{
  "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": "Muse Glimmer 30B scores 70 (BB) on agent readiness against Kronos's 55.2 (C), and leads in every scored category.",
    "b": {
      "slug": "muse-glimmer-30b",
      "name": "Muse Glimmer 30B",
      "vendor": "Meta",
      "vendorUrl": "https://dev.meta.ai/docs/muse-glimmer",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Muse Glimmer 30B is Meta's open-weight dense multimodal model for local agent work, 29.6B parameters with text and image input and a 128K-token context, published on Hugging Face under Apache 2.0 with GGUF and ExecuTorch builds.",
      "url": "https://www.anchorterminal.com/tools/muse-glimmer-30b",
      "markdownUrl": "https://www.anchorterminal.com/tools/muse-glimmer-30b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/muse-glimmer-30b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/muse-glimmer-30b.json",
      "repo": "https://huggingface.co/meta-models/Muse-Glimmer-30B",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "meta-models/Muse-Glimmer-30B"
        },
        {
          "registry": "huggingface",
          "name": "meta-models/Muse-Glimmer-30B-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "meta-models/Muse-Glimmer-30B-assistant"
        },
        {
          "registry": "huggingface",
          "name": "meta-models/Muse-Glimmer-30B-ExecuTorch-PTE"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of the four repositories is gated (checked 2026-10-21), and Meta's docs say there is no API key and no gated access.",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0, with a separate Muse Glimmer Usage Policy listing prohibited uses. Meta Model API doesn't serve it. Running it yourself costs the hardware, 17 GB for Meta's Q4_K_M GGUF or about 60 GB in BF16 (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://dev.meta.ai/docs/muse-glimmer",
      "llmsTxt": "https://dev.meta.ai/llms.txt",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "multimodal",
        "reasoning",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "gguf",
        "self-hosted",
        "new"
      ],
      "lastRelease": "2026-10-20",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 70,
        "grade": "BB",
        "agentReady": true,
        "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": 5,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 50,
          "interface": 75,
          "reach": 90,
          "risk": 80,
          "upkeep": 65
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A dense 30B multimodal cousin for local agent and coding work with images, documented tool calls and adjustable reasoning. Meta's 17 GB GGUF fits a 24 GB card and runs in llama.cpp, Ollama, vLLM, SGLang and Transformers. The main risks are one tool call per turn, no documented JSON output and a usage policy that adds limits to Apache 2.0.",
        "bestFor": "Local agents and coding with image input on one 24 to 32 GB GPU or an Apple silicon laptop, where one tool call per turn is enough",
        "strengths": [
          "Meta's own 16.8 GB Q4_K_M GGUF fits a 24 GB card, with a DFlash drafter for speculative decoding",
          "Released model code in vLLM, SGLang, Transformers, llama.cpp and Ollama, no trust_remote_code",
          "Tool format and parser names per runtime documented by Meta, with a Jinja template in the repository",
          "Trained against indirect prompt injection with a principal hierarchy, and injection results published",
          "Ungated download, safetensors and GGUF from Meta's verified organisation, and three live inference providers"
        ],
        "weaknesses": [
          "One tool call per turn and no parallel calls",
          "No structured output documented, and reasoning can't be turned off",
          "A usage policy adds prohibited uses beyond the law to Apache 2.0",
          "Meta replied to 1 of the 40 most recent discussions, and a chat template bug report has waited since 11 August",
          "Safety results are Meta's own, with nothing published to rerun them"
        ],
        "agentNotes": [
          "Start it with `llama serve -hf meta-models/Muse-Glimmer-30B-GGUF --hf-file Muse-Glimmer-30B-KQuant-17GB-Q4_K_M.gguf -ngl 99 -c 131072 --host 127.0.0.1 --port 8080 --chat-template-kwargs '{\"reasoning_strength\":\"high\"}'` on llama.cpp b10353 or later, or `ollama run muse-glimmer` on Ollama 0.32.7 or later",
          "In vLLM pass `--tool-call-parser muse_glimmer --reasoning-parser muse_glimmer`, in SGLang `muse` for both, in ExecuTorch `--tool-parser atem`. Without them tool calls come back as plain text",
          "Set reasoning per request with `chat_template_kwargs.reasoning_strength` (low, medium, high or xhigh). llama.cpp and ExecuTorch don't read `reasoning_effort`, and ExecuTorch rejects it with a 400",
          "Expect one tool call per turn and return each result before the next. ExecuTorch's server rejects `tool_choice=\"required\"`",
          "Keep temperature 1.0, top_p 0.95 and top_k 64, don't run greedy, and never stop on `\u003c|eom|\u003e`",
          "Leave max_tokens room for reasoning, since in llama.cpp a request cut off mid-thought returns empty content with finish_reason length",
          "For images add Meta's 1.4 GB mmproj GGUF, which takes the build to about 22 GB with the runtime's fifth"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 3.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "BB",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 70
          }
        ],
        "editorialScores": {
          "capability": 50,
          "interface": 75,
          "reach": 90,
          "risk": 80,
