{ "data": { "a": { "slug": "joey-1", "name": "joey-1", "vendor": "Baseten", "vendorUrl": "https://labs.baseten.co", "kind": "weights", "category": "decision-models", "summary": "joey-1 is Base Labs' open-weight 2B decision model, a full fine-tune of Qwen3.5-2B that takes text and images and returns a probability for every option of typed questions about a state in one prefill. Base Labs is Baseten's research lab.", "url": "https://www.anchorterminal.com/tools/joey-1", "markdownUrl": "https://www.anchorterminal.com/tools/joey-1.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/joey-1.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/joey-1.json", "repo": "https://huggingface.co/baselabs/joey-1", "license": "MIT, named in the card's metadata (license: mit) since the upload of 27 October 2026, with no LICENSE file. The base Qwen/Qwen3.5-2B is Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "baselabs/joey-1" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-30). Baseten's hosted Model API for the same model needs a Baseten API key.", "pricing": "free", "pricingNotes": "Free to download under the MIT licence the card's metadata names. Running it costs the hardware, 5.47 GB of BF16 files by the Hub's file list. Baseten also serves it on its Model APIs as decision-models/baseten-joey-2b-latest at $0.01 per million input tokens, with cached input and output at $0.00, per its library page (checked 2026-10-30).", "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 2026-10-30).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-30" }, "docsUrl": "https://huggingface.co/baselabs/joey-1", "capabilities": [ "inference.decision", "model.open-weights" ], "tags": [ "open-weights", "model", "decision-model", "full-fine-tune", "calibrated-probabilities", "vision", "mit", "safetensors", "self-hosted" ], "lastRelease": "2026-10-29", "graded": true, "anchor": { "graded": true, "score": 57.1, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1239, "categoryRank": 16, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 70, "reach": 60, "risk": 55, "upkeep": 54 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-30" }, "negative": -2, "negativeNotes": [ "29 October 2026. Baseten's library page says 'On release (October 2026), joey-1 is the top 2B model on the Jev Decision Index 0.4.' The index (edition 0.4.1, joey-1 added 29 October in PR #330) ranks by its Full score and says the public benchmarks don't count. On it joey-1 has 35.91, 149th of 247, below StartLux-Decision v1.1 2B at 40.11 on the same Qwen3.5-2B base. Only the public score (63.30, first of the models under 4B) supports the claim, and the card names that split while the library page doesn't. Sources: https://www.baseten.co/library/joey-1/ and https://huggingface.co/spaces/multimodalart/jev-decision-index/blob/main/data/index.json (-2)" ], "verdict": "A very small cousin for routing, triage and guardrail checks, answering several typed questions about one state, with text or images, as probabilities from one prefill. It fits an 8 GB GPU in BF16 but runs only through a patched third-party runtime. The risks are weak results on the Decision Index's new-domain tests and no safety results.", "bestFor": "Agents that want self-hosted, low-cost ticket routing or guardrail checks with probabilities to threshold, on a GPU with 8 GB, and can accept a patched runtime.", "strengths": [ "Probabilities for choice, yes or no and score questions, several per request from one shared prefill, over text and images", "5.5 GB in BF16, so it fits an 8 GB GPU", "Highest public score of every model under 4B on the Jev Decision Index 0.4.1 at 63.30", "The card documents the request, answer fields and a runnable call, and the licence has been MIT since upload", "Not gated, and Baseten's own Model API serves the same model at $0.01 per million input tokens" ], "weaknesses": [ "Serving it needs OneJev at a pinned commit plus the repository's serve.patch, with no released runtime support", "Full score 35.91 on the Decision Index's private tests, 