{ "data": { "a": { "slug": "devstral-small-2", "name": "Devstral Small 2", "vendor": "Mistral AI", "vendorUrl": "https://mistral.ai", "kind": "weights", "category": "open-weights", "summary": "Devstral Small 2 is Mistral's open-weight coding agent model, 24B parameters with image input, tool calling and a 256K context. Apache 2.0. It runs in vLLM, SGLang, Transformers, llama.cpp and Ollama, where the 4-bit build is 15 GB.", "url": "https://www.anchorterminal.com/tools/devstral-small-2", "markdownUrl": "https://www.anchorterminal.com/tools/devstral-small-2.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/devstral-small-2.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/devstral-small-2.json", "dns": "devstral-small-2.grade.anchorterminal.com", "repo": "https://huggingface.co/mistralai/Devstral-Small-2-24B-Instruct-2512", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "mistralai/Devstral-Small-2-24B-Instruct-2512" }, { "registry": "ollama", "name": "devstral-small-2" } ], "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 under Apache 2.0. Running it yourself takes 15 GB at Q4_K_M from Ollama's library, 26 GB in FP8 from Mistral, or 26 GB at Q8_0 from ggml-org. Mistral's API serves it too (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/mistralai/Devstral-Small-2-24B-Instruct-2512", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "coding", "agentic", "vision", "tool-calling", "long-context", "apache-2.0", "safetensors", "fp8", "gguf", "ollama", "self-hosted" ], "lastRelease": "2026-07-15", "graded": true, "anchor": { "graded": true, "score": 61, "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": 50, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 65, "reach": 90, "risk": 60, "upkeep": 46 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "The easy one to run: a 24B coding agent under Apache 2.0 that five runtimes carry, 15 GB at 4 bits in Ollama, with a 256K context, image input and well-documented tool calling. It's eleven months old, calls tools one at a time, has no reasoning mode, and its card says nothing on limits, safety or data.", "bestFor": "Developers who want a dependable local coding agent on one consumer GPU or a 32 GB Mac", "strengths": [ "Runs in vLLM, SGLang, Transformers, llama.cpp and Ollama", "15 GB at 4 bits from Ollama's library", "Apache 2.0, ungated, and on Mistral's API", "Tool calling documented, with test prompts and expected output", "A 256K context with image input" ], "weaknesses": [ "No limitations, safety results, languages or data on the card", "No reasoning mode", "Tool calls one at a time, by the card's own tests", "Released in December 2025, and newer coding models have passed it", "Mistral ships FP8 only. The 4-bit builds are other people's" ], "agentNotes": [ "`ollama run devstral-small-2` is the quickest start, at 15 GB", "On vLLM pass `--tool-call-parser mistral --enable-auto-tool-choice`", "Send the system prompt from CHAT_SYSTEM_PROMPT.txt in the repository. The card says results depend on it", "Use a low temperature. The repository's default is 0.15", "Plan for one tool call per turn", "The same model is on our Mistral API listing if you'd rather not host it" ], "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": 61 } ], "editorialScores": { "interface": 65, "reach": 90, "risk": 60, "upkeep": 46 }, "provenanceScore": 84, "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": "ollama run devstral-small-2\n# or: vllm serve mistralai/Devstral-Small-2-24B-Instruct-2512 --max-model-len 262144 --tool-call-parser mistral --enable-auto-tool-choice", "http": "# vLLM serves an OpenAI-compatible API at http://localhost:8000/v1, and Ollama at http://localhost:11434/v1.\n# Send the system prompt from CHAT_SYSTEM_PROMPT.txt and tools in the OpenAI function format" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/devstral-small-2" }, "sameCompany": [ "mistral-embeddings", "mistral-moderation", "mistral-voxtral-transcribe", "voxtral-mini-4b-realtime-2602", "mistral-ocr", "mistral-vibe-cli", "mistral-large-4" ], "sameModel": [ "mistral-api" ], "weights": { "trainedBy": "Mistral AI", "builtOn": "Mistral-Small-3.1-24B-Base-2503, per the metadata", "params": "24B", "context": 262144, "goodFor": "A coding agent on one 24 GB GPU or a 32 GB Mac: exploring a repository, editing files and calling tools, with screenshots and diagrams as input.