{ "data": { "a": { "slug": "kronos", "name": "Kronos", "vendor": "Yu Shi (shiyu-coder, NeoQuasar), Tsinghua University", "vendorUrl": "https://github.com/shiyu-coder/Kronos", "kind": "weights", "category": "open-weights", "summary": "Kronos is an open-weight family of small forecasting models for financial candlestick (K-line) series, from Tsinghua University researchers. It reads open, high, low, close, volume and amount bars with timestamps and generates forecast bars, under MIT.", "url": "https://www.anchorterminal.com/tools/kronos", "markdownUrl": "https://www.anchorterminal.com/tools/kronos.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/kronos.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/kronos.json", "repo": "https://github.com/shiyu-coder/Kronos", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "NeoQuasar/Kronos-mini" }, { "registry": "huggingface", "name": "NeoQuasar/Kronos-small" }, { "registry": "huggingface", "name": "NeoQuasar/Kronos-base" }, { "registry": "huggingface", "name": "NeoQuasar/Kronos-Tokenizer-base" }, { "registry": "huggingface", "name": "NeoQuasar/Kronos-Tokenizer-2k" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The Hub reports gated false for all five NeoQuasar repositories, and the code is in a public GitHub repository (checked 2026-10-25).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the MIT licence named in each card's metadata and in the GitHub repository's LICENSE. Nobody sells per-call access and no inference provider serves it. Running it costs the hardware, 0.03 GB for Kronos-mini with its tokeniser and 0.43 GB for Kronos-base with its tokeniser, in FP32 (checked 2026-10-25).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-25).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 43000, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-25" }, "docsUrl": "https://github.com/shiyu-coder/Kronos", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "time-series", "forecasting", "finance", "candlestick", "k-line", "mit", "safetensors", "self-hosted" ], "lastRelease": "2026-04-13", "graded": true, "anchor": { "graded": true, "score": 55.2, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1210, "categoryRank": 68, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 60, "reach": 55, "risk": 50, "upkeep": 61 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-25" }, "negative": 0, "verdict": "Forecasts the next price bars from a history of up to 512 candlestick bars. Every size fits in under 0.5 GB and runs on a CPU, but only through the publisher's own Python package. Nobody has benchmarked it independently, its quickstart data was deleted from the repository, and the publisher hasn't replied since November 2025.", "bestFor": "Agents that need a quick local forecast or synthetic sample of a candlestick series on modest hardware, for research or as one input among others.", "strengths": [ "MIT with nothing added, ungated, safetensors only with SHA-256 hashes", "Under 0.5 GB for every public size, so it runs on a CPU, Apple MPS or a small GPU", "Input and output documented as plain DataFrames, with batch prediction over many series", "Training data listed by exchange with observation counts, filtering and a June 2024 end" ], "weaknesses": [ "Runs only through the publisher's own PyTorch code from GitHub, in no named runtime and with no quantised build", "A 512-bar context for the small and base sizes, and the mean forecast only, without quantiles", "The sample data the quickstart reads was deleted on 13 April 2026, and the cards' licence link points at a missing branch", "No safety results, no security contact and no publisher reply in its Hugging Face discussions since November 2025", "The publisher's forecasting results come from its own paper, with no independent benchmark found" ], "agentNotes": [ "Clone https://github.com/shiyu-coder/Kronos and `pip install -r requirements.txt`, then load `Kronos.from_pretrained(\"NeoQuasar/Kronos-small\")` and `KronosTokenizer.from_pretrained(\"NeoQuasar/Kronos-Tokenizer-base\")` and wrap them in KronosPredictor", "Pair Kronos-mini with Kronos-Tokenizer-2k and Kronos-small or Kronos-base with Kronos-Tokenizer-base, the cards' pairing", "The examples read examples/data/XSHG_5min_600977.csv, which no longer exists; point them at tests/data/regression_input.csv, 2,500 five-minute bars", "Keep the lookback at or under max_context, 512 bars, and pass the future timestamps you want as y_timestamp, since the calendar fields come from them", "Raise sample_count for a steadier forecast, but expect only the mean back; call the lower-level auto_regressive_inference yourself if you need the paths", "Treat forecasts as model output to check, not signals; the README says its own backtest pipeline isn't a production trading system" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 55.2 } ], "editorialScores": { "interface": 60, "reach": 55, "risk": 50, "upkeep": 61 }, "provenanceScore": 40, "checked": { "categories": { "interface": "2026-10-25", "reach": "2026-10-25", "risk": "2026-10-25", "upkeep": "2026-10-25" }, "last": "2026-10-25", "oldest": "2026-10-25", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "git clone https://github.com/shiyu-coder/Kronos \u0026\u0026 cd Kronos\npip install -r requirements.txt # Python 3.10+, PyTorch 2.0+\npython - \u003c\u003c'EOF'\nimport pandas as pd\nfrom model import Kronos, KronosTokenizer, KronosPredictor\ntok = KronosTokenizer.from_pretrained(\"NeoQuasar/Kronos-Tokenizer-base\")\nmodel = Kronos.from_pretrained(\"NeoQuasar/Kronos-small\")\np = KronosPredictor(model, tok, max_context=512) # picks cuda, mps or cpu\ndf = pd.read_csv(\"tests/data/regression_input.csv\", parse_dates=[\"timestamps\"])\nx, y = df.iloc[:400], df[\"timestamps\"].iloc[400:520]\ncols = [\"open\", \"high\", \"low\", \"close\", \"volume\", \"amount\"]\nprint(p.predict(df=x[cols], x_timestamp=x[\"timestamps\"], y_timestamp=y, pred_len=120, T=1.0, top_p=0.9, sample_count=1).head())\nEOF" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/kronos" }, "sameCompany": [ "github-mcp-server", "github-copilot-cli", "ai-job-search", "talorys" ], "weights": { "trainedBy": "Yu Shi and co-authors, Tsinghua University", "params": "4.1M to 102.3M", "context": 512, "goodFor": "Forecasting the next bars of a financial candlestick series, or sampling synthetic ones, on any machine, from a short history of open, high, low, close, volume and amount.", "runtimes": [], "builds": [ { "name": "Kronos-mini", "url": "https://huggingface.co/NeoQuasar/Kronos-mini", "who": "publisher", "precision": "fp32", "sizeGB": 0.03, "memoryGB": 8, "format": "safetensors", "runsOn": [ "PyTorch" ], "note": "32,304,418 bytes with Kronos-Tokenizer-2k, which it needs (16,451,361 for the model repository and 15,853,057 for the tokeniser). 2,048-bar context per the card" }, { "name": "Kronos-small", "url": "https://huggingface.co/NeoQuasar/Kronos-small", "who": "publisher", "precision": "fp32", "sizeGB": 0.11, "memoryGB": 8, "format": "safetensors", "runsOn": [ "PyTorch" ], "note": "114,844,309 bytes with Kronos-Tokenizer-base (98,991,258 and 15,853,051). The model the card's and README's examples load" }, { "name": "Kronos-base", "url": "https://huggingface.co/NeoQuasar/Kronos-base", "who": "publisher", "precision": "fp32", "sizeGB": 0.43, "memoryGB": 8, "format": "safetensors", "runsOn": [ "PyTorch" ], "note": "425,127,647 bytes with Kronos-Tokenizer-base (409,274,596 and 15,853,051). The largest public size" } ], "run": "git clone https://github.com/shiyu-coder/Kronos \u0026\u0026 cd Kronos \u0026\u0026 pip install -r requirements.txt, then in Python: KronosPredictor(Kronos.from_pretrained(\"NeoQuasar/Kronos-small\"), KronosTokenizer.from_pretrained(\"NeoQuasar/Kronos-Tokenizer-base\"), max_context=512).predict(df=..., x_timestamp=..., y_timestamp=..., pred_len=120)", "runBuild": "Kronos-small", "checked": "2026-10-25", "minMemoryGB": 8 }, "area": "models", "provenance": { "legalEntity": "No company. Yu Shi publishes it from the GitHub account shiyu-coder and the Hugging Face user account NeoQuasar (named ShiYu), with co-authors at Tsinghua University per the paper", "domain": "github.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-25", "notes": [ "NeoQuasar is a Hugging Face user account (fullname ShiYu), not an organisation. The GitHub README and the paper link it, so it's the publisher's own account.", "Nothing is hosted, so the MIT licence stands in for terms of service and no privacy policy is scored.", "No SECURITY.md, security contact or disclosure policy was found in the GitHub repository or on the cards.", "No changelog and no GitHub releases. The README's News section has three dated items, the last on 10 November 2025." ], "score": 40 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/kronos.json", "live": { "slug": "kronos", "securityTxt": { "url": "https://github.com/.well-known/security.txt", "state": "valid", "expires": "2026-11-24T15:28:23z", "checkedAt": "2026-10-25T15:28:23.736926714Z" }, "updatedAt": "2026-10-25T15:28:23.736926714Z" } }, "answer": "Qwen3-Coder-30B-A3B-Instruct and Kronos score within a point of each other on agent readiness, 55.7 (C) and 55.2 (C). Kronos leads on documentation and upkeep.", "b": { "slug": "qwen3-coder-30b-a3b-instruct", "name": "Qwen3-Coder-30B-A3B-Instruct", "vendor": "Alibaba (Qwen team)", "vendorUrl": "https://qwen.ai", "kind": "weights", "category": "open-weights", "summary": "Qwen3-Coder-30B-A3B-Instruct is Alibaba's open-weight mixture-of-experts coding model, 30.5B parameters with 3.3B active, for agentic coding and tool use, with a 262,144-token context, published in BF16 and FP8 under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/qwen3-coder-30b-a3b-instruct", "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-coder-30b-a3b-instruct.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-coder-30b-a3b-instruct.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-coder-30b-a3b-instruct.json", "repo": "https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Qwen/Qwen3-Coder-30B-A3B-Instruct" }, { "registry": "huggingface", "name": "Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8" }, { "registry": "ollama", "name": "qwen3-coder:30b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither the BF16 nor the FP8 repository is gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, 61.1 GB for the BF16 files, 31.2 GB for Alibaba's FP8 build and 19 GB for Ollama's 4-bit build (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "coding", "tool-calling", "long-context", "apache-2.0", "safetensors", "fp8", "self-hosted" ], "lastRelease": "2025-12-03", "graded": true, "anchor": { "graded": true, "score": 55.7, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1210, "categoryRank": 66, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 33, "interface": 75, "reach": 90, "risk": 55, "upkeep": 31 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A fast coding cousin, with 3.3B of 30.5B parameters active per token, for agentic coding tools, repository reading and fill-in-the-middle without a thinking step. It needs