive-flint.json",
        "title": "Kronos vs Recursive Flint",
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-smollm3-3b.json",
        "title": "Kronos vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-smollm3-3b"
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-spark-x2-6-4b.json",
        "title": "Kronos vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-spark-x2-6-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/kronos-vs-swift-1-5-qwen3-8-27b.json",
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        "url": "https://www.anchorterminal.com/compare/kronos-vs-swift-1-5-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-swift-1-5-qwen3-8-flash-next.json",
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        "url": "https://www.anchorterminal.com/compare/kronos-vs-swift-1-5-qwen3-8-flash-next"
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      {
        "json": "https://www.anchorterminal.com/compare/kronos-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Kronos vs ThinkingCap (Qwen3.8 27B)",
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-super-120b-a12b.json",
        "title": "Laguna M.1 vs Nemotron 3 Super 120B A12B",
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        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-super-120b-a12b.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-super-120b-a12b"
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        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-super-120b-a12b.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-super-120b-a12b"
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        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-super-120b-a12b.json",
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        "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-nemotron-3-super-120b-a12b.json",
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        "title": "Muse Glimmer 30B vs Nemotron 3 Super 120B A12B",
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-olmo-3-7b-think"
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        "title": "Nemotron 3 Super 120B A12B vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ornith-1-5-35b-a3b"
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ornith-1-5-397b"
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      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ornith-1-5-9b.json",
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ornith-1-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "Nemotron 3 Super 120B A12B vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-30b-a3b-instruct-2507"
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-4b-instruct-2507.json",
        "title": "Nemotron 3 Super 120B A12B vs Qwen3-4B-Instruct-2507",
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      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-5-0-8b.json",
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-5-0-8b"
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      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-5-122b-a10b.json",
        "title": "Nemotron 3 Super 120B A12B vs Qwen3.5-122B-A10B",
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-5-2b.json",
        "title": "Nemotron 3 Super 120B A12B vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-5-2b"
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      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-5-397b-a17b.json",
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-6-27b.json",
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-6-27b"
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-6-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-6-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-8-27b.json",
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-8-flash-next.json",
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-8-flash-next"
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-coder-30b-a3b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-coder-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-coder-next.json",
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-omni-30b-a3b-instruct.json",
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-vl-2b-instruct.json",
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-vl-30b-a3b-instruct.json",
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-vl-32b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-vl-32b-instruct"
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-vl-4b-instruct.json",
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwen3-vl-8b-instruct.json",
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwopus3-8-27b-flash-v2.json",
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-qwopus3-8-27b-flash-v2"
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-recursive-flint.json",
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-smollm3-3b.json",
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-smollm3-3b"
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      {
        "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-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"
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-swift-1-5-qwen3-8-flash-next.json",
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        "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"
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    ],
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 20,
        "edge": "nemotron-3-super-120b-a12b",
        "key": "reach",
        "kronos": 55,
        "name": "Reach",
        "nemotron-3-super-120b-a12b": 75,