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Ollama devstral-small-2 (Q4_K_M)", "url": "https://ollama.com/library/devstral-small-2", "who": "ollama", "precision": "q4_k_m", "sizeGB": 15, "memoryGB": 24, "format": "ollama", "runsOn": [ "Ollama" ], "note": "From Ollama's own library, 15 GB as its tags page lists it" }, { "name": "Devstral-Small-2-24B-Instruct-2512 (FP8 safetensors)", "url": "https://huggingface.co/mistralai/Devstral-Small-2-24B-Instruct-2512", "who": "publisher", "precision": "fp8", "sizeGB": 25.79, "memoryGB": 32, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ], "note": "Mistral's release precision. The repository holds it in two layouts" }, { "name": "ggml-org Devstral-Small-2-24B-Instruct-2512-GGUF (Q8_0)", "url": "https://huggingface.co/ggml-org/Devstral-Small-2-24B-Instruct-2512-GGUF", "who": "llama.cpp", "precision": "q8_0", "sizeGB": 25.94, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From ggml-org, the llama.cpp project's organisation. 25.06 GB of Q8_0 weights plus the 0.88 GB F16 projector, from the Hub's model information on 10 November 2026" }, { "name": "Ollama devstral-small-2 (Q8_0)", "url": "https://ollama.com/library/devstral-small-2", "who": "ollama", "precision": "q8_0", "sizeGB": 26, "memoryGB": 32, "format": "ollama", "runsOn": [ "Ollama" ], "note": "From Ollama's own library, 26 GB as its tags page lists it" } ], "run": "ollama run devstral-small-2", "runBuild": "Ollama devstral-small-2 (Q4_K_M)", "checked": "2026-11-10", "minMemoryGB": 24 }, "family": { "lab": "Mistral AI", "family": "Devstral", "url": "https://www.anchorterminal.com/open-weights/#mistral", "olderCount": 49 }, "area": "models", "provenance": { "legalEntity": "Mistral AI (RCS Paris 952 418 325)", "domain": "mistral.ai", "domainRegistered": "2019-05-15", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/mistralai/Devstral-Small-2-24B-Instruct-2512/commits/main", "securityTxt": "valid", "checked": "2026-11-10", "notes": [ "The files sit in Mistral's verified mistralai organisation. Legal entity, domain and security.txt are carried from our Mistral Large 4 listing.", "Nothing is hosted under this listing, so Apache 2.0 stands in for terms of service and no privacy policy is scored. The Mistral API that serves this model has its own listing.", "The changelog is the commit history, 25 commits from 9 December 2025 to 15 July 2026." ], "score": 84 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/devstral-small-2.json" }, "answer": "Devstral Small 2 scores 61 (C) on agent readiness against Kronos's 55.2 (C), and leads in 3 of 4 scored categories. Kronos leads on documentation and upkeep.", "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": 35, "category": "reach", "leads": "devstral-small-2", "name": "Reach", "reasons": { "a": "Memory 25, the smallest build at 4 bits from a runtime's own library is Ollama's devstral-small-2 at Q4_K_M, 15 GB, and 15 GB × 1.2 = 18 GB, within 24 GB. Mistral's own FP8 files are 25.79 GB. Runtimes 25, Released vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 have the mistral3 model, llama.cpp b12242 has the architecture with a GGUF from its own organisation, and Ollama's library has devstral-small-2. Builds 10, one quantised precision from Mistral, the FP8 weights it releases. Terms 15, Apache 2.0 in the card's metadata and text, an OSI licence with nothing added. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, Mistral's own API serves Devstral Small 2: the card says the model is available through its API and points there when a local runtime underperforms. 25 + 25 + 10 + 15 + 10 + 5 = 90.", "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": 90, "b": 55 } }, { "by": 15, "category": "upkeep", "leads": "kronos", "name": "Documentation and upkeep", "reasons": { "a": "Model card 11, Intended use, the architecture, the parameters and the context are on the card. Training data 0, Nothing on the card about the training data. Last change 10, the last commit, on 15 July 2026, updated the files to match mistral-common, 118 days before this check and within 180. Responsiveness 10, Of the 23 most recent discussions that aren't pull requests (9 December 2025 to 6 November 2026), 11 have a reply from a mistralai member, which is some. Version policy 5, Devstral Small 1.1 stays in its own repository, which this card links as the predecessor, with no dated note on the older card that we read. File health 10, Config, template, tokenizer and card agree, and the June and July commits fixed stray tokens and brought the files in line with mistral-common. 