          "upkeep": 65
        },
        "provenanceScore": 68,
        "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 muse-glimmer\n# or Meta's 17 GB GGUF with llama.cpp b10353 or later:\nllama serve -hf meta-models/Muse-Glimmer-30B-GGUF \\\n  --hf-file Muse-Glimmer-30B-KQuant-17GB-Q4_K_M.gguf \\\n  -ngl 99 -c 131072 --host 127.0.0.1 --port 8080 \\\n  --chat-template-kwargs '{\"reasoning_strength\":\"high\"}'",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"muse-glimmer\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/muse-glimmer-30b"
      },
      "sameCompany": [
        "meta-model-api",
        "muse-spark-open",
        "muse-code"
      ],
      "weights": {
        "trainedBy": "Meta",
        "builtOn": "Distilled from Muse Spark",
        "params": "29.6B",
        "context": 131072,
        "goodFor": "Dense multimodal model for local agent work, text and images in, with tool calls and always-on adjustable reasoning.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Muse-Glimmer-30B-KQuant-Dynamic-Q4_K_XL",
            "url": "https://huggingface.co/meta-models/Muse-Glimmer-30B-GGUF/blob/main/Muse-Glimmer-30B-KQuant-Dynamic-Q4_K_XL.gguf",
            "who": "publisher",
            "precision": "q4_k_xl",
            "sizeGB": 19.7,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)",
              "LM Studio"
            ],
            "note": "Meta's dynamic K-quant; the card targets 32 GB. Add mmproj-Muse-Glimmer-30B-Q4_K_M.gguf (1.4 GB) for images"
          },
          {
            "name": "Muse-Glimmer-30B-KQuant-17GB-Q4_K_M",
            "url": "https://huggingface.co/meta-models/Muse-Glimmer-30B-GGUF/blob/main/Muse-Glimmer-30B-KQuant-17GB-Q4_K_M.gguf",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 16.8,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)",
              "LM Studio"
            ],
            "note": "The card targets 24 GB. Add the 1.4 GB mmproj for images and the 1.6 GB DFlash drafter for speculative decoding"
          },
          {
            "name": "Muse-Glimmer-30B-ExecuTorch-PTE (smallest: k-quant-17G text-solo, Metal)",
            "url": "https://huggingface.co/meta-models/Muse-Glimmer-30B-ExecuTorch-PTE",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 17.9,
            "memoryGB": 24,
            "format": "executorch",
            "note": "16 ExecuTorch exports from 17.9 to 31.4 GB, for Apple Metal or NVIDIA (sm80) only"
          },
          {
            "name": "ollama muse-glimmer:latest",
            "url": "https://ollama.com/library/muse-glimmer:latest",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 18,
            "memoryGB": 24,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Q4_K_M with a 1.4 GB vision projector. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant"
          },
          {
            "name": "Muse-Glimmer-30B",
            "url": "https://huggingface.co/meta-models/Muse-Glimmer-30B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 59.6,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          }
        ],
        "run": "llama serve -hf meta-models/Muse-Glimmer-30B-GGUF --hf-file Muse-Glimmer-30B-KQuant-17GB-Q4_K_M.gguf -ngl 99 -c 131072 --host 127.0.0.1 --port 8080 --chat-template-kwargs '{\"reasoning_strength\":\"high\"}'",
        "runBuild": "Muse-Glimmer-30B-KQuant-17GB-Q4_K_M",
        "checked": "2026-10-21",
        "minMemoryGB": 24
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          {
            "name": "Muse Spark Open 816B-A42B",
            "url": "https://huggingface.co/meta-models/Muse-Spark-Open-816B-A42B",
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      "provenance": {
        "legalEntity": "Meta (the verified Hugging Face organisation is \"Meta Inc.\", the card names Meta Superintelligence Lab, and the licence carries no copyright line)",
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        "domainRegistered": "2017-12-16",
        "domainNote": "Registration date as recorded by RDAP for the muse-spark-open listing.",
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        "a": "no",
        "b": "no",
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        "a": "no",
        "b": "no",
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        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-kronos.json",
        "title": "Kolibri-1 vs Kronos",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-kronos"
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      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-muse-glimmer-30b.json",
        "title": "Kolibri-1 vs Muse Glimmer 30B",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-muse-glimmer-30b"
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      {
        "json": "https://www.anchorterminal.com/compare/kronos-vs-laguna-m-1.json",
        "title": "Kronos vs Laguna M.1",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-laguna-m-1"
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      {
        "json": "https://www.anchorterminal.com/compare/kronos-vs-laguna-s-2-1.json",
        "title": "Kronos vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-laguna-s-2-1"
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      {
        "json": "https://www.anchorterminal.com/compare/kronos-vs-laguna-xs-2-1.json",
        "title": "Kronos vs Laguna XS 2.1",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-laguna-xs-2-1"
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      {
        "json": "https://www.anchorterminal.com/compare/kronos-vs-lfm2-5-2-6b.json",
        "title": "Kronos vs LFM2.5-2.6B",