10.2 on new domains, with one test excluded for signs of training overlap", "Trained at 16,384 tokens, and longer requests are refused", "No quantised build, no safety results and no prompt-injection guidance", "No LICENSE file, and the Apache-2.0 base's licence isn't carried" ], "agentNotes": [ "Start on a CUDA GPU in pytorch/pytorch:2.8.0-cuda12.8-cudnn9-devel with `hf download baselabs/joey-1 --local-dir joey-1 \u0026\u0026 bash joey-1/setup.sh`, which clones OneJev at 81ce62f, applies serve.patch and installs the Decision Index kit", "Then call `DecisionEngine(\"baselabs/joey-1\", device=\"cuda:0\").decide(SystemOneRequest(state=..., questions=...))` from qev, as the card shows", "Don't load it with stock vLLM or Transformers. They ignore the block-bidirectional attention in config.json's modeling_overrides and give probabilities the model wasn't trained for", "Keep each request under 16,384 tokens, the trained length, or the engine refuses it", "Expect weaker answers on domains unlike its training mix, and threshold on the returned probability before acting", "Without hardware, Baseten's /v1/systemone serves it as decision-models/baseten-joey-2b-latest with a Baseten key" ], "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": 57.1 } ], "editorialScores": { "interface": 70, "reach": 60, "risk": 55, "upkeep": 54 }, "provenanceScore": 50, "checked": { "categories": { "interface": "2026-10-30", "reach": "2026-10-30", "risk": "2026-10-30", "upkeep": "2026-10-30" }, "last": "2026-10-30", "oldest": "2026-10-30", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "hf download baselabs/joey-1 --local-dir joey-1 \u0026\u0026 bash joey-1/setup.sh # clones OneJev at 81ce62f, applies serve.patch, installs the Decision Index kit (CUDA image pytorch/pytorch:2.8.0-cuda12.8-cudnn9-devel)\n# then, in Python:\nfrom qev.engine import DecisionEngine\nfrom qev.schema import SystemOneRequest\nengine = DecisionEngine(\"baselabs/joey-1\", device=\"cuda:0\")\nresp, _ = engine.decide(SystemOneRequest(state=..., questions={...}))" }, "letme": { "capability": "https://letme.dev/inference.decision", "tool": "https://letme.dev/joey-1" }, "sameCompany": [ "baseten", "elephant-1", "mammoth-1" ], "weights": { "trainedBy": "Base Labs (Baseten), on Alibaba's Qwen3.5-2B", "builtOn": "Qwen/Qwen3.5-2B (full fine-tune, Apache-2.0)", "params": "2B", "context": 16384, "goodFor": "Cheap typed decisions with calibrated probabilities over text and images, several choice, yes or no and score questions about one state in one prefill, for routing, triage and guardrails on a small GPU.", "runtimes": [ "OneJev qev (patched)" ], "builds": [ { "name": "joey-1", "url": "https://huggingface.co/baselabs/joey-1", "who": "publisher", "precision": "bf16", "sizeGB": 5.5, "memoryGB": 8, "format": "safetensors", "runsOn": [ "OneJev qev with serve.patch" ], "note": "model.safetensors (5.44 GB), tokenizer, configs, banner and serving bundle, 5.47 GB summed from the Hub's file list. 5.47 × 1.2 = 6.6 GB. No quantised build from Base Labs and no Ollama or LM Studio library entry" } ], "run": "bash setup.sh \u0026\u0026 python -c 'from qev.engine import DecisionEngine; DecisionEngine(\"baselabs/joey-1\", device=\"cuda:0\")'", "runBuild": "joey-1", "checked": "2026-10-30", "minMemoryGB": 8 }, "family": { "lab": "Base Labs (Baseten)", "family": "Base Labs decision models (-1)", "url": "https://www.anchorterminal.com/open-weights/#baselabs", "others": [ { "name": "elephant-1", "url": "https://huggingface.co/baselabs/elephant-1", "params": "9B (9,409,813,744 by the Hub's safetensors count)", "listing": "https://www.anchorterminal.com/tools/elephant-1" }, { "name": "mammoth-1", "url": "https://huggingface.co/baselabs/mammoth-1", "params": "27B (27,356,728,560 by the Hub's safetensors count)", "listing": "https://www.anchorterminal.com/tools/mammoth-1" } ] }, "area": "models", "provenance": { "legalEntity": "Baseten Labs, Inc.", "domain": "baseten.co", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-30", "notes": [ "Base Labs is Baseten's