about 21 to 23 GB at 4 bits, and several providers host it. The main risk is that it runs commands for agents with no safety results or guidance, and Alibaba doesn't answer users.", "bestFor": "Coding agents and local coding tools that want a fast, capable open model on one 24 GB GPU or a 32 GB Mac, with a plain licence.", "strengths": [ "Only 3.3B parameters active per token, so it runs fast for its size", "4-bit builds from Ollama and LM Studio fit 24 GB", "Its own qwen3_coder tool parser shipped in the repository and carried by released vLLM and SGLang", "Plain Apache 2.0 with no gate, no account and no use policy, and a checksum on every file", "262,144 tokens of native context and fill-in-the-middle support" ], "weaknesses": [ "No safety results, prompt-injection guidance or security contact, though it's meant to run commands", "config.json was corrected in place in December 2025 with no note, and older GGUF copies may predate it", "No reply from Alibaba in 26 discussions, and parser reports wait", "No thinking mode and no documented structured output", "Alibaba's only quantised build is FP8" ], "agentNotes": [ "Quickest start is `ollama run qwen3-coder:30b`, 19 GB", "On our reference machine use BF16 with `vllm serve Qwen/Qwen3-Coder-30B-A3B-Instruct --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, 61 GB of weights fits the GPU share", "Use the qwen3_coder parser in vLLM or SGLang, not hermes, since calls come back as function and parameter blocks rather than JSON", "Pass tool-call arguments in the history as objects, the template iterates them as a mapping", "Sampling per the card is temperature 0.7, top_p 0.8, top_k 20 and repetition_penalty 1.05, with up to 65,536 output tokens", "For code completion use the fill-in-the-middle tokens `\u003c|fim_prefix|\u003e`, `\u003c|fim_suffix|\u003e` and `\u003c|fim_middle|\u003e` from Qwen's README", "Sandbox the commands and files it touches, since nothing from Alibaba covers prompt injection" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 55.7 } ], "editorialScores": { "capability": 33, "interface": 75, "reach": 90, "risk": 55, "upkeep": 31 }, "provenanceScore": 49, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run qwen3-coder:30b\n# or, with vLLM:\nvllm serve Qwen/Qwen3-Coder-30B-A3B-Instruct --max-model-len 262144 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_coder", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"qwen3-coder:30b\", \"messages\": [{\"role\": \"user\", \"content\": \"Write a quick sort in Go.\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/qwen3-coder-30b-a3b-instruct" }, "sameCompany": [ "qwen3-embedding-0-6b", "qwen3-embedding-4b", "qwen3-embedding-8b", "qwen3-vl-embedding-2b", "qwen3-vl-embedding-8b", "qwen3-asr-0-6b", "qwen3-asr-1-7b", "qwen3-tts-1-7b-customvoice", "qwen3-30b-a3b-instruct-2507", "qwen3-4b-instruct-2507", "qwen3-coder-next", "qwen3-omni-30b-a3b-instruct", "qwen3-vl-2b-instruct", "qwen3-vl-30b-a3b-instruct", "qwen3-vl-32b-instruct", "qwen3-vl-4b-instruct", "qwen3-vl-8b-instruct", "qwen3-5-0-8b", "qwen3-5-122b-a10b", "qwen3-5-2b", "qwen3-5-397b-a17b", "qwen3-5-4b", "qwen3-5-9b", "qwen3-6-27b", "qwen3-6-35b-a3b", "qwen3-8-27b", "qwen3-8-flash-next" ], "weights": { "trainedBy": "Alibaba (Qwen team)", "params": "30.5B (3.3B active)", "context": 262144, "goodFor": "Mixture-of-experts coding model for agentic coding tools, repository-scale code reading, tool calls and fill-in-the-middle, fast for its size and with no thinking step.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "ollama qwen3-coder:30b", "url": "https://ollama.com/library/qwen3-coder:30b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 19, "memoryGB": 24, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Digest 06c1097efce0, also tagged latest and 30b-a3b-q4_K_M. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB" }, { "name": "LM Studio qwen/qwen3-coder-30b (Q4_K_M GGUF)", "url": "https://huggingface.co/lmstudio-community/Qwen3-Coder-30B-A3B-Instruct-GGUF", "who": "lmstudio", "precision": "q4_k_m", "sizeGB": 18.63, "memoryGB": 24, "format": "gguf", "runsOn": [ "LM Studio", "llama.cpp" ], "note": "LM Studio's catalogue entry qwen/qwen3-coder-30b draws on this repository (Q3_K_L, Q4_K_M, Q6_K and Q8_0) and on MLX builds at 4 to 8 bits. The MLX 4-bit repository is 17.19 GB, also in the 24 GB band" }, { "name": "Qwen3-Coder-30B-A3B-Instruct-FP8", "url": "https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 31.2, "memoryGB": 48, "format": "safetensors", "note": "Block-wise FP8 with 128-wide blocks, per its card" }, { "name": "Qwen3-Coder-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct", "who": "publisher", "precision": "bf16", "sizeGB": 61.08, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "vllm serve Qwen/Qwen3-Coder-30B-A3B-Instruct --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder", "runBuild": "Qwen3-Coder-30B-A3B-Instruct", "checked": "2026-10-21", "minMemoryGB": 24 }, "family": { "lab": "Alibaba (Qwen)", "family": "Qwen3", "url": "https://www.anchorterminal.com/open-weights/#qwen", "others": [ { "name": "Qwen3-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-0.6B", "params": "0.6B" }, { "name": "Qwen3-1.7B", "url": "https://huggingface.co/Qwen/Qwen3-1.7B", "params": "1.7B" }, { "name": "Qwen3-14B", "url": "https://huggingface.co/Qwen/Qwen3-14B", "params": "14B" }, { "name": "Qwen3-235B-A22B", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B", "params": "235B-A22B" }, { "name": "Qwen3-30B-A3B", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B", "params": "30B-A3B" }, { "name": "Qwen3-32B", "url": "https://huggingface.co/Qwen/Qwen3-32B", "params": "32B" }, { "name": "Qwen3-4B", "url": "https://huggingface.co/Qwen/Qwen3-4B", "params": "4B" }, { "name": "Qwen3-8B", "url": "https://huggingface.co/Qwen/Qwen3-8B", "params": "8B" }, { "name": "Qwen3-0.6B-Base", "url": "https://huggingface.co/Qwen/Qwen3-0.6B-Base", "params": "0.6B" }, { "name": "Qwen3-1.7B-Base", "url": "https://huggingface.co/Qwen/Qwen3-1.7B-Base", "params": "1.7B" }, { "name": "Qwen3-14B-Base", "url": "https://huggingface.co/Qwen/Qwen3-14B-Base", "params": "14B" }, { "name": "Qwen3-30B-A3B-Base", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Base", "params": "30B-A3B" }, { "name": "Qwen3-4B-Base", "url": "https://huggingface.co/Qwen/Qwen3-4B-Base", "params": "4B" }, { "name": "Qwen3-8B-Base", "url": "https://huggingface.co/Qwen/Qwen3-8B-Base", "params": "8B" }, { "name": "Qwen3-Reranker-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-0.6B", "params": "0.6B" }, { "name": "Qwen3-Reranker-8B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-8B", "params": "8B" }, { "name": "Qwen3-Embedding-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-0.6B", "params": "0.6B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-0-6b" }, { "name": "Qwen3-Embedding-4B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-4B", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-4b" }, { "name": "Qwen3-Embedding-8B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-8B", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-8b" }, { "name": "Qwen3-Reranker-4B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-4B", "params": "4B" }, { "name": "Qwen3-235B-A22B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507", "params": "235B-A22B" }, { "name": "Qwen3-Coder-480B-A35B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct", "params": "480B-A35B" }, { "name": "Qwen3-235B-A22B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507", "params": "235B-A22B" }, { "name": "Qwen3-30B-A3B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-30b-a3b-instruct-2507" }, { "name": "Qwen3-30B-A3B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507", "params": "30B-A3B" }, { "name": "Qwen3-4B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-4b-instruct-2507" }, { "name": "Qwen3-4B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507", "params": "4B" }, { "name": "Qwen3-Next-80B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct", "params": "80B-A3B" }, { "name": "Qwen3-Next-80B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Thinking", "params": "80B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Captioner", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Captioner", "params": "30B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Thinking", "params": "30B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-omni-30b-a3b-instruct" }, { "name": "Qwen3-VL-235B-A22B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Instruct", "params": "235B-A22B" }, { "name": "Qwen3-VL-235B-A22B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Thinking", "params": "235B-A22B" }, { "name": "Qwen3Guard-Gen-0.6B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-0.6B", "params": "0.6B" }, { "name": "Qwen3Guard-Gen-4B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-4B", "params": "4B" }, { "name": "Qwen3Guard-Gen-8B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-8B", "params": "8B" }, { "name": "Qwen3Guard-Stream-0.6B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-0.6B", "params": "0.6B" }, { "name": "Qwen3Guard-Stream-4B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-4B", "params": "4B" }, { "name": "Qwen3Guard-Stream-8B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-8B", "params": "8B" }, { "name": "Qwen3-4B-SafeRL", "url": "https://huggingface.co/Qwen/Qwen3-4B-SafeRL", "params": "4B" }, { "name": "Qwen3-VL-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-30b-a3b-instruct" }, { "name": "Qwen3-VL-30B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Thinking", "params": "30B-A3B" }, { "name": "Qwen3-VL-4B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-4b-instruct" }, { "name": "Qwen3-VL-4B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Thinking", "params": "4B" }, { "name": "Qwen3-VL-8B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-8b-instruct" }, { "name": "Qwen3-VL-8B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Thinking", "params": "8B" }, { "name": "Qwen3-VL-2B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-2b-instruct" }, { "name": "Qwen3-VL-2B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-2B-Thinking", "params": "2B" }, { "name": "Qwen3-VL-32B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct", "params": "32B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-32b-instruct" }, { "name": "Qwen3-VL-32B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Thinking", "params": "32B" }, { "name": "Qwen3-VL-Embedding-2B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-2b" }, { "name": "Qwen3-VL-Embedding-8B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-8B", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-8b" }, { "name": "Qwen3-VL-Reranker-2B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Reranker-2B", "params": "2B" }, { "name": "Qwen3-VL-Reranker-8B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Reranker-8B", "params": "8B" }, { "name": "Qwen3-TTS-12Hz-0.6B-Base", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-Base", "params": "0.6B" }, { "name": "Qwen3-TTS-12Hz-0.6B-CustomVoice", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice", "params": "0.6B" }, { "name": "Qwen3-TTS-12Hz-1.7B-Base", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-Base", "params": "1.7B" }, { "name": "Qwen3-TTS-12Hz-1.7B-CustomVoice", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice", "params": "1.