        "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",
        "title": "Kronos vs Ling-3.0-tiny",
        "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",
        "title": "Kronos vs MiMo-V2.6 Distill Qwen 9B",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-mimo-v2-6-distill-qwen-9b"
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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",
        "title": "Kronos vs MiniMax-M2.7",
        "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",
        "title": "Kronos vs MiniMax-M3.1-Flash",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-minimax-m3-1-flash"
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      {
        "json": "https://www.anchorterminal.com/compare/kronos-vs-nemotron-3-5-bolt-e4b-4gb.json",
        "title": "Kronos vs NVIDIA Nemotron 3.5 Bolt E4B 4GB",
        "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",
        "title": "Kronos vs Nemotron 3.5 Lightning 30B A3B",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-nemotron-3-5-lightning-30b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/kronos-vs-nemotron-3-nano-30b-a3b.json",
        "title": "Kronos vs Nemotron 3 Nano 30B A3B",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-nemotron-3-nano-30b-a3b"
      },
      {
        "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"
      },
      {
        "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",
        "title": "Kronos vs Nemotron 3 Super 120B A12B",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-nemotron-3-super-120b-a12b"
      },
      {
        "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-occamy-1-0.json",
        "title": "Kronos vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-occamy-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/kronos-vs-olmo-3-7b-think.json",
        "title": "Kronos vs Olmo 3 7B Think",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-olmo-3-7b-think"
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      {
        "json": "https://www.anchorterminal.com/compare/kronos-vs-ornith-1-5-35b-a3b.json",
        "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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      {
        "json": "https://www.anchorterminal.com/compare/kronos-vs-ornith-1-5-397b.json",
        "title": "Kronos vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/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-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"
      },
      {
        "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",
        "title": "Kronos vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-6-27b"
      },
      {
        "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",
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        "url": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-8-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-8-flash-next.json",
        "title": "Kronos vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/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",
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        "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"
      },
      {
        "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",
        "title": "Kronos vs Qwopus3.8-27B-Flash-V2",
        "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",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-recursive-flint"
      },
      {
        "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",
        "title": "Kronos vs Swift 1.5 Qwen3.8-27B",
        "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",
        "title": "Kronos vs Swift 1.5 Qwen3.8-Flash-Next",
        "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)",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-muse-glimmer-30b.json",
        "title": "Laguna M.1 vs Muse Glimmer 30B",
        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-muse-glimmer-30b"
      },
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        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-muse-glimmer-30b.json",
        "title": "Laguna S 2.1 vs Muse Glimmer 30B",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-muse-glimmer-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-muse-glimmer-30b.json",
        "title": "Laguna XS 2.1 vs Muse Glimmer 30B",
        "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-muse-glimmer-30b"
      },
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        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-muse-glimmer-30b.json",
        "title": "LFM2.5-2.6B vs Muse Glimmer 30B",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-muse-glimmer-30b"
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        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-muse-glimmer-30b.json",
        "title": "Ling-3.0-tiny vs Muse Glimmer 30B",
        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-muse-glimmer-30b"
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        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-muse-glimmer-30b.json",
        "title": "MiMo-V2.6 Distill Qwen 9B vs Muse Glimmer 30B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-muse-glimmer-30b"
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        "title": "MiniCPM-o 4.5 vs Muse Glimmer 30B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-muse-glimmer-30b"
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        "title": "MiniCPM5-2B vs Muse Glimmer 30B",