research lab (labs.baseten.co). The legal entity is from the baseten listing's check of Baseten's terms on 30 September 2026.", "The baselabs Hugging Face organisation is unverified, with nine members, among them the Base Labs report's authors. Baseten's library page and the card link it to Baseten.", "Nothing is hosted under this listing, so the licence stands in for terms of service and no privacy policy is scored.", "No changelog. security.txt is recorded as absent from the elephant-1 check of 21 October 2026 and wasn't re-read." ], "score": 50 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/joey-1.json" }, "answer": "joey-1 scores 57.1 (C) on agent readiness against mammoth-1's 47.7 (D), and leads in 3 of 4 scored categories.", "b": { "slug": "mammoth-1", "name": "mammoth-1", "vendor": "Baseten", "vendorUrl": "https://labs.baseten.co", "kind": "weights", "category": "decision-models", "summary": "mammoth-1 is Base Labs' open-weight 27B decision model, a full fine-tune of Qwen3.5-27B that returns a probability for every option of each question about a state, without generating text. Base Labs is Baseten's research lab.", "url": "https://www.anchorterminal.com/tools/mammoth-1", "markdownUrl": "https://www.anchorterminal.com/tools/mammoth-1.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/mammoth-1.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/mammoth-1.json", "repo": "https://huggingface.co/baselabs/mammoth-1", "license": "MIT, named only in the card's metadata (license: other, license_name: mit) with no LICENSE file. Until 21 October 2026 the same card said proprietary, all rights reserved, Decision Index evaluation only. The base, Qwen/Qwen3.5-27B, is Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "baselabs/mammoth-1" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-21). Baseten's hosted Model API for it needs a Baseten account and API key and isn't graded here.", "pricing": "free", "pricingNotes": "Free to download. The card's metadata names MIT, with no licence text in the repository. Running it costs the hardware, 54.7 GB of BF16 files by the Hub's file list. Base Labs publishes no quantised build. Baseten's Model APIs serve it as decision-models/baseten-mammoth-27b-latest, free for a week from 21 October 2026 per Baseten's blog, until 27 October 2026 at 11:59 PM PT per Baseten's announcement (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://labs.baseten.co/articles/mammoth-1-and-elephant-1-frontier-decision-models", "capabilities": [ "inference.decision", "model.open-weights" ], "tags": [ "open-weights", "model", "decision-model", "full-fine-tune", "calibrated-probabilities", "mit", "safetensors", "self-hosted" ], "lastRelease": "2026-10-21", "graded": true, "anchor": { "graded": true, "score": 47.7, "grade": "D", "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": 1239, "categoryRank": 21, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 55, "reach": 40, "risk": 50, "upkeep": 55 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "A cousin for typed decisions, several questions about one state answered with probabilities from one prefill. It needs about 66 GB in BF16 and a pinned, patched third-party runtime. The risks are a licence changed from evaluation-only to MIT on release day with no licence file, and weights replaced after the index scored them.", "bestFor": "Agents that want self-hosted routing, triage, classification or judging with probabilities to threshold, many questions per state, on one 80 GB or 96 GB GPU.", "strengths": [ "Several choice, yes-or-no and score questions answered together from one shared-prefix prefill, with no text to parse", "Tied for first on the community Jev Decision Index 0.4.1, first on 0.4, scored by the index's maintainer on private tests", "Not gated, safetensors only, in the organisation Baseten's own pages link to", "Baseten's own Model API serves it, free for its first week", "A detailed technical report on the architecture, training recipe and data mix" ], "weaknesses": [ "Runs as