{
  "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",
      "dns": "kronos.grade.anchorterminal.com",
      "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": 1518,
        "categoryRank": 91,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 60,
          "reach": 55,
          "risk": 50,
          "upkeep": 61
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-04"
        },
        "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": "0.7",
            "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": 50,
        "checked": {
          "categories": {
            "interface": "2026-11-04",
            "reach": "2026-11-04",
            "risk": "2026-11-04",
            "upkeep": "2026-11-04"
          },
          "last": "2026-11-04",
          "oldest": "2026-11-04",
          "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"
      },
      "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": 50
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/kronos.json",
      "live": {
        "slug": "kronos",
        "githubStars": 47440,
        "securityTxt": {
          "url": "https://github.com/.well-known/security.txt",
          "state": "valid",
          "expires": "2026-12-10T16:07:34z",
          "checkedAt": "2026-11-10T16:07:34.352094479Z"
        },
        "domain": {
          "domain": "github.com",
          "registered": "2007-10-09",
          "source": "https://rdap.verisign.com/com/v1/domain/github.com",
          "checkedAt": "2026-11-08T15:13:35.342098516Z"
        },
        "github": {
          "repo": "shiyu-coder/kronos",
          "checkedAt": "2026-11-09T10:02:46.822302547Z",
          "stars": 47378,
          "archived": false,
          "issuesEnabled": true,
          "pushedAt": "2026-04-13T12:38:49Z",
          "openIssues": 221,
          "openPRs": 77,
          "issuesOpened90d": 17,
          "issuesClosed90d": 3,
          "prsMerged90d": 0,
          "replies": {
            "sampled": 25,
            "unanswered": 21,
            "medianDays": 0.5
          }
        },
        "updatedAt": "2026-11-10T16:07:34.352094479Z"
      }
    },
    "answer": "Kronos and Ling-3.1-flash-1019 score within a point of each other on agent readiness, 55.2 (C) and 54.8 (C). Ling-3.1-flash-1019 leads on reach.",
    "b": {
      "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"
    },
    "edges": [
      {
        "by": 5,
        "category": "reach",
        "leads": "ling-3-1-flash-1019",
        "name": "Reach",
        "reasons": {
          "a": "Memory 30, the publisher's Kronos-mini with its tokeniser is 0.03 GB and even Kronos-base is 0.43 GB in FP32. Runtimes 0, it runs only through the publisher's own PyTorch package from GitHub, and none of the six named runtimes supports it. Builds 0, FP32 safetensors only. Terms 15, MIT with nothing added. Download 10, nothing is gated. Hosted 0, no inference provider or publisher API serves it (provider mapping still empty on 4 November).",
          "b": "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."
        },
        "scores": {
          "a": 55,
          "b": 60
        }
      },
      {
        "by": 5,
        "category": "risk",
        "leads": "kronos",
        "name": "Risk",
        "reasons": {
          "a": "File safety 15, safetensors only, but the only documented loading path is the publisher's Python package in place of a runtime. Provenance 15, the files are in the NeoQuasar account the publisher's README links, with SHA-256 hashes. Safety evaluation 0, none published. Agent guidance 5, the README's general cautions that the fine-tuning pipeline isn't a production trading system. Licence risk 15, plain MIT; the broken licence link is charged to file health. Security programme 0, no reporting route found.",
          "b": "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."
        },
        "scores": {
          "a": 50,
          "b": 45
        }
      },
      {
        "by": 5,
        "category": "upkeep",
        "leads": "kronos",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 11, intended use, architecture, parameters and context are there, limitations, languages and a cut-off aren't. Training data 20, the paper lists every exchange with asset and observation counts, the filtering and a June 2024 end. Last change 5, the reference code on 13 April 2026 (still the newest commit on 4 November, 205 days) and the Hub repositories on 9 September 2025. Responsiveness 20, the publisher answered 10 of 18 Hugging Face discussions (recounted by script on 4 November), though none since 24 November 2025, and the seven opened since April 2026 have no reply from it. Version policy 5, a first release. File health 0, the cards point to example data the repository has deleted and to licence and requirements links on a branch that doesn't exist.",
          "b": "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."