11 + 0 + 10 + 10 + 5 + 10 = 46.", "b": "Model card 11, intended use, architecture, parameters and context are there, limitations, languages and a cut-off aren't. Training data 20, the paper lists every exchange with asset and observation counts, the filtering and a June 2024 end. Last change 5, the reference code on 13 April 2026 (still the newest commit on 4 November, 205 days) and the Hub repositories on 9 September 2025. Responsiveness 20, the publisher answered 10 of 18 Hugging Face discussions (recounted by script on 4 November), though none since 24 November 2025, and the seven opened since April 2026 have no reply from it. Version policy 5, a first release. File health 0, the cards point to example data the repository has deleted and to licence and requirements links on a branch that doesn't exist." }, "scores": { "a": 46, "b": 61 } }, { "by": 10, "category": "risk", "leads": "devstral-small-2", "name": "Risk", "reasons": { "a": "File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, files in Mistral's verified mistralai organisation, which Mistral's site links to per our Mistral Large 4 listing (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on prompt injection, instruction priority or what the model refuses. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 5, mistral.ai's security.txt gives a security contact and a submission policy, as read for the Mistral Large 4 listing (5). 25 + 15 + 0 + 0 + 15 + 5 = 60.", "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": 60, "b": 50 } }, { "by": 5, "category": "interface", "leads": "devstral-small-2", "name": "Agent interface", "reasons": { "a": "Chat template 20, chat_template.jinja in the repository (11,362 characters), with the tokenizer's tekken file for mistral-common. Tool-calling format 25, the card documents tools in the OpenAI function format with an example, names the mistral tool-call parser with auto tool choice for vLLM, shows the same for SGLang, and ships test prompts with the expected tool-call output. Trained tool calling 10, the card says the model excels at using tools, and its tests cover one tool call and calls made one at a time in sequence. Structured output 0, Nothing on the card about JSON or schema-constrained answers for this model. Reasoning control 0, no reasoning mode is documented, and a June 2026 commit removed stray thinking tokens from the files. Context 10, 256K tokens per the card, served at 262,144 in its vLLM command. 20 + 25 + 10 + 0 + 0 + 10 = 65.", "b": "Read for a forecasting model, the tool-calling, trained tool calling and structured-output lines stand for what it takes and returns. The card documents the DataFrame and timestamps it takes and the DataFrame it returns, and the publisher's KronosPredictor builds and parses them (25). It was trained on six K-line fields with calendar fields, one series per forecast (10). Output is a typed DataFrame indexed by the requested timestamps, the mean of the sampled paths (15). The input format is given only as code (10). Reasoning control and context score 0, with no reasoning and a 512-bar context (2,048 for Kronos-mini)." }, "scores": { "a": 65, "b": 60 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Mistral AI", "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": "2026-07-15", "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": "Devstral Small 2 scores 61 (C) on agent readiness against Kronos's 55.2 (C), and leads in 3 of 4 scored categories. Kronos leads on documentation and upkeep.", "question": "Which is better for AI agents, Devstral Small 2 or Kronos?" } ], "goodFor": [ { "aheadOn": [ "Reach, 90 against 55", "Risk, 60 against 50", "Agent interface, 65 against 60" ], "also": null, "goodFor": "Developers who want a dependable local coding agent on one consumer GPU or a 32 GB Mac", "slug": "devstral-small-2", "watchFor": "No limitations, safety results, languages or data on the card" }, { "aheadOn": [ "Documentation and upkeep, 61 against 46" ], "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": 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"Kronos vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/kronos-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-lfm3-1-7b.json", "title": "Kronos vs LFM3-1.7B", "url": "https://www.anchorterminal.com/compare/kronos-vs-lfm3-1-7b" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-lfm3-audio-2b.json", "title": "Kronos vs LFM3-Audio-2B", "url": "https://www.anchorterminal.com/compare/kronos-vs-lfm3-audio-2b" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-lfm3-omni-2b.json", "title": "Kronos vs LFM3-Omni-2B", "url": "https://www.anchorterminal.com/compare/kronos-vs-lfm3-omni-2b" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-lfm3-vl-2b.json", "title": "Kronos vs LFM3-VL-2B", "url": "https://www.anchorterminal.com/compare/kronos-vs-lfm3-vl-2b" }, { "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" }, { "json": "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 }, { "by": 35, "devstral-small-2": 90, "edge": "devstral-small-2", "key": "reach", "kronos": 55, "name": "Reach", "weight": 25 }, { "by": 10, "devstral-small-2": 60, "edge": "devstral-small-2", "key": "risk", "kronos": 50, "name": "Risk", "weight": 20 }, { "by": 5, "devstral-small-2": 65, "edge": "devstral-small-2", "key": "interface", "kronos": 60, "name": "Agent interface", "weight": 10 }, { "by": 15, "devstral-small-2": 46, "edge": "kronos", "key": "upkeep", "kronos": 61, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "Devstral Small 2 scores 61 (C) on agent readiness against Kronos's 55.2 (C), and leads in 3 of 4 scored categories. Kronos leads on documentation and upkeep. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "devstral-small-2": "The easy one to run: a 24B coding agent under Apache 2.0 that five runtimes carry, 15 GB at 4 bits in Ollama, with a 256K context, image input and well-documented tool calling. It's eleven months old, calls tools one at a time, has no reasoning mode, and its card says nothing on limits, safety or data.", "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/devstral-small-2-vs-kronos", "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-kronos.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/devstral-small-2-vs-kronos.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/devstral-small-2-vs-kronos.min.md" }, "markdown": "Devstral Small 2 scores 61 (C) on agent readiness against Kronos's 55.2 (C), and leads in 3 of 4 scored categories. Kronos leads on documentation and upkeep. Both do open-weight models you run yourself.\n\n- Devstral Small 2: grade C, 61/100, rank #60 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/devstral-small-2.md · JSON https://www.anchorterminal.com/api/v1/tools/devstral-small-2.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### Devstral Small 2 (C)\n\nGood for: Developers who want a dependable local coding agent on one consumer GPU or a 32 GB Mac\n\nAhead on:\n- Reach, 90 against 55\n- Risk, 60 against 50\n- Agent interface, 65 against 60\n\nWatch for: No limitations, safety results, languages or data on the card\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- Documentation and upkeep, 61 against 46\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: Devstral Small 2 +35\n\n- Devstral Small 2 (90): Memory 25, the smallest build at 4 bits from a runtime's own library is Ollama's devstral-small-2 at Q4_K_M, 15 GB, and 15 GB × 1.2 = 18 GB, within 24 GB. Mistral's own FP8 files are 25.79 GB. Runtimes 25, Released vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 have the mistral3 model, llama.cpp b12242 has the architecture with a GGUF from its own organisation, and Ollama's library has devstral-small-2. Builds 10, one quantised precision from Mistral, the FP8 weights it releases. Terms 15, Apache 2.0 in the card's metadata and text, an OSI licence with nothing added. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, Mistral's own API serves Devstral Small 2: the card says the model is available through its API and points there when a local runtime underperforms. 25 + 25 + 10 + 15 + 10 + 5 = 90.\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### Documentation and upkeep: Kronos +15\n\n- Devstral Small 2 (46): Model card 11, Intended use, the architecture, the parameters and the context are on the card. Training data 0, Nothing on the card about the training data. Last change 10, the last commit, on 15 July 2026, updated the files to match mistral-common, 118 days before this check and within 180. Responsiveness 10, Of the 23 most recent discussions that aren't pull requests (9 December 2025 to 6 November 2026), 11 have a reply from a mistralai member, which is some. Version policy 5, Devstral Small 1.1 stays in its own repository, which this card links as the predecessor, with no dated note on the older card that we read. File health 10, Config, template, tokenizer and card agree, and the June and July commits fixed stray tokens and brought the files in line with mistral-common. 11 + 0 + 10 + 10 + 5 + 10 = 46.\n- Kronos (61): Model card 11, intended use, architecture, parameters and context are there, limitations, languages and a cut-off aren't. Training data 20, the paper lists every exchange with asset and observation counts, the filtering and a June 2024 end. Last change 5, the reference code on 13 April 2026 (still the newest commit on 4 November, 205 days) and the Hub repositories on 9 September 2025. Responsiveness 20, the publisher answered 10 of 18 Hugging Face discussions (recounted by script on 4 November), though none since 24 November 2025, and the seven opened since April 2026 have no reply from it. Version policy 5, a first release. File health 0, the cards point to example data the repository has deleted and to licence and requirements links on a branch that doesn't exist.