trained only on OneJev's qev runtime at a pinned commit with the repository's patch", "No quantised build, so about 66 GB is the floor", "Licence named only in metadata, changed from proprietary on 21 October 2026 with no note and no LICENSE file", "The main branch's weights differ from the revision the Jev Decision Index scored", "A 295-byte card with no intended use, limitations, context or cut-off, and no safety results" ], "agentNotes": [ "Run `bash setup.sh \u003cworkdir\u003e` in the pytorch/pytorch:2.8.0-cuda12.8-cudnn9-devel image on one CUDA GPU. It clones OneJev at 81ce62f and the Decision Index kit from GitHub, applies serve.patch and pip-installs them, so read it first", "Load through engine.py's DecisionModel (qev DecisionEngine). Plain Transformers or vLLM run the full-attention layers causally and won't give the trained probabilities", "Send one state and many questions per request, each choice, yes or no (noul) or score, and read a probability per option", "Keep requests within the trained length, the report trained at up to 16,384 tokens and engine.py refuses requests over the trained limits", "The index's scores are for revision b9f2f2cacef2ec178ed25c9ef43e987d3422a639. Pin that revision to get the scored weights, or main for the later ones", "Text only, the vision encoder is the base's, frozen and untrained for decisions" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.3, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "D", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 47.7 } ], "editorialScores": { "interface": 55, "reach": 40, "risk": 50, "upkeep": 55 }, "provenanceScore": 28, "checked": { "categories": { "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": "git clone https://huggingface.co/baselabs/mammoth-1 \u0026\u0026 cd mammoth-1\nbash setup.sh /tmp/w # clones OneJev @81ce62f, applies serve.patch, installs the Decision Index kit; one CUDA GPU, pytorch/pytorch:2.8.0-cuda12.8-cudnn9-devel" }, "letme": { "capability": "https://letme.dev/inference.decision", "tool": "https://letme.dev/mammoth-1" }, "sameCompany": [ "baseten", "elephant-1", "joey-1" ], "weights": { "trainedBy": "Base Labs (Baseten), full fine-tune of Alibaba's Qwen3.5-27B", "builtOn": "Qwen/Qwen3.5-27B (full fine-tune, Apache-2.0)", "params": "27B", "context": 262144, "goodFor": "Typed decisions with a probability per option, several questions about one state answered together from one prefill, for routing, triage, classification, scoring and judging.", "runtimes": [], "builds": [ { "name": "mammoth-1", "url": "https://huggingface.co/baselabs/mammoth-1", "who": "publisher", "precision": "bf16", "sizeGB": 54.7, "memoryGB": 96, "format": "safetensors", "runsOn": [ "OneJev qev @81ce62f + serve.patch" ], "note": "Two shards (54.71 GB) and the tokenizer, summed from the Hub's file list. The main branch's weights (20 October) differ from revision b9f2f2c, which the Jev Decision Index scored. No quantised build from Base Labs, and no Ollama or LM Studio library entry" } ], "run": "bash setup.sh /tmp/w \u0026\u0026 python -m decision_index pipeline --engine engine:DecisionModel --option model=. --option device=cuda:0 --suite-dir \u003csuite-0.3\u003e --out runs/mammoth-1", "runBuild": "mammoth-1", "checked": "2026-10-21", "minMemoryGB": 96 }, "family": { "lab": "Base Labs (Baseten)", "family": "Base Labs decision models (-1)", "url": "https://www.anchorterminal.com/open-weights/#baselabs", "others": [ { "name": "joey-1", "url": "https://huggingface.co/baselabs/joey-1", "params": "2B (2,721,801,024 by the Hub's safetensors count)", "listing": "https://www.anchorterminal.com/tools/joey-1" }, { "name": "elephant-1", "url": "https://huggingface.co/baselabs/elephant-1", "params": "9B (9,409,813,744 by the Hub's safetensors count)", "listing": "https://www.anchorterminal.com/tools/elephant-1" } ] }, "area": "models", "provenance": { "legalEntity": "", "domain": "baseten.co", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "Base Labs' site at labs.baseten.co says it is a research lab by