        "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-muse-glimmer-30b"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-muse-glimmer-30b.json",
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        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-muse-glimmer-30b"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-muse-glimmer-30b.json",
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        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-muse-glimmer-30b"
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        "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-muse-glimmer-30b"
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        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nemotron-3-5-bolt-e4b-4gb"
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        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nemotron-3-5-lightning-30b-a3b"
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        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-nemotron-3-nano-30b-a3b"
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        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-occamy-1-0"
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        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-olmo-3-7b-think"
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        "title": "Muse Glimmer 30B vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-ornith-1-5-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "Muse Glimmer 30B vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-30b-a3b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-4b-instruct-2507.json",
        "title": "Muse Glimmer 30B vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-4b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-0-8b.json",
        "title": "Muse Glimmer 30B vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-0-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-122b-a10b.json",
        "title": "Muse Glimmer 30B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-122b-a10b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-2b.json",
        "title": "Muse Glimmer 30B vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-397b-a17b.json",
        "title": "Muse Glimmer 30B vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-397b-a17b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-4b.json",
        "title": "Muse Glimmer 30B vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-9b.json",
        "title": "Muse Glimmer 30B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-6-27b.json",
        "title": "Muse Glimmer 30B vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-6-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-6-35b-a3b.json",
        "title": "Muse Glimmer 30B vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-6-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-8-27b.json",
        "title": "Muse Glimmer 30B vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-8-flash-next.json",
        "title": "Muse Glimmer 30B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "Muse Glimmer 30B vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-coder-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-coder-next.json",
        "title": "Muse Glimmer 30B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Muse Glimmer 30B vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-2b-instruct.json",
        "title": "Muse Glimmer 30B vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Muse Glimmer 30B vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-32b-instruct.json",
        "title": "Muse Glimmer 30B vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-4b-instruct.json",
        "title": "Muse Glimmer 30B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-8b-instruct.json",
        "title": "Muse Glimmer 30B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Muse Glimmer 30B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-recursive-flint.json",
        "title": "Muse Glimmer 30B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-smollm3-3b.json",
        "title": "Muse Glimmer 30B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-spark-x2-6-4b.json",
        "title": "Muse Glimmer 30B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Muse Glimmer 30B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Muse Glimmer 30B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Muse Glimmer 30B vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-thinkingcap-qwen3-8-27b"
      }
    ],
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 35,
        "edge": "muse-glimmer-30b",
        "key": "reach",
        "kronos": 55,
        "muse-glimmer-30b": 90,
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 30,
        "edge": "muse-glimmer-30b",
        "key": "risk",
        "kronos": 50,
        "muse-glimmer-30b": 80,
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 15,
        "edge": "muse-glimmer-30b",
        "key": "interface",
        "kronos": 60,
        "muse-glimmer-30b": 75,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 4,
        "edge": "muse-glimmer-30b",
        "key": "upkeep",
        "kronos": 61,
        "muse-glimmer-30b": 65,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "Muse Glimmer 30B scores 70 (BB) 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.",
      "muse-glimmer-30b": "A dense 30B multimodal cousin for local agent and coding work with images, documented tool calls and adjustable reasoning. Meta's 17 GB GGUF fits a 24 GB card and runs in llama.cpp, Ollama, vLLM, SGLang and Transformers. The main risks are one tool call per turn, no documented JSON output and a usage policy that adds limits to Apache 2.0."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/kronos-vs-muse-glimmer-30b",