        "weight": 25
      },
      {
        "by": 15,
        "edge": "nemotron-3-super-120b-a12b",
        "key": "risk",
        "kronos": 50,
        "name": "Risk",
        "nemotron-3-super-120b-a12b": 65,
        "weight": 20
      },
      {
        "by": 30,
        "edge": "nemotron-3-super-120b-a12b",
        "key": "interface",
        "kronos": 60,
        "name": "Agent interface",
        "nemotron-3-super-120b-a12b": 90,
        "weight": 10
      },
      {
        "by": 4,
        "edge": "nemotron-3-super-120b-a12b",
        "key": "upkeep",
        "kronos": 61,
        "name": "Documentation and upkeep",
        "nemotron-3-super-120b-a12b": 65,
        "weight": 10
      }
    ],
    "summary": "Nemotron 3 Super 120B A12B scores 66.2 (B) on agent readiness against Kronos's 55.2 (C), and leads in every scored category. Both do open-weight models you run yourself.",
    "tutorials": [],
    "verdicts": {
      "kronos": "Forecasts the next price bars from a history of up to 512 candlestick bars. Every size fits in under 0.5 GB and runs on a CPU, but only through the publisher's own Python package. Nobody has benchmarked it independently, its quickstart data was deleted from the repository, and the publisher hasn't replied since November 2025.",
      "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/kronos-vs-nemotron-3-super-120b-a12b",
    "json": "https://www.anchorterminal.com/compare/kronos-vs-nemotron-3-super-120b-a12b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/kronos-vs-nemotron-3-super-120b-a12b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/kronos-vs-nemotron-3-super-120b-a12b.min.md"
  },
  "markdown": "Nemotron 3 Super 120B A12B scores 66.2 (B) on agent readiness against Kronos's 55.2 (C), and leads in every scored category. Both do open-weight models you run yourself.\n\n- Kronos: grade C, 55.2/100, rank #86 of 110 open-weight models. Markdown https://www.anchorterminal.com/tools/kronos.md · JSON https://www.anchorterminal.com/api/v1/tools/kronos.json\n- Nemotron 3 Super 120B A12B: grade B, 66.2/100, rank #16 of 110 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 3260 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Kronos (C)\n\nGood for: Agents that need a quick local forecast or synthetic sample of a candlestick series on modest hardware, for research or as one input among others.\n\nWatch for: Runs only through the publisher's own PyTorch code from GitHub, in no named runtime and with no quantised build\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 55\n- Risk, 65 against 50\n- Agent interface, 90 against 60\n\nWatch for: The card's serving commands pass --trust-remote-code\n\n\n## Score by category\n\n| Category | Weight | Kronos | Nemotron 3 Super 120B A12B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 55 | 75 | Nemotron 3 Super 120B A12B +20 |\n| Risk | 20% (20 this run) | 50 | 65 | Nemotron 3 Super 120B A12B +15 |\n| Agent interface | 10% (10 this run) | 60 | 90 | Nemotron 3 Super 120B A12B +30 |\n| Documentation and upkeep | 10% (10 this run) | 61 | 65 | Nemotron 3 Super 120B A12B +4 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **55.2 · C** | **66.2 · B** | |\n\n## Facts side by side\n\n| Fact | Kronos | Nemotron 3 Super 120B A12B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Yu Shi (shiyu-coder, NeoQuasar), Tsinghua University | 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 | 2026-04-13 | 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| 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| Popularity | 43k stars | none |\n| Agent reviews | none | 2.8/5 (4) |\n\n## Verdicts\n\n**Kronos.** Forecasts the next price bars from a history of up to 512 candlestick bars. Every size fits in under 0.5 GB and runs on a CPU, but only through the publisher's own Python package. Nobody has benchmarked it independently, its quickstart data was deleted from the repository, and the publisher hasn't replied since November 2025.\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### Kronos\n\n1. Clone https://github.com/shiyu-coder/Kronos and `pip install -r requirements.txt`, then load `Kronos.from_pretrained(\"NeoQuasar/Kronos-small\")` and `KronosTokenizer.from_pretrained(\"NeoQuasar/Kronos-Tokenizer-base\")` and wrap them in KronosPredictor\n2. Pair Kronos-mini with Kronos-Tokenizer-2k and Kronos-small or Kronos-base with Kronos-Tokenizer-base, the cards' pairing\n3. The examples read examples/data/XSHG_5min_600977.csv, which no longer exists; point them at tests/data/regression_input.csv, 2,500 five-minute bars\n4. Keep the lookback at or under max_context, 512 bars, and pass the future timestamps you want as y_timestamp, since the calendar fields come from them\n5. Raise sample_count for a steadier forecast, but expect only the mean back; call the lower-level auto_regressive_inference yourself if you need the paths\n6. Treat forecasts as model output to check, not signals; the README says its own backtest pipeline isn't a production trading system\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, Kronos or Nemotron 3 Super 120B A12B?\n\nNemotron 3 Super 120B A12B scores 66.2 (B) on agent readiness against Kronos's 55.2 (C), and leads in every scored category.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/kronos-vs-nemotron-3-super-120b-a12b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/kronos-vs-nemotron-3-super-120b-a12b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"kronos\", \"b\": \"nemotron-3-super-120b-a12b\"}`. 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    "section": "tools",
    "title": "Kronos vs Nemotron 3 Super 120B A12B for AI agents (2026)",
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