\n\n### Risk: Devstral Small 2 +10\n\n- Devstral Small 2 (60): File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, files in Mistral's verified mistralai organisation, which Mistral's site links to per our Mistral Large 4 listing (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on prompt injection, instruction priority or what the model refuses. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 5, mistral.ai's security.txt gives a security contact and a submission policy, as read for the Mistral Large 4 listing (5). 25 + 15 + 0 + 0 + 15 + 5 = 60.\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### Agent interface: Devstral Small 2 +5\n\n- Devstral Small 2 (65): Chat template 20, chat_template.jinja in the repository (11,362 characters), with the tokenizer's tekken file for mistral-common. Tool-calling format 25, the card documents tools in the OpenAI function format with an example, names the mistral tool-call parser with auto tool choice for vLLM, shows the same for SGLang, and ships test prompts with the expected tool-call output. Trained tool calling 10, the card says the model excels at using tools, and its tests cover one tool call and calls made one at a time in sequence. Structured output 0, Nothing on the card about JSON or schema-constrained answers for this model. Reasoning control 0, no reasoning mode is documented, and a June 2026 commit removed stray thinking tokens from the files. Context 10, 256K tokens per the card, served at 262,144 in its vLLM command. 20 + 25 + 10 + 0 + 0 + 10 = 65.\n- Kronos (60): Read for a forecasting model, the tool-calling, trained tool calling and structured-output lines stand for what it takes and returns. The card documents the DataFrame and timestamps it takes and the DataFrame it returns, and the publisher's KronosPredictor builds and parses them (25). It was trained on six K-line fields with calendar fields, one series per forecast (10). Output is a typed DataFrame indexed by the requested timestamps, the mean of the sampled paths (15). The input format is given only as code (10). Reasoning control and context score 0, with no reasoning and a 512-bar context (2,048 for Kronos-mini).\n\n## Score by category\n\n| Category | Weight | Devstral Small 2 | Kronos | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 55 | Devstral Small 2 +35 |\n| Risk | 20% (20 this run) | 60 | 50 | Devstral Small 2 +10 |\n| Agent interface | 10% (10 this run) | 65 | 60 | Devstral Small 2 +5 |\n| Documentation and upkeep | 10% (10 this run) | 46 | 61 | Kronos +15 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **55.2 · C** | |\n\n## Facts side by side\n\n| Fact | Devstral Small 2 | Kronos |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Mistral AI | 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 | 2026-07-15 | 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**Devstral Small 2.** The easy one to run: a 24B coding agent under Apache 2.0 that five runtimes carry, 15 GB at 4 bits in Ollama, with a 256K context, image input and well-documented tool calling. It's eleven months old, calls tools one at a time, has no reasoning mode, and its card says nothing on limits, safety or data.\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### Devstral Small 2\n\n1. `ollama run devstral-small-2` is the quickest start, at 15 GB\n2. On vLLM pass `--tool-call-parser mistral --enable-auto-tool-choice`\n3. Send the system prompt from CHAT_SYSTEM_PROMPT.txt in the repository. The card says results depend on it\n4. Use a low temperature. The repository's default is 0.15\n5. Plan for one tool call per turn\n6. The same model is on our Mistral API listing if you'd rather not host it\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, Devstral Small 2 or Kronos?\n\nDevstral Small 2 scores 61 (C) on agent readiness against Kronos's 55.2 (C), and leads in 3 of 4 scored categories. Kronos leads on documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/devstral-small-2-vs-kronos.json, and with the fewest tokens: https://www.anchorterminal.com/compare/devstral-small-2-vs-kronos.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"devstral-small-2\", \"b\": \"kronos\"}`. 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ZAYA1-8B](https://www.anchorterminal.com/compare/kronos-vs-zaya1-8b.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-11-11", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Devstral Small 2 vs Kronos", "url": "" } ], "description": "Devstral Small 2 scores 61 (C) to Kronos's 55.2 (C) for open-weight 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