        },
        "scores": {
          "a": 61,
          "b": 56
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Yu Shi (shiyu-coder, NeoQuasar), Tsinghua University",
        "b": "inclusionAI (Ant Group)",
        "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": "MIT",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-04-13",
        "b": "2026-11-04",
        "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": "43k stars",
        "b": "none",
        "name": "Popularity"
      }
    ],
    "faq": [
      {
        "answer": "Kronos and Ling-3.1-flash-1019 score within a point of each other on agent readiness, 55.2 (C) and 54.8 (C). Ling-3.1-flash-1019 leads on reach.",
        "question": "Which is better for AI agents, Kronos or Ling-3.1-flash-1019?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Risk, 50 against 45",
          "Documentation and upkeep, 61 against 56"
        ],
        "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, 60 against 55"
        ],
        "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"
      }
    ],
    "history": null,
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-kronos.json",
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        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-kronos"
      },
      {
        "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/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"
      },
      {
        "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/apodex-1-1-mini-vs-kronos.json",
        "title": "Apodex-1.1-mini vs Kronos",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kronos"
      },
      {
        "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/arete-27b-vs-kronos.json",
        "title": "Arete-27B vs Kronos",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-kronos"
      },
      {
        "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/bigbang-v1-vs-kronos.json",
        "title": "BigBang-v1 vs Kronos",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-kronos"
      },
      {
        "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/carbon-x-vs-kronos.json",
        "title": "Carbon-X vs Kronos",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-kronos"
      },
      {
        "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/cola2-vs-kronos.json",
        "title": "Cola2 vs Kronos",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-kronos"
      },
      {
        "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/cosmos-reason2-2b-vs-kronos.json",
        "title": "Cosmos-Reason2-2B vs Kronos",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-kronos"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ling-3-1-flash-1019.json",
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        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ling-3-1-flash-1019"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-kronos.json",
        "title": "Darwin-36B-Opus vs Kronos",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-kronos"
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      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ling-3-1-flash-1019.json",
        "title": "Darwin-36B-Opus vs Ling-3.1-flash-1019",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ling-3-1-flash-1019"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-kronos.json",
        "title": "DeepSeek-V3.2 vs Kronos",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-kronos"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ling-3-1-flash-1019.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ling-3-1-flash-1019"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kronos.json",
        "title": "DeepSeek V4.1 Flash vs Kronos",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kronos"
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ling-3-1-flash-1019.json",
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        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-kronos.json",
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      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-kronos.json",
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-ling-3-1-flash-1019"
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ling-3-1-flash-1019"
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-ling-3-1-flash-1019"
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        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ling-3-1-flash-1019.json",
        "title": "Hy-Memory-30B-A3B vs Ling-3.1-flash-1019",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ling-3-1-flash-1019"
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-kronos.json",
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        "title": "Inkling-Small vs Ling-3.1-flash-1019",
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        "json": "https://www.anchorterminal.com/compare/inkling-vs-kronos.json",
        "title": "Inkling vs Kronos",
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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/kimi-k3-1-pro-vs-kronos.json",
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        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ling-3-1-flash-1019"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-kronos.json",
        "title": "Kimi K3 vs Kronos",
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-ling-3-1-flash-1019.json",
        "title": "Kimi K3 vs Ling-3.1-flash-1019",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-ling-3-1-flash-1019"
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      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-kronos.json",
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        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-kronos"
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      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-ling-3-1-flash-1019.json",
        "title": "Kolibri-1 vs Ling-3.1-flash-1019",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-ling-3-1-flash-1019"
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-lfm3-1-7b.json",