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-tts-1-7b-customvoice" }, { "name": "Qwen3-TTS-12Hz-1.7B-VoiceDesign", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign", "params": "1.7B" }, { "name": "Qwen3-TTS-Tokenizer-12Hz", "url": "https://huggingface.co/Qwen/Qwen3-TTS-Tokenizer-12Hz", "params": "not in name" }, { "name": "Qwen3-ASR-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-ASR-0.6B", "params": "0.6B", "listing": "https://www.anchorterminal.com/tools/qwen3-asr-0-6b" }, { "name": "Qwen3-ASR-1.7B", "url": "https://huggingface.co/Qwen/Qwen3-ASR-1.7B", "params": "1.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-asr-1-7b" }, { "name": "Qwen3-ForcedAligner-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-ForcedAligner-0.6B", "params": "0.6B" }, { "name": "Qwen3-Coder-Next", "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next", "params": "not in name; Hub safetensors total 79.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-coder-next" }, { "name": "Qwen3-Coder-Next-Base", "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next-Base", "params": "not in name; Hub safetensors total 79.7B" }, { "name": "WebWorld-14B", "url": "https://huggingface.co/Qwen/WebWorld-14B", "params": "14B" }, { "name": "WebWorld-32B", "url": "https://huggingface.co/Qwen/WebWorld-32B", "params": "32B" }, { "name": "WebWorld-8B", "url": "https://huggingface.co/Qwen/WebWorld-8B", "params": "8B" }, { "name": "Qwen3.5-397B-A17B", "url": "https://huggingface.co/Qwen/Qwen3.5-397B-A17B", "params": "397B-A17B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-397b-a17b" }, { "name": "Qwen3.5-122B-A10B", "url": "https://huggingface.co/Qwen/Qwen3.5-122B-A10B", "params": "122B-A10B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-122b-a10b" }, { "name": "Qwen3.5-27B", "url": "https://huggingface.co/Qwen/Qwen3.5-27B", "params": "27B" }, { "name": "Qwen3.5-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B", "params": "35B-A3B" }, { "name": "Qwen3.5-35B-A3B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B-Base", "params": "35B-A3B" }, { "name": "Qwen3.5-9B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-9B-Base", "params": "9B" }, { "name": "Qwen3.5-4B", "url": "https://huggingface.co/Qwen/Qwen3.5-4B", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-4b" }, { "name": "Qwen3.5-4B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-4B-Base", "params": "4B" }, { "name": "Qwen3.5-9B", "url": "https://huggingface.co/Qwen/Qwen3.5-9B", "params": "9B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-9b" }, { "name": "Qwen3.5-0.8B", "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B", "params": "0.8B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-0-8b" }, { "name": "Qwen3.5-0.8B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B-Base", "params": "0.8B" }, { "name": "Qwen3.5-2B", "url": "https://huggingface.co/Qwen/Qwen3.5-2B", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-2b" }, { "name": "Qwen3.5-2B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-2B-Base", "params": "2B" }, { "name": "Qwen3.6-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen3.6-35B-A3B", "params": "35B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-6-35b-a3b" }, { "name": "Qwen3.6-27B", "url": "https://huggingface.co/Qwen/Qwen3.6-27B", "params": "27B", "listing": "https://www.anchorterminal.com/tools/qwen3-6-27b" }, { "name": "Qwen-AgentWorld-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B", "params": "35B-A3B" }, { "name": "Qwen3.8-27B", "url": "https://huggingface.co/Qwen/Qwen3.8-27B", "params": "27B", "listing": "https://www.anchorterminal.com/tools/qwen3-8-27b" }, { "name": "Qwen3.8-2.4T-A95B", "url": "https://huggingface.co/Qwen/Qwen3.8-2.4T-A95B", "params": "2.4T-A95B" }, { "name": "Qwen3.8-Flash-Next", "url": "https://huggingface.co/Qwen/Qwen3.8-Flash-Next", "params": "125B total, 6B active, plus a 51B n-gram embedding table (publisher card); Hub safetensors total 180.0B", "listing": "https://www.anchorterminal.com/tools/qwen3-8-flash-next" }, { "name": "Qwen-Drive-1.0-4B", "url": "https://huggingface.co/Qwen/Qwen-Drive-1.0-4B", "params": "4B" } ], "olderCount": 109, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Alibaba Cloud (named in the licence's copyright line)", "domain": "qwen.ai", "domainRegistered": "2023-08-27", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The LICENSE file reads Copyright 2024 Alibaba Cloud. The registration date of qwen.ai is carried from the 20 October check for qwen3-8-27b.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "No changelog. Commit titles name the template, parser and config updates of 7 and 21 August and 3 December 2025, without saying what changed.", "qwen.ai/.well-known/security.txt redirects to the home page, so there is no security.txt." ], "score": 49 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-coder-30b-a3b-instruct.json", "live": { "slug": "qwen3-coder-30b-a3b-instruct", "securityTxt": { "url": "https://qwen.