Baseten, and Baseten's blog links to the baselabs organisation. Baseten is listed as baseten, whose terms name Baseten Labs, Inc. No legal entity is named in this repository.", "Nothing is hosted from the weights, so the licence stands in for terms of service and no privacy policy is scored.", "No changelog. The commit messages on the Hub are Add weights and Update README.md.", "www.baseten.co/.well-known/security.txt returns 404. Baseten's trust centre gives a security contact for coordinated disclosure, per its listing." ], "score": 28 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/mammoth-1.json", "live": { "slug": "mammoth-1", "securityTxt": { "url": "https://baseten.co/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-30T15:37:47.163946348Z" }, "domain": { "domain": "baseten.co", "checkedAt": "2026-10-25T13:53:50.96331261Z" }, "updatedAt": "2026-10-30T15:37:47.163946348Z" } }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Baseten", "b": "Baseten", "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, named in the card's metadata (license: mit) since the upload of 27 October 2026, with no LICENSE file. The base Qwen/Qwen3.5-2B is Apache-2.0", "b": "MIT, named only in the card's metadata (license: other, license_name: mit) with no LICENSE file. Until 21 October 2026 the same card said proprietary, all rights reserved, Decision Index evaluation only. The base, Qwen/Qwen3.5-27B, is Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-29", "b": "2026-10-21", "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": "none", "b": "2.3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "joey-1 scores 57.1 (C) on agent readiness against mammoth-1's 47.7 (D), and leads in 3 of 4 scored categories.", "question": "Which is better for AI agents, joey-1 or mammoth-1?" } ], "goodFor": [ { "aheadOn": [ "Reach, 60 against 40", "Risk, 55 against 50", "Agent interface, 70 against 55" ], "also": null, "goodFor": "Agents that want self-hosted, low-cost ticket routing or guardrail checks with probabilities to threshold, on a GPU with 8 GB, and can accept a patched runtime.", "slug": "joey-1", "watchFor": "Serving it needs OneJev at a pinned commit plus the repository's serve.patch, with no released runtime support" }, { "aheadOn": null, "also": null, "goodFor": "Agents that want self-hosted routing, triage, classification or judging with probabilities to threshold, many questions per state, on one 80 GB or 96 GB GPU.", "slug": "mammoth-1", "watchFor": "Runs as trained only on OneJev's qev runtime at a pinned commit with the repository's patch" } ], "job": { "capability": "inference.decision", "name": "Decision models" }, "others": [ { "json": "https://www.anchorterminal.com/compare/elephant-1-vs-joey-1.json", "title": "elephant-1 vs joey-1", "url": "https://www.anchorterminal.com/compare/elephant-1-vs-joey-1" }, { "json": "https://www.anchorterminal.com/compare/elephant-1-vs-mammoth-1.json", "title": "elephant-1 vs mammoth-1", "url": "https://www.anchorterminal.com/compare/elephant-1-vs-mammoth-1" }, { "json": "https://www.anchorterminal.com/compare/jev-27b-vl-vs-joey-1.json", "title": "JEV-27B-VL vs joey-1", "url": "https://www.anchorterminal.com/compare/jev-27b-vl-vs-joey-1" }, { "json": "https://www.anchorterminal.com/compare/jev-27b-vl-vs-mammoth-1.json", "title": "JEV-27B-VL vs mammoth-1", "url": "https://www.anchorterminal.com/compare/jev-27b-vl-vs-mammoth-1" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 20, "edge": "joey-1", "joey-1": 60, "key": "reach", "mammoth-1": 40, "name": "Reach", "weight": 25 }, { "by": 5, "edge": "joey-1", "joey-1": 55, "key": "risk", "mammoth-1": 50, "name": "Risk", "weight": 20 }, { "by": 15, "edge": "joey-1", "joey-1": 70, "key": "interface", "mammoth-1": 55, "name": "Agent interface", "weight": 10 }, { "by": 1, "edge": "mammoth-1", "joey-1": 54, "key": "upkeep", "mammoth-1": 55, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "joey-1 scores 57.1 (C) on agent readiness against mammoth-1's 47.7 (D), and leads in 3 of 4 scored categories. Both