{ "data": { "a": { "slug": "elephant-1", "name": "elephant-1", "vendor": "Baseten", "vendorUrl": "https://labs.baseten.co", "kind": "weights", "category": "decision-models", "summary": "elephant-1 is Base Labs' open-weight 9B decision model, a full fine-tune of Qwen3.5-9B that 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/elephant-1", "markdownUrl": "https://www.anchorterminal.com/tools/elephant-1.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/elephant-1.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/elephant-1.json", "repo": "https://huggingface.co/baselabs/elephant-1", "license": "MIT, named only in the card's metadata (license: other, license_name: mit) since 21 October 2026, with no LICENSE file. Until then the card said proprietary, all rights reserved, access for Decision Index evaluation only. The base Qwen/Qwen3.5-9B is Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "baselabs/elephant-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 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, 18.8 GB of BF16 files by the Hub's file list. Baseten also serves it on its Model APIs as decision-models/baseten-elephant-9b-latest, free for a week from 21 October 2026 per Baseten's blog (the announcement gives the end as 27 October 2026, 11:59 PM PT), with no price on the pricing page after that (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": 47.6, "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": 22, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 45, "reach": 55, "risk": 50, "upkeep": 40 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": -2, "negativeNotes": [ "21 October 2026. Baseten's blog says elephant-1 is tied for first with d1-ultra on the Artificial Analysis Decision Index, and its library page gives elephant-1 a score of 65.2 there. Artificial Analysis's own page that day lists d1-ultra at 65.25 and Elephant 9B at 64.69, second of the decision models, and states no tie rule. 65.2 is the Base Labs report's own replica figure. The two only match when rounded to whole numbers. Sources: https://www.baseten.co/blog/the-best-decision-models-in-the-world-mammoth-1-and-elephant-1/ , https://www.baseten.co/library/elephant-1/ and https://artificialanalysis.ai/evaluations/artificial-analysis-decision-index (-2)" ], "verdict": "A small cousin for routing, triage and judging, answering several typed questions about one state with probabilities in one prefill. It fits a 24 GB GPU in BF16 but runs only through a patched third-party runtime. The risks are a licence switched from proprietary to MIT on release day with no licence file, and a thin card.", "bestFor": "Agents that want self-hosted ticket routing, guardrail checks or rubric scoring with probabilities to threshold, on one 24 GB GPU, and can accept a patched runtime.", "strengths": [ "Probabilities for choice, yes or no and score questions, several per request from one shared prefill", "18.8 GB in BF16, so it fits a 24 GB GPU", "Highest model of 8B to 14B on the Jev Decision Index 0.4.1 at 60.26, run on private tests by the index", "Not gated, and Baseten's own Model API serves the same model" ], "weaknesses": [ "Serving it needs OneJev at a pinned commit plus the repository's serve.patch, with no released runtime support", "The licence was proprietary until release day, and MIT now appears only in the metadata with no licence file", "Trained at 16,384 tokens, and longer requests are refused", "The card is two sentences, with no limitations, languages or cut-off", "No quantised build, no safety results and no prompt-injection guidance" ], "agentNotes": [ "Start on a CUDA GPU in pytorch/pytorch:2.8.0-cuda12.8-cudnn9-devel with `git clone https://huggingface.co/baselabs/elephant-1 \u0026\u0026 cd elephant-1 \u0026\u0026 bash setup.sh /tmp/w`, which clones OneJev at 81ce62f, applies serve.patch and installs the Decision Index kit", "Then `qev serve --model . --port 8000` is OneJev's documented server. Base Labs doesn't document it for this checkpoint, so check the answers before relying on them", "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", "Group the questions about one state in one request, since the state is read once and shared", "Without hardware, Baseten's /v1/systemone serves it as decision-models/baseten-elephant-9b-latest with a Baseten key, free until the end of the launch week" ], "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": 47.6 } ], "editorialScores": { "interface": 45, "reach": 55, "risk": 50, "upkeep": 40 }, "provenanceScore": 50, "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/elephant-1 \u0026\u0026 cd elephant-1\nbash setup.sh /tmp/w # clones OneJev at 81ce62f, applies serve.patch, installs the