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-minicpm-o-4-5.json",
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-minicpm-v-4-7-1b.json",
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-minicpm-v-4-7-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/kronos-vs-minicpm-v-4-7-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-minimax-m2-7.json",
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-mistral-large-4.json",
        "title": "Kronos vs Mistral Large 4",
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-nanbeige-4-3-33b.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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        "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-omni-30b-a3b-reasoning.json",
        "title": "Kronos vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "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-super-120b-a12b.json",
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        "url": "https://www.anchorterminal.com/compare/kronos-vs-nemotron-3-super-120b-a12b"
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-nemotron-3-ultra-550b-a55b.json",
        "title": "Kronos vs Nemotron 3 Ultra 550B A55B",
        "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",
        "title": "Kronos vs Olmo 3 7B Think",
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        "title": "Kronos vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-ornith-1-5-35b-a3b"
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        "title": "Kronos vs Ornith-1.5-397B",
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-ornith-1-5-9b.json",
        "title": "Kronos vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-ornith-1-5-9b"
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-otel-2-0-llm-31b-it.json",
        "title": "Kronos vs OTel-2.0-LLM-31B-IT",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-otel-2-0-llm-31b-it"
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-qwen-agentworld-35b-a3b.json",
        "title": "Kronos vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "Kronos vs Qwen3-30B-A3B-Instruct-2507",
        "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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        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-unlimited-ocr-2"
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        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-wevisone-vl-2b"
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        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-wevisone-vl-4b"
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    "panel": null,
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        "pending": true,
        "weight": 35
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      {
        "by": 5,
        "edge": "ling-3-1-flash-1019",
        "key": "reach",
        "kronos": 55,
        "ling-3-1-flash-1019": 60,
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 5,
        "edge": "kronos",
        "key": "risk",
        "kronos": 50,
        "ling-3-1-flash-1019": 45,
        "name": "Risk",
        "weight": 20
      },
      {
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        "edge": "",
        "key": "interface",
        "kronos": 60,
        "ling-3-1-flash-1019": 60,
        "name": "Agent interface",
        "weight": 10
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        "key": "upkeep",
        "kronos": 61,
        "ling-3-1-flash-1019": 56,
        "name": "Documentation and upkeep",
        "weight": 10
      }
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    "summary": "Kronos and Ling-3.1-flash-1019 score within a point of each other on agent readiness, 55.2 (C) and 54.8 (C). Ling-3.1-flash-1019 leads on reach. Both do open-weight models you run yourself.",
    "tasks": null,
    "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.",
      "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."
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    "json": "https://www.anchorterminal.com/compare/kronos-vs-ling-3-1-flash-1019.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
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  "markdown": "Kronos and Ling-3.1-flash-1019 score within a point of each other on agent readiness, 55.2 (C) and 54.8 (C). Ling-3.1-flash-1019 leads on reach. Both do open-weight models you run yourself.\n\n- Kronos: grade C, 55.2/100, rank #122 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/kronos.md · JSON https://www.anchorterminal.com/api/v1/tools/kronos.json\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- 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### 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\nAhead on:\n- Risk, 50 against 45\n- Documentation and upkeep, 61 against 56\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### 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\nAhead on:\n- Reach, 60 against 55\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\n## Why the edge\n\n### Reach: Ling-3.1-flash-1019 +5\n\n- Kronos (55): Memory 30, the publisher's Kronos-mini with its tokeniser is 0.03 GB and even Kronos-base is 0.43 GB in FP32. Runtimes 0, it runs only through the publisher's own PyTorch package from GitHub, and none of the six named runtimes supports it. Builds 0, FP32 safetensors only. Terms 15, MIT with nothing added. Download 10, nothing is gated. Hosted 0, no inference provider or publisher API serves it (provider mapping still empty on 4 November).\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\n### Risk: Kronos +5\n\n- Kronos (50): File safety 15, safetensors only, but the only documented loading path is the publisher's Python package in place of a runtime. Provenance 15, the files are in the NeoQuasar account the publisher's README links, with SHA-256 hashes. Safety evaluation 0, none published. Agent guidance 5, the README's general cautions that the fine-tuning pipeline isn't a production trading system. Licence risk 15, plain MIT; the broken licence link is charged to file health. Security programme 0, no reporting route found.