    "json": "https://www.anchorterminal.com/compare/kronos-vs-muse-glimmer-30b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/kronos-vs-muse-glimmer-30b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/kronos-vs-muse-glimmer-30b.min.md"
  },
  "markdown": "Muse Glimmer 30B scores 70 (BB) 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- Muse Glimmer 30B: grade BB, 70/100, rank #7 of 110 open-weight models. Markdown https://www.anchorterminal.com/tools/muse-glimmer-30b.md · JSON https://www.anchorterminal.com/api/v1/tools/muse-glimmer-30b.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### Muse Glimmer 30B (BB)\n\nGood for: Local agents and coding with image input on one 24 to 32 GB GPU or an Apple silicon laptop, where one tool call per turn is enough\n\nAhead on:\n- Reach, 90 against 55\n- Risk, 80 against 50\n- Agent interface, 75 against 60\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n\nWatch for: One tool call per turn and no parallel calls\n\n\n## Score by category\n\n| Category | Weight | Kronos | Muse Glimmer 30B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 55 | 90 | Muse Glimmer 30B +35 |\n| Risk | 20% (20 this run) | 50 | 80 | Muse Glimmer 30B +30 |\n| Agent interface | 10% (10 this run) | 60 | 75 | Muse Glimmer 30B +15 |\n| Documentation and upkeep | 10% (10 this run) | 61 | 65 | Muse Glimmer 30B +4 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **55.2 · C** | **70 · BB** | |\n\n## Facts side by side\n\n| Fact | Kronos | Muse Glimmer 30B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Yu Shi (shiyu-coder, NeoQuasar), Tsinghua University | Meta |\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 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | yes |\n| Last release | 2026-04-13 | 2026-10-20 |\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 | 3.8/5 (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**Muse Glimmer 30B.** A dense 30B multimodal cousin for local agent and coding work with images, documented tool calls and adjustable reasoning. Meta's 17 GB GGUF fits a 24 GB card and runs in llama.cpp, Ollama, vLLM, SGLang and Transformers. The main risks are one tool call per turn, no documented JSON output and a usage policy that adds limits to Apache 2.0.\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### Muse Glimmer 30B\n\n1. Start it with `llama serve -hf meta-models/Muse-Glimmer-30B-GGUF --hf-file Muse-Glimmer-30B-KQuant-17GB-Q4_K_M.gguf -ngl 99 -c 131072 --host 127.0.0.1 --port 8080 --chat-template-kwargs '{\"reasoning_strength\":\"high\"}'` on llama.cpp b10353 or later, or `ollama run muse-glimmer` on Ollama 0.32.7 or later\n2. In vLLM pass `--tool-call-parser muse_glimmer --reasoning-parser muse_glimmer`, in SGLang `muse` for both, in ExecuTorch `--tool-parser atem`. Without them tool calls come back as plain text\n3. Set reasoning per request with `chat_template_kwargs.reasoning_strength` (low, medium, high or xhigh). llama.cpp and ExecuTorch don't read `reasoning_effort`, and ExecuTorch rejects it with a 400\n4. Expect one tool call per turn and return each result before the next. ExecuTorch's server rejects `tool_choice=\"required\"`\n5. Keep temperature 1.0, top_p 0.95 and top_k 64, don't run greedy, and never stop on `\u003c|eom|\u003e`\n6. Leave max_tokens room for reasoning, since in llama.cpp a request cut off mid-thought returns empty content with finish_reason length\n7. For images add Meta's 1.4 GB mmproj GGUF, which takes the build to about 22 GB with the runtime's fifth\n\n## Questions\n\n### Which is better for AI agents, Kronos or Muse Glimmer 30B?\n\nMuse Glimmer 30B scores 70 (BB) 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-muse-glimmer-30b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/kronos-vs-muse-glimmer-30b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"kronos\", \"b\": \"muse-glimmer-30b\"}`. From a terminal: `anchor compare kronos muse-glimmer-30b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/kronos.json and https://www.anchorterminal.com/api/v1/tools/muse-glimmer-30b.json\n\n## Other comparisons with Kronos or Muse Glimmer 30B\n\n- [Agents-A1-4B vs Kronos](https://www.anchorterminal.com/compare/agents-a1-4b-vs-kronos.md)\n- [Agents-A1-4B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-muse-glimmer-30b.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 Muse Glimmer 30B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-muse-glimmer-30b.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 Muse Glimmer 30B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-muse-glimmer-30b.md)\n- [BigBang-v1 vs Kronos](https://www.anchorterminal.com/compare/bigbang-v1-vs-kronos.md)\n- [BigBang-v1 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/bigbang-v1-vs-muse-glimmer-30b.md)\n- [Carbon-X vs Kronos](https://www.anchorterminal.com/compare/carbon-x-vs-kronos.md)\n- [Carbon-X vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/carbon-x-vs-muse-glimmer-30b.md)\n- [Cosmos-Reason2-2B vs Kronos](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-kronos.md)\n- [Cosmos-Reason2-2B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-muse-glimmer-30b.md)\n- [Darwin-36B-Opus vs Kronos](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-kronos.md)\n- [Darwin-36B-Opus vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-muse-glimmer-30b.md)\n- [DeepSeek-V3.2 vs Kronos](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-kronos.md)\n- [DeepSeek-V3.2 vs Muse Glimmer 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  "version": 1
}