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-25T15:28:54.526754219Z" }, "domain": { "domain": "qwen.ai", "registered": "2023-08-27", "source": "https://rdap.identitydigital.services/rdap/domain/qwen.ai", "checkedAt": "2026-10-25T13:51:56.4259776Z" }, "updatedAt": "2026-10-25T15:28:54.526754219Z" } }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Yu Shi (shiyu-coder, NeoQuasar), Tsinghua University", "b": "Alibaba (Qwen team)", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "MIT", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-04-13", "b": "2025-12-03", "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": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "43k stars", "b": "none", "name": "Popularity" }, { "a": "none", "b": "2.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Qwen3-Coder-30B-A3B-Instruct and Kronos score within a point of each other on agent readiness, 55.7 (C) and 55.2 (C). Kronos leads on documentation and upkeep.", "question": "Which is better for AI agents, Kronos or Qwen3-Coder-30B-A3B-Instruct?" } ], "goodFor": [ { "aheadOn": [ "Documentation and upkeep, 61 against 31" ], "also": null, "goodFor": "Agents that need a quick local forecast or synthetic sample of a candlestick series on modest hardware, for research or as one input among others.", "slug": "kronos", "watchFor": "Runs only through the publisher's own PyTorch code from GitHub, in no named runtime and with no quantised build" }, { "aheadOn": [ "Reach, 90 against 55", "Risk, 55 against 50", "Agent interface, 75 against 60" ], "also": null, "goodFor": "Coding agents and local coding tools that want a fast, capable open model on one 24 GB GPU or a 32 GB Mac, with a plain licence.", "slug": "qwen3-coder-30b-a3b-instruct", "watchFor": "No safety results, prompt-injection guidance or security contact, though it's meant to run commands" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-kronos.json", "title": "Agents-A1-4B vs Kronos", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-kronos" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Agents-A1-4B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kronos.json", "title": "Apertus v1.5 8B vs Kronos", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kronos" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Apertus v1.5 8B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kronos.json", "title": "Apodex-1.1-mini vs Kronos", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kronos" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Apodex-1.1-mini vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-kronos.json", "title": "BigBang-v1 vs Kronos", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-kronos" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-coder-30b-a3b-instruct.json", "title": "BigBang-v1 vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-kronos.json", "title": "Carbon-X vs Kronos", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-kronos" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Carbon-X vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-kronos.json", "title": "Cosmos-Reason2-2B vs Kronos", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-kronos" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Cosmos-Reason2-2B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-kronos.json", "title": "Darwin-36B-Opus vs Kronos", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-kronos" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Darwin-36B-Opus vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-kronos.json", "title": "DeepSeek-V3.2 vs Kronos", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-kronos" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-coder-30b-a3b-instruct.json", "title": "DeepSeek-V3.2 vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kronos.json", "title": "DeepSeek V4.1 Flash vs Kronos", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kronos" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-coder-30b-a3b-instruct.json", "title": "DeepSeek V4.1 Flash vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-kronos.json", "title": "DeepSeek-V4-Flash-0731 vs Kronos", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-kronos" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-coder-30b-a3b-instruct.json", "title": "DeepSeek-V4-Flash-0731 vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-kronos.json", "title": "DiffusionGemma 26B A4B IT vs Kronos", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-kronos" }, { "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-coder-30b-a3b-instruct.json", "title": "DiffusionGemma 26B A4B IT vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-kronos.json", "title": "Gemma 4 12B IT vs Kronos", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-kronos" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Gemma 4 12B IT vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-kronos.json", "title": "Gemma 4 26B A4B IT vs Kronos", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-kronos" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Gemma 4 26B A4B IT vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-kronos.json", "title": "Gemma 4 31B IT vs Kronos", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-kronos" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Gemma 4 31B IT vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-kronos.json", "title": "Gemma 4 E2B IT vs Kronos", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-kronos" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Gemma 4 E2B IT vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-kronos.json", "title": "Gemma 4 E4B IT vs Kronos", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-kronos" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Gemma 