do decision models.", "tutorials": [], "verdicts": { "joey-1": "A very small cousin for routing, triage and guardrail checks, answering several typed questions about one state, with text or images, as probabilities from one prefill. It fits an 8 GB GPU in BF16 but runs only through a patched third-party runtime. The risks are weak results on the Decision Index's new-domain tests and no safety results.", "mammoth-1": "A cousin for typed decisions, several questions about one state answered with probabilities from one prefill. It needs about 66 GB in BF16 and a pinned, patched third-party runtime. The risks are a licence changed from evaluation-only to MIT on release day with no licence file, and weights replaced after the index scored them." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/joey-1-vs-mammoth-1", "json": "https://www.anchorterminal.com/compare/joey-1-vs-mammoth-1.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/joey-1-vs-mammoth-1.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/joey-1-vs-mammoth-1.min.md" }, "markdown": "joey-1 scores 57.1 (C) on agent readiness against mammoth-1's 47.7 (D), and leads in 3 of 4 scored categories. Both do decision models.\n\n- joey-1: grade C, 57.1/100, rank #78 of 117 open-weight models. Markdown https://www.anchorterminal.com/tools/joey-1.md · JSON https://www.anchorterminal.com/api/v1/tools/joey-1.json\n- mammoth-1: grade D, 47.7/100, rank #112 of 117 open-weight models. Markdown https://www.anchorterminal.com/tools/mammoth-1.md · JSON https://www.anchorterminal.com/api/v1/tools/mammoth-1.json\n- Best decision models for AI agents: https://www.anchorterminal.com/best/decision-models/index.md\n- All 196 decisions comparisons: https://www.anchorterminal.com/compare/decision-models/index.md\n\n## Which one, for what\n\n### joey-1 (C)\n\nGood for: Agents that want self-hosted, low-cost ticket routing or guardrail checks with probabilities to threshold, on a GPU with 8 GB, and can accept a patched runtime.\n\nAhead on:\n- Reach, 60 against 40\n- Risk, 55 against 50\n- Agent interface, 70 against 55\n\nWatch for: Serving it needs OneJev at a pinned commit plus the repository's serve.patch, with no released runtime support\n\n### mammoth-1 (D)\n\nGood for: Agents that want self-hosted routing, triage, classification or judging with probabilities to threshold, many questions per state, on one 80 GB or 96 GB GPU.\n\nWatch for: Runs as trained only on OneJev's qev runtime at a pinned commit with the repository's patch\n\n\n## Score by category\n\n| Category | Weight | joey-1 | mammoth-1 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 60 | 40 | joey-1 +20 |\n| Risk | 20% (20 this run) | 55 | 50 | joey-1 +5 |\n| Agent interface | 10% (10 this run) | 70 | 55 | joey-1 +15 |\n| Documentation and upkeep | 10% (10 this run) | 54 | 55 | mammoth-1 +1 |\n| Negative events | ≤15 | -2 | 0 | |\n| **Total** | | **57.1 · C** | **47.7 · D** | |\n\n## Facts side by side\n\n| Fact | joey-1 | mammoth-1 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Baseten | Baseten |\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, named in the card's metadata (license: mit) since the upload of 27 October 2026, with no LICENSE file. The base Qwen/Qwen3.5-2B is Apache-2.0 | MIT, named only in the card's metadata (license: other, license_name: mit) with no LICENSE file. Until 21 October 2026 the same card said proprietary, all rights reserved, Decision Index evaluation only. The base, Qwen/Qwen3.5-27B, is Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-29 | 2026-10-21 |\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| Agent reviews | none | 2.3/5 (4) |\n\n## Verdicts\n\n**joey-1.** A very small cousin for routing, triage and guardrail checks, answering several typed questions about one state, with text or images, as probabilities from one prefill. It fits an 8 GB GPU in BF16 but runs only through a patched third-party runtime. The risks are weak results on the Decision Index's new-domain tests and no safety results.\n\n**mammoth-1.