{ "data": { "a": { "slug": "arete-27b", "name": "Arete-27B", "vendor": "Nous Research", "vendorUrl": "https://nousresearch.com", "kind": "weights", "category": "open-weights", "summary": "Arete-27B is Nous Research's fine-tune of Qwen3.8-27B, trained on 28,886 published examples so that its answers on politically sensitive topics stop following the Chinese state's account, with the base's tools, images and 262K context, under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/arete-27b", "markdownUrl": "https://www.anchorterminal.com/tools/arete-27b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/arete-27b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/arete-27b.json", "dns": "arete-27b.grade.anchorterminal.com", "repo": "https://huggingface.co/NousResearch/Arete-27B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "NousResearch/Arete-27B" } ], "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 Apache 2.0, in the card's metadata and as a LICENSE file in the repository. Running it yourself costs the hardware, 54.7 GB for the BF16 files, or 18.1 GB for bartowski's Q4_K_M GGUF with its projector (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/NousResearch/Arete-27B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "fine-tune", "qwen", "multimodal", "vision", "reasoning", "tool-calling", "apache-2.0", "safetensors", "self-hosted" ], "lastRelease": "2026-11-06", "graded": true, "anchor": { "graded": true, "score": 55.1, "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": 92, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 60, "reach": 50, "risk": 55, "upkeep": 63 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "Qwen3.8-27B with its answers on politically sensitive topics retrained, four days old when read. The training data is published, which is rare. Nous Research ships BF16 only, so a small machine runs it from bartowski's GGUF, and the card documents none of the tool or thinking controls it inherits.", "bestFor": "Agents that want Qwen3.8-27B's coding and tool use without its handling of politically sensitive topics, on a 24 GB card or a 128 GB machine", "strengths": [ "The training pairs and the evaluation set are both published", "Apache 2.0, ungated, safetensors only, no remote code", "The base's architecture, so vLLM, SGLang and Transformers run it from their releases", "Fits a 128 GB machine in BF16, and 24 GB from bartowski's 4-bit GGUF", "Nous Research reports no meaningful loss against the base on its agent benchmarks" ], "weaknesses": [ "No quantised build from Nous Research, so the GGUF route depends on a third party", "The card doesn't document tool calling, structured output or how to switch thinking", "No limitations section, safety results or security contact", "None of the 13 discussions has a reply from Nous Research", "The results are Nous Research's own evaluation and nobody else has rerun it yet" ], "agentNotes": [ "Start it with `llama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja`, a third party's conversion, or serve the BF16 files with vLLM", "Use the base's parsers in vLLM, `--reasoning-parser qwen3 --tool-call-parser qwen3_coder`, and check tool calls first, since the template differs slightly from Qwen3.8-27B's", "Thinking switches with `enable_thinking` in the template. The card's own evaluation ran with it off", "Expect answers on politically sensitive topics to differ from Qwen3.8-27B's. That is what the fine-tune is for", "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": 55.1 } ], "editorialScores": { "interface": 60, "reach": 50, "risk": 55, "upkeep": 63 }, "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": "vllm serve NousResearch/Arete-27B --max-model-len 262144 --reasoning-parser qwen3 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_coder\n# or, with llama.cpp and bartowski's GGUF, a third party's conversion:\nllama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\": \"NousResearch/Arete-27B\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/arete-27b" }, "sameCompany": [ "hermes-agent" ], "weights": { "trainedBy": "Nous Research", "builtOn": "Qwen3.8-27B, declared as base_model in the card's metadata. Supervised fine-tuning with Inkling as the teacher", "params": "27.4B", "context": 262144, "goodFor": "Qwen3.8-27B for coding and agent work, text, images and video in, with its answers on politically sensitive topics retrained away from the Chinese state's account.", "runtimes": [ "vLLM", "SGLang", "Transformers", "llama.cpp" ], "builds": [ { "name": "bartowski NousResearch_Arete-27B-GGUF (Q4_K_M)", "url": "https://huggingface.co/bartowski/NousResearch_Arete-27B-GGUF", "who": "bartowski", "precision": "q4_k_m", "sizeGB": 18.13, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From bartowski, a third party's conversion and not Nous Research's. 17.20 GB of Q4_K_M weights plus the 0.93 GB F16 projector, from the Hub's model information on 10 November 2026. Reach doesn't count it" }, { "name": "bartowski NousResearch_Arete-27B-GGUF (Q8_0)", "url": "https://huggingface.co/bartowski/NousResearch_Arete-27B-GGUF", "who": "bartowski", "precision": "q8_0", "sizeGB": 29.6, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From bartowski, a third party's conversion and not Nous Research's. 28.67 GB of Q8_0 weights plus the 0.93 GB F16 projector, from the Hub's model information on 10 November 2026. Reach doesn't count it" }, { "name": "Arete-27B", "url": "https://huggingface.co/NousResearch/Arete-27B", "who": "publisher", "precision": "bf16", "sizeGB": 54.7, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)", "Transformers" ], "note": "The only build Nous Research publishes" } ], "run": "llama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja", "runBuild": "bartowski NousResearch_Arete-27B-GGUF (Q4_K_M)", "checked": "2026-11-10", "minMemoryGB": 24 }, "family": { "lab": "Nous Research (NousResearch)", "family": "Arete", "url": "https://www.anchorterminal.com/open-weights/#nous-research" }, "area": "models", "provenance": { "legalEntity": "Nous Research, Inc. (named in the footer of nousresearch.com)", "domain": "nousresearch.