4 E4B IT vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-kronos.json", "title": "GLM-4.5-Air vs Kronos", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-kronos" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-coder-30b-a3b-instruct.json", "title": "GLM-4.5-Air vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-kronos.json", "title": "GLM-4.7-Flash vs Kronos", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-kronos" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-coder-30b-a3b-instruct.json", "title": "GLM-4.7-Flash vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-qwen3-coder-30b-a3b-instruct" }, { "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-flash-vs-qwen3-coder-30b-a3b-instruct.json", "title": "GLM-5.3-Flash vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-coder-30b-a3b-instruct" }, { "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/glm-5-3-vs-qwen3-coder-30b-a3b-instruct.json", "title": "GLM-5.3 vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-qwen3-coder-30b-a3b-instruct" }, { "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-120b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "gpt-oss-120b vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-qwen3-coder-30b-a3b-instruct" }, { "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/gpt-oss-20b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "gpt-oss-20b vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-kronos.json", "title": "Granite 4.2 3B vs Kronos", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-kronos" }, { "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Granite 4.2 3B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-kronos.json", "title": "Hy3 vs Kronos", "url": 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Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwen3-vl-8b-instruct.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwopus3-8-27b-flash-v2.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-recursive-flint.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-smollm3-3b.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-spark-x2-6-4b.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-swift-1-5-qwen3-8-27b.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 35, "edge": "qwen3-coder-30b-a3b-instruct", "key": "reach", "kronos": 55, "name": "Reach", "qwen3-coder-30b-a3b-instruct": 90, "weight": 25 }, { "by": 5, "edge": "qwen3-coder-30b-a3b-instruct", "key": "risk", "kronos": 50, "name": "Risk", "qwen3-coder-30b-a3b-instruct": 55, "weight": 20 }, { "by": 15, "edge": "qwen3-coder-30b-a3b-instruct", "key": "interface", "kronos": 60, "name": "Agent interface", "qwen3-coder-30b-a3b-instruct": 75, "weight": 10 }, { "by": 30, "edge": "kronos", "key": "upkeep", "kronos": 61, "name": "Documentation and upkeep", "qwen3-coder-30b-a3b-instruct": 31, "weight": 10 } ], "summary": "Qwen3-Coder-30B-A3B-Instruct and Kronos score within a point of each other on agent readiness, 55.7 (C) and 55.2 (C). Kronos leads on documentation and upkeep. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "kronos": "Forecasts the next price bars from a history of up to 512 candlestick bars. Every size fits in under 0.5 GB and runs on a CPU, but only through the publisher's own Python package. Nobody has benchmarked it independently, its quickstart data was deleted from the repository, and the publisher hasn't replied since November 2025.", "qwen3-coder-30b-a3b-instruct": "A fast coding cousin, with 3.3B of 30.5B parameters active per token, for agentic coding tools, repository reading and fill-in-the-middle without a thinking step. It needs about 21 to 23 GB at 4 bits, and several providers host it. The main risk is that it runs commands for agents with no safety results or guidance, and Alibaba doesn't answer users." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-coder-30b-a3b-instruct", "json": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-coder-30b-a3b-instruct.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-coder-30b-a3b-instruct.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/kronos-vs-qwen3-coder-30b-a3b-instruct.min.md" }, "markdown": "Qwen3-Coder-30B-A3B-Instruct and Kronos score within a point of each other on agent readiness, 55.7 (C) and 55.2 (C). Kronos leads on documentation and upkeep. Both do open-weight models you run yourself.\n\n- Kronos: grade C, 55.2/100, rank #86 of 110 open-weight models. Markdown https://www.anchorterminal.com/tools/kronos.md · JSON https://www.anchorterminal.com/api/v1/tools/kronos.json\n- Qwen3-Coder-30B-A3B-Instruct: grade C, 55.7/100, rank #82 of 110 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-coder-30b-a3b-instruct.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-coder-30b-a3b-instruct.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 3260 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Kronos (C)\n\nGood for: Agents that need a quick local forecast or synthetic sample of a candlestick series on modest hardware, for research or as one input among others.\n\nAhead on:\n- Documentation and upkeep, 61 against 31\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### Qwen3-Coder-30B-A3B-Instruct (C)\n\nGood for: Coding agents and local coding tools that want a fast, capable open model on one 24 GB GPU or a 32 GB Mac, with a plain licence.