\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\n### Documentation and upkeep: Kronos +5\n\n- Kronos (61): Model card 11, intended use, architecture, parameters and context are there, limitations, languages and a cut-off aren't. Training data 20, the paper lists every exchange with asset and observation counts, the filtering and a June 2024 end. Last change 5, the reference code on 13 April 2026 (still the newest commit on 4 November, 205 days) and the Hub repositories on 9 September 2025. Responsiveness 20, the publisher answered 10 of 18 Hugging Face discussions (recounted by script on 4 November), though none since 24 November 2025, and the seven opened since April 2026 have no reply from it. Version policy 5, a first release. File health 0, the cards point to example data the repository has deleted and to licence and requirements links on a branch that doesn't exist.\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\n## Score by category\n\n| Category | Weight | Kronos | Ling-3.1-flash-1019 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 55 | 60 | Ling-3.1-flash-1019 +5 |\n| Risk | 20% (20 this run) | 50 | 45 | Kronos +5 |\n| Agent interface | 10% (10 this run) | 60 | 60 | even |\n| Documentation and upkeep | 10% (10 this run) | 61 | 56 | Kronos +5 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **55.2 · C** | **54.8 · C** | |\n\n## Facts side by side\n\n| Fact | Kronos | Ling-3.1-flash-1019 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Yu Shi (shiyu-coder, NeoQuasar), Tsinghua University | inclusionAI (Ant Group) |\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 | MIT |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-04-13 | 2026-11-04 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Popularity | 43k stars | none |\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**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## 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### 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## Questions\n\n### Which is better for AI agents, Kronos or Ling-3.1-flash-1019?\n\nKronos and Ling-3.1-flash-1019 score within a point of each other on agent readiness, 55.2 (C) and 54.8 (C). Ling-3.1-flash-1019 leads on reach.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/kronos-vs-ling-3-1-flash-1019.json, and with the fewest tokens: https://www.anchorterminal.com/compare/kronos-vs-ling-3-1-flash-1019.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"kronos\", \"b\": \"ling-3-1-flash-1019\"}`. From a terminal: `anchor compare kronos ling-3-1-flash-1019`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/kronos.json and https://www.anchorterminal.com/api/v1/tools/ling-3-1-flash-1019.json\n\n## Other comparisons with Kronos or Ling-3.1-flash-1019\n\n- [Agents-A1-4B vs Kronos](https://www.anchorterminal.com/compare/agents-a1-4b-vs-kronos.md)\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- [Apertus v1.5 8B vs Kronos](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kronos.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- [Apodex-1.1-mini vs Kronos](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kronos.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- [Arete-27B vs 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Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ling-3-1-flash-1019.md)\n- [Gemma 4 26B A4B IT vs Kronos](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-kronos.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 31B IT vs Kronos](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-kronos.md)\n- [Gemma 4 31B IT vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ling-3-1-flash-1019.md)\n- [Gemma 4 E2B IT vs Kronos](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-kronos.md)\n- [Gemma 4 E2B IT vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ling-3-1-flash-1019.md)\n- [Gemma 4 E4B IT vs Kronos](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-kronos.md)\n- [Gemma 4 E4B IT vs 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Kronos](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-kronos.md)\n- [gpt-oss-120b vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-ling-3-1-flash-1019.md)\n- [gpt-oss-20b vs Kronos](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-kronos.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- [Granite 4.2 30B vs Kronos](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-kronos.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 3B vs Kronos](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-kronos.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 8B vs Kronos](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-kronos.md)\n- [Granite 4.2 8B vs 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9B](https://www.anchorterminal.com/compare/kronos-vs-mimo-v2-6-distill-qwen-9b.md)\n- [Kronos vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/kronos-vs-mimo-v2-6-flash-rl.md)\n- [Kronos vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/kronos-vs-minicpm-o-4-5.md)\n- [Kronos vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/kronos-vs-minicpm-v-4-7-1b.md)\n- [Kronos vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/kronos-vs-minicpm-v-4-7-35b-a3b.md)\n- [Kronos vs MiniCPM5-2B](https://www.anchorterminal.com/compare/kronos-vs-minicpm5-2b.md)\n- [Kronos vs MiniMax-M2.5](https://www.anchorterminal.com/compare/kronos-vs-minimax-m2-5.md)\n- [Kronos vs MiniMax-M2.7](https://www.anchorterminal.com/compare/kronos-vs-minimax-m2-7.md)\n- [Kronos vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/kronos-vs-minimax-m3-1-flash.md)\n- [Kronos vs Mistral Large 4](https://www.anchorterminal.com/compare/kronos-vs-mistral-large-4.md)\n- [Kronos vs 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    "description": "Kronos and Ling-3.1-flash-1019 score within a point of each other for open-weight models you run yourself, 55.2 and 54.8 out of 100. Prices, MCP, x402, uptime and agent notes side by side.",
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    "section": "tools",
    "title": "Kronos vs Ling-3.1-flash-1019 for AI agents in 2026: scores and prices",
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    "updated": "2026-11-10",
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