** A cousin for typed decisions, several questions about one state answered with probabilities from one prefill. It needs about 66 GB in BF16 and a pinned, patched third-party runtime. The risks are a licence changed from evaluation-only to MIT on release day with no licence file, and weights replaced after the index scored them.\n\n## Before you call either\n\n### joey-1\n\n1. Start on a CUDA GPU in pytorch/pytorch:2.8.0-cuda12.8-cudnn9-devel with `hf download baselabs/joey-1 --local-dir joey-1 \u0026\u0026 bash joey-1/setup.sh`, which clones OneJev at 81ce62f, applies serve.patch and installs the Decision Index kit\n2. Then call `DecisionEngine(\"baselabs/joey-1\", device=\"cuda:0\").decide(SystemOneRequest(state=..., questions=...))` from qev, as the card shows\n3. Don't load it with stock vLLM or Transformers. They ignore the block-bidirectional attention in config.json's modeling_overrides and give probabilities the model wasn't trained for\n4. Keep each request under 16,384 tokens, the trained length, or the engine refuses it\n5. Expect weaker answers on domains unlike its training mix, and threshold on the returned probability before acting\n6. Without hardware, Baseten's /v1/systemone serves it as decision-models/baseten-joey-2b-latest with a Baseten key\n\n### mammoth-1\n\n1. Run `bash setup.sh \u003cworkdir\u003e` in the pytorch/pytorch:2.8.0-cuda12.8-cudnn9-devel image on one CUDA GPU. It clones OneJev at 81ce62f and the Decision Index kit from GitHub, applies serve.patch and pip-installs them, so read it first\n2. Load through engine.py's DecisionModel (qev DecisionEngine). Plain Transformers or vLLM run the full-attention layers causally and won't give the trained probabilities\n3. Send one state and many questions per request, each choice, yes or no (noul) or score, and read a probability per option\n4. Keep requests within the trained length, the report trained at up to 16,384 tokens and engine.py refuses requests over the trained limits\n5. The index's scores are for revision b9f2f2cacef2ec178ed25c9ef43e987d3422a639. Pin that revision to get the scored weights, or main for the later ones\n6. Text only, the vision encoder is the base's, frozen and untrained for decisions\n\n## Questions\n\n### Which is better for AI agents, joey-1 or mammoth-1?\n\njoey-1 scores 57.1 (C) on agent readiness against mammoth-1's 47.7 (D), and leads in 3 of 4 scored categories.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/joey-1-vs-mammoth-1.json, and with the fewest tokens: https://www.anchorterminal.com/compare/joey-1-vs-mammoth-1.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"joey-1\", \"b\": \"mammoth-1\"}`. From a terminal: `anchor compare joey-1 mammoth-1`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/joey-1.json and https://www.anchorterminal.com/api/v1/tools/mammoth-1.json\n\n## Other comparisons with joey-1 or mammoth-1\n\n- [elephant-1 vs joey-1](https://www.anchorterminal.com/compare/elephant-1-vs-joey-1.md)\n- [elephant-1 vs mammoth-1](https://www.anchorterminal.com/compare/elephant-1-vs-mammoth-1.md)\n- [JEV-27B-VL vs joey-1](https://www.anchorterminal.com/compare/jev-27b-vl-vs-joey-1.md)\n- [JEV-27B-VL vs mammoth-1](https://www.anchorterminal.com/compare/jev-27b-vl-vs-mammoth-1.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": "2026-10-31", "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": "joey-1 vs mammoth-1", "url": "" } ], "description": "joey-1 scores 57.1 (C) to mammoth-1's 47.7 (D) for decision models. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "joey-1 C 57.1", "mammoth-1 D 47.7", "scores" ], "h1": "joey-1 vs mammoth-1", "image": "https://www.anchorterminal.com/assets/og/compare-joey-1-vs-mammoth-1.png", "path": "/compare/joey-1-vs-mammoth-1", "published": "2026-10-01", "section": "tools", "title": "joey-1 vs mammoth-1 for AI agents in 2026: scores and prices", "toc": null, "updated": "2026-10-30", "url": "https://www.anchorterminal.com/compare/joey-1-vs-mammoth-1" }, "tokens": { "markdown": 1900, "slim": 480 }, "version": 1 }