\n\nAhead on:\n- Reach, 90 against 55\n- Risk, 55 against 50\n- Agent interface, 75 against 60\n\nWatch for: No safety results, prompt-injection guidance or security contact, though it's meant to run commands\n\n\n## Score by category\n\n| Category | Weight | Kronos | Qwen3-Coder-30B-A3B-Instruct | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 55 | 90 | Qwen3-Coder-30B-A3B-Instruct +35 |\n| Risk | 20% (20 this run) | 50 | 55 | Qwen3-Coder-30B-A3B-Instruct +5 |\n| Agent interface | 10% (10 this run) | 60 | 75 | Qwen3-Coder-30B-A3B-Instruct +15 |\n| Documentation and upkeep | 10% (10 this run) | 61 | 31 | Kronos +30 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **55.2 · C** | **55.7 · C** | |\n\n## Facts side by side\n\n| Fact | Kronos | Qwen3-Coder-30B-A3B-Instruct |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Yu Shi (shiyu-coder, NeoQuasar), Tsinghua University | Alibaba (Qwen team) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | MIT | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-04-13 | 2025-12-03 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Popularity | 43k stars | none |\n| Agent reviews | none | 2.8/5 (4) |\n\n## Verdicts\n\n**Kronos.** Forecasts the next price bars from a history of up to 512 candlestick bars. Every size fits in under 0.5 GB and runs on a CPU, but only through the publisher's own Python package. Nobody has benchmarked it independently, its quickstart data was deleted from the repository, and the publisher hasn't replied since November 2025.\n\n**Qwen3-Coder-30B-A3B-Instruct.** A fast coding cousin, with 3.3B of 30.5B parameters active per token, for agentic coding tools, repository reading and fill-in-the-middle without a thinking step. It needs about 21 to 23 GB at 4 bits, and several providers host it. The main risk is that it runs commands for agents with no safety results or guidance, and Alibaba doesn't answer users.\n\n## Before you call either\n\n### Kronos\n\n1. Clone https://github.com/shiyu-coder/Kronos and `pip install -r requirements.txt`, then load `Kronos.from_pretrained(\"NeoQuasar/Kronos-small\")` and `KronosTokenizer.from_pretrained(\"NeoQuasar/Kronos-Tokenizer-base\")` and wrap them in KronosPredictor\n2. Pair Kronos-mini with Kronos-Tokenizer-2k and Kronos-small or Kronos-base with Kronos-Tokenizer-base, the cards' pairing\n3. The examples read examples/data/XSHG_5min_600977.csv, which no longer exists; point them at tests/data/regression_input.csv, 2,500 five-minute bars\n4. Keep the lookback at or under max_context, 512 bars, and pass the future timestamps you want as y_timestamp, since the calendar fields come from them\n5. Raise sample_count for a steadier forecast, but expect only the mean back; call the lower-level auto_regressive_inference yourself if you need the paths\n6. Treat forecasts as model output to check, not signals; the README says its own backtest pipeline isn't a production trading system\n\n### Qwen3-Coder-30B-A3B-Instruct\n\n1. Quickest start is `ollama run qwen3-coder:30b`, 19 GB\n2. On our reference machine use BF16 with `vllm serve Qwen/Qwen3-Coder-30B-A3B-Instruct --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, 61 GB of weights fits the GPU share\n3. Use the qwen3_coder parser in vLLM or SGLang, not hermes, since calls come back as function and parameter blocks rather than JSON\n4. Pass tool-call arguments in the history as objects, the template iterates them as a mapping\n5. Sampling per the card is temperature 0.7, top_p 0.8, top_k 20 and repetition_penalty 1.05, with up to 65,536 output tokens\n6. For code completion use the fill-in-the-middle tokens `\u003c|fim_prefix|\u003e`, `\u003c|fim_suffix|\u003e` and `\u003c|fim_middle|\u003e` from Qwen's README\n7. Sandbox the commands and files it touches, since nothing from Alibaba covers prompt injection\n\n## Questions\n\n### Which is better for AI agents, Kronos or Qwen3-Coder-30B-A3B-Instruct?\n\nQwen3-Coder-30B-A3B-Instruct and Kronos score within a point of each other on agent readiness, 55.7 (C) and 55.2 (C). Kronos leads on documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/kronos-vs-qwen3-coder-30b-a3b-instruct.json, and with the fewest tokens: https://www.anchorterminal.com/compare/kronos-vs-qwen3-coder-30b-a3b-instruct.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"kronos\", \"b\": \"qwen3-coder-30b-a3b-instruct\"}`. From a terminal: `anchor compare kronos qwen3-coder-30b-a3b-instruct`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/kronos.json and https://www.anchorterminal.com/api/v1/tools/qwen3-coder-30b-a3b-instruct.json\n\n## Other comparisons with Kronos or Qwen3-Coder-30B-A3B-Instruct\n\n- [Agents-A1-4B vs Kronos](https://www.anchorterminal.com/compare/agents-a1-4b-vs-kronos.md)\n- [Agents-A1-4B vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-coder-30b-a3b-instruct.md)\n- [Apertus v1.5 8B vs Kronos](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kronos.md)\n- [Apertus v1.5 8B vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-coder-30b-a3b-instruct.md)\n- [Apodex-1.1-mini vs Kronos](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kronos.md)\n- [Apodex-1.1-mini vs 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27B)](https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-thinkingcap-qwen3-8-27b.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": "2026-10-25", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.6", "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": "Kronos vs Qwen3-Coder-30B-A3B-Instruct", "url": "" } ], "description": "Qwen3-Coder-30B-A3B-Instruct and Kronos score within a point of each other for open-weight models you run yourself, 55.7 and 55.2 out of 100. 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