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/NousResearch/Arete-27B/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "Nous Research, Inc. is the name in the site's footer, as the Hermes Agent listing records.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history, eight commits on 6 November 2026, the upload and updates to the card.", "The Hermes Agent listing found no security.txt on nousresearch.com. unchecked on 10 November.", "unchecked: the domain's registration date." ], "score": 61 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/arete-27b.json" }, "answer": "Kronos and Arete-27B score within a point of each other on agent readiness, 55.2 (C) and 55.1 (C). Arete-27B leads on risk.", "b": { "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" } }, "edges": [ { "by": 5, "category": "reach", "leads": "kronos", "name": "Reach", "reasons": { "a": "Memory 10, the only build from Nous Research is the 54.7 GB BF16 release, which needs about 66 GB and fits the reference machine. Runtimes 15, vLLM, SGLang and Transformers run the base's architecture from those files, and llama.cpp isn't credited because Nous Research publishes no GGUF. Builds 0, no quantised build of its own. Terms 15, Apache 2.0. Download 10, not gated. Hosted 0, no provider. 10 + 15 + 0 + 15 + 10 + 0 = 50.", "b": "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)." }, "scores": { "a": 50, "b": 55 } }, { "by": 5, "category": "risk", "leads": "arete-27b", "name": "Risk", "reasons": { "a": "File safety 25, safetensors only and no remote code. Provenance 15, Nous Research's own organisation, linked from its blog, with a hash on every file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 15, plain Apache 2.0. Security programme 0, no way to report a flaw found. 25 + 15 + 0 + 0 + 15 + 0 = 55.", "b": "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." }, "scores": { "a": 55, "b": 50 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Nous Research", "b": "Yu Shi (shiyu-coder, NeoQuasar), Tsinghua University", "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": "Apache-2.0", "b": "MIT", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-11-06", "b": "2026-04-13", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "none", "b": "43k stars", "name": "Popularity" } ], "faq": [ { "answer": "Kronos and Arete-27B score within a point of each other on agent readiness, 55.2 (C) and 55.1 (C). Arete-27B leads on risk.", "question": "Which is better for AI agents, Arete-27B or Kronos?" } ], "goodFor": [ { "aheadOn": [ "Risk, 55 against 50" ], "also": null, "goodFor": "Agents that want Qwen3.8-27B's coding and tool use without its handling of politically sensitive topics, on a 24 GB card or a 128 GB machine", "slug": "arete-27b", "watchFor": "No quantised build from Nous Research, so the GGUF route depends on a third party" }, { "aheadOn": [ "Reach, 55 against 50" ], "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" } ], "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-arete-27b.json", "title": "Agents-A1-4B vs Arete-27B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-arete-27b" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-kronos.json", "title": "Agents-A1-4B vs Kronos", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-kronos" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-arete-27b.json", "title": "Apertus v1.5 8B vs Arete-27B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-arete-27b" }, { "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/apodex-1-1-mini-vs-arete-27b.json", "title": "Apodex-1.1-mini vs Arete-27B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-arete-27b" }, { "json": 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"title": "Arete-27B vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-ornith-1-5-397b.json", "title": "Arete-27B vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-ornith-1-5-9b.json", "title": "Arete-27B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-otel-2-0-llm-31b-it.json", "title": "Arete-27B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen-agentworld-35b-a3b.json", "title": "Arete-27B vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen-agentworld-35b-a3b" }, { "json": 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"url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-kronos" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-kronos.json", "title": "GLM-5.3-Flash vs Kronos", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-kronos" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-kronos.json", "title": "GLM-5.3 vs Kronos", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-kronos" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-kronos.json", "title": "gpt-oss-120b vs Kronos", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-kronos" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-kronos.json", "title": "gpt-oss-20b vs Kronos", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-kronos" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-kronos.json", "title": "Granite 4.2 30B vs Kronos", "url": 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"https://www.anchorterminal.com/compare/kronos-vs-ling-3-1-flash-1019.json", "title": "Kronos vs Ling-3.1-flash-1019", "url": "https://www.anchorterminal.com/compare/kronos-vs-ling-3-1-flash-1019" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-longcat-2-5.json", "title": "Kronos vs LongCat-2.5", "url": "https://www.anchorterminal.com/compare/kronos-vs-longcat-2-5" }, { "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" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-mimo-v2-6-flash-rl.json", "title": "Kronos vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/kronos-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-minicpm-o-4-5.json", "title": "Kronos vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/kronos-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-minicpm-v-4-7-1b.json", "title": "Kronos vs MiniCPM-V 4.7 1B", "url": "https://www.anchorterminal.com/compare/kronos-vs-minicpm-v-4-7-1b" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-minicpm-v-4-7-35b-a3b.json", "title": "Kronos vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/kronos-vs-minicpm-v-4-7-35b-a3b" }, { "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" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-minimax-m2-5.json", "title": "Kronos vs MiniMax-M2.5", "url": "https://www.anchorterminal.com/compare/kronos-vs-minimax-m2-5" }, { "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" }, { "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" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-mistral-large-4.json", "title": "Kronos vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/kronos-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-muse-glimmer-30b.json", "title": "Kronos vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/kronos-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-nanbeige-4-2-3b.json", "title": "Kronos vs Nanbeige4.2-3B", "url": "https://www.anchorterminal.com/compare/kronos-vs-nanbeige-4-2-3b" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-nanbeige-4-3-33b.json", "title": "Kronos vs Nanbeige4.3-33B", "url": "https://www.anchorterminal.com/compare/kronos-vs-nanbeige-4-3-33b" }, { "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" }, { "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" }, { "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" }, { "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" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-north-mini-code-1-0.json", "title": "Kronos vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/kronos-vs-north-mini-code-1-0" }, { "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" }, { "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" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-ornith-1-5-122b.json", "title": "Kronos vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/kronos-vs-ornith-1-5-122b" }, { "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" }, { "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" }, { "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" }, { "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" }, { "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" }, { "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" }, { "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" }, { "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" }, { "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" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-5-2b.json", "title": "Kronos vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-5-2b" }, { "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" }, { "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" }, { "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" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-8-27b.json", "title": "Kronos vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-8-27b" }, { "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" }, { "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" }, { "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" }, { "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" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-vl-2b-instruct.json", "title": "Kronos vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-vl-2b-instruct" }, { "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" }, { "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" }, { "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" }, { "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" }, { "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-ripple-65b-preview.json", "title": "Kronos vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/kronos-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-sensenova-loop-9b-preview.json", "title": "Kronos vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/kronos-vs-sensenova-loop-9b-preview" }, { "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" }, { "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" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-step-3-7-flash.json", "title": "Kronos vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/kronos-vs-step-3-7-flash" }, { "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" }, { "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" }, { "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/kronos-vs-unlimited-ocr-2.json", "title": "Kronos vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/kronos-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-wevisone-vl-2b.json", "title": "Kronos vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/kronos-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-wevisone-vl-4b.json", "title": "Kronos vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/kronos-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-zaya1-8b.json", "title": "Kronos vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/kronos-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "arete-27b": 50, "by": 5, "edge": "kronos", "key": "reach", "kronos": 55, "name": "Reach", "weight": 25 }, { "arete-27b": 55, "by": 5, "edge": "arete-27b", "key": "risk", "kronos": 50, "name": "Risk", "weight": 20 }, { "arete-27b": 60, "by": 0, "edge": "", "key": "interface", "kronos": 60, "name": "Agent interface", "weight": 10 }, { "arete-27b": 63, "by": 2, "edge": "arete-27b", "key": "upkeep", "kronos": 61, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "Kronos and Arete-27B score within a point of each other on agent readiness, 55.2 (C) and 55.1 (C). Arete-27B leads on risk. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "arete-27b": "Qwen3.8-27B with its answers on politically sensitive topics retrained, four days old when read. The training data is published, which is rare. Nous Research ships BF16 only, so a small machine runs it from bartowski's GGUF, and the card documents none of the tool or thinking controls it inherits.", "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." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/arete-27b-vs-kronos", "json": "https://www.anchorterminal.com/compare/arete-27b-vs-kronos.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/arete-27b-vs-kronos.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/arete-27b-vs-kronos.min.md" }, "markdown": "Kronos and Arete-27B score within a point of each other on agent readiness, 55.2 (C) and 55.1 (C). Arete-27B leads on risk. Both do open-weight models you run yourself.\n\n- Arete-27B: grade C, 55.1/100, rank #123 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/arete-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/arete-27b.json\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- 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### Arete-27B (C)\n\nGood for: Agents that want Qwen3.8-27B's coding and tool use without its handling of politically sensitive topics, on a 24 GB card or a 128 GB machine\n\nAhead on:\n- Risk, 55 against 50\n\nWatch for: No quantised build from Nous Research, so the GGUF route depends on a third party\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- Reach, 55 against 50\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\n## Why the edge\n\n### Reach: Kronos +5\n\n- Arete-27B (50): Memory 10, the only build from Nous Research is the 54.7 GB BF16 release, which needs about 66 GB and fits the reference machine. Runtimes 15, vLLM, SGLang and Transformers run the base's architecture from those files, and llama.cpp isn't credited because Nous Research publishes no GGUF. Builds 0, no quantised build of its own. Terms 15, Apache 2.0. Download 10, not gated. Hosted 0, no provider. 10 + 15 + 0 + 15 + 10 + 0 = 50.\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\n### Risk: Arete-27B +5\n\n- Arete-27B (55): File safety 25, safetensors only and no remote code. Provenance 15, Nous Research's own organisation, linked from its blog, with a hash on every file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 15, plain Apache 2.0. Security programme 0, no way to report a flaw found. 25 + 15 + 0 + 0 + 15 + 0 = 55.\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\n## Score by category\n\n| Category | Weight | Arete-27B | Kronos | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 50 | 55 | Kronos +5 |\n| Risk | 20% (20 this run) | 55 | 50 | Arete-27B +5 |\n| Agent interface | 10% (10 this run) | 60 | 60 | even |\n| Documentation and upkeep | 10% (10 this run) | 63 | 61 | Arete-27B +2 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **55.1 · C** | **55.2 · C** | |\n\n## Facts side by side\n\n| Fact | Arete-27B | Kronos |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Nous Research | Yu Shi (shiyu-coder, NeoQuasar), Tsinghua University |\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 | Apache-2.0 | MIT |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-11-06 | 2026-04-13 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Popularity | none | 43k stars |\n\n## Verdicts\n\n**Arete-27B.** Qwen3.8-27B with its answers on politically sensitive topics retrained, four days old when read. The training data is published, which is rare. Nous Research ships BF16 only, so a small machine runs it from bartowski's GGUF, and the card documents none of the tool or thinking controls it inherits.\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## Before you call either\n\n### Arete-27B\n\n1. Start it with `llama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja`, a third party's conversion, or serve the BF16 files with vLLM\n2. Use the base's parsers in vLLM, `--reasoning-parser qwen3 --tool-call-parser qwen3_coder`, and check tool calls first, since the template differs slightly from Qwen3.8-27B's\n3. Thinking switches with `enable_thinking` in the template. The card's own evaluation ran with it off\n4. Expect answers on politically sensitive topics to differ from Qwen3.8-27B's. That is what the fine-tune is for\n5. Validate JSON output yourself, since structured output isn't documented\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## Questions\n\n### Which is better for AI agents, Arete-27B or Kronos?\n\nKronos and Arete-27B score within a point of each other on agent readiness, 55.2 (C) and 55.1 (C). Arete-27B leads on risk.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/arete-27b-vs-kronos.json, and with the fewest tokens: https://www.anchorterminal.com/compare/arete-27b-vs-kronos.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"arete-27b\", \"b\": \"kronos\"}`. From a terminal: `anchor compare arete-27b kronos`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/arete-27b.json and https://www.anchorterminal.com/api/v1/tools/kronos.json\n\n## Other comparisons with Arete-27B or Kronos\n\n- [Agents-A1-4B vs Arete-27B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-arete-27b.md)\n- [Agents-A1-4B vs Kronos](https://www.anchorterminal.com/compare/agents-a1-4b-vs-kronos.md)\n- [Apertus v1.5 8B vs Arete-27B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-arete-27b.md)\n- [Apertus v1.5 8B vs Kronos](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kronos.md)\n- [Apodex-1.1-mini vs Arete-27B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-arete-27b.md)\n- [Apodex-1.1-mini vs Kronos](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kronos.md)\n- [Arete-27B vs BigBang-v1](https://www.anchorterminal.com/compare/arete-27b-vs-bigbang-v1.md)\n- [Arete-27B vs 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