Decision Index kit (CUDA image pytorch/pytorch:2.8.0-cuda12.8-cudnn9-devel)", "http": "qev serve --model . --port 8000 # OneJev's System One server; its README documents the command, Base Labs doesn't" }, "letme": { "capability": "https://letme.dev/inference.decision", "tool": "https://letme.dev/elephant-1" }, "sameCompany": [ "baseten", "joey-1", "mammoth-1" ], "weights": { "trainedBy": "Base Labs (Baseten), on Alibaba's Qwen3.5-9B", "builtOn": "Qwen/Qwen3.5-9B (full fine-tune, Apache-2.0)", "params": "9B", "context": 16384, "goodFor": "Typed decisions with calibrated probabilities over text, several choice, yes or no and score questions about one state in one prefill, for routing, triage, guardrails and judging.", "runtimes": [ "OneJev qev (patched)" ], "builds": [ { "name": "elephant-1", "url": "https://huggingface.co/baselabs/elephant-1", "who": "publisher", "precision": "bf16", "sizeGB": 18.8, "memoryGB": 24, "format": "safetensors", "runsOn": [ "OneJev qev with serve.patch" ], "note": "model.safetensors (18.82 GB), tokenizer, configs and serving bundle, 18.84 GB summed from the Hub's file list. 18.84 × 1.2 = 22.6 GB. No quantised build from Base Labs and no Ollama or LM Studio library entry" } ], "run": "bash setup.sh /tmp/w \u0026\u0026 qev serve --model . --port 8000", "runBuild": "elephant-1", "checked": "2026-10-21", "minMemoryGB": 24 }, "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": "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": "2026-10-21", "notes": [ "Base Labs is Baseten's research lab (labs.baseten.co, 'A research lab by Baseten'). 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 report's authors. Baseten's library page, blog and report link to it.", "Nothing is hosted under this listing, so the licence stands in for terms of service and no privacy policy is scored.", "No changelog. www.baseten.co/.well-known/security.txt doesn't exist (an HTML page comes back)." ], "score": 50 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/elephant-1.json", "live": { "slug": "elephant-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" } }, "answer": "joey-1 scores 57.1 (C) on agent readiness against elephant-1's 47.6 (D), and leads in every scored category.", "b": { "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" }, "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 only in the card's metadata (license: other, license_name: mit) since 21 October 2026, with no LICENSE file. Until then the card said proprietary, all rights reserved, access for Decision Index evaluation only. The base Qwen/Qwen3.5-9B is Apache-2.0", "b": "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", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-21", "b": "2026-10-29", "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": "2.3/5 (4)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "joey-1 scores 57.1 (C) on agent readiness against elephant-1's 47.6 (D), and leads in every scored category.", "question": "Which is better for AI agents, elephant-1 or joey-1?" } ], "goodFor": [ { "aheadOn": null, "also": null, "goodFor": "Agents that want self-hosted ticket routing, guardrail checks or rubric scoring with probabilities to threshold, on one 24 GB GPU, and can accept a patched runtime.", "slug": "elephant-1", "watchFor": "Serving it needs OneJev at a pinned commit plus the repository's serve.patch, with no released runtime support" }, { "aheadOn": [ "Reach, 60 against 55", "Risk, 55 against 50", "Agent interface, 70 against 45", "Documentation and upkeep, 54 against 40" ], "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" } ], "job": { "capability": "inference.decision", "name": "Decision models" }, "others": [ { "json": "https://www.anchorterminal.com/compare/elephant-1-vs-jev-27b-vl.json", "title": "elephant-1 vs JEV-27B-VL", "url": "https://www.anchorterminal.com/compare/elephant-1-vs-jev-27b-vl" }, { "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/joey-1-vs-mammoth-1.json", "title": "joey-1 vs mammoth-1", "url": "https://www.anchorterminal.com/compare/joey-1-vs-mammoth-1" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 5, "edge": "joey-1", "elephant-1": 55, "joey-1": 60, "key": "reach", "name": "Reach", "weight": 25 }, { "by": 5, "edge": "joey-1", "elephant-1": 50, "joey-1": 55, "key": "risk", "name": "Risk", "weight": 20 }, { "by": 25, "edge": "joey-1", "elephant-1": 45, "joey-1": 70, "key": "interface", "name": "Agent interface", "weight": 10 }, { "by": 14, "edge": "joey-1", "elephant-1": 40, "joey-1": 54, "key": "upkeep", "name": "Documentation and upkeep", "weight": 10 } ], "summary": "joey-1 scores 57.1 (C) on agent readiness against elephant-1's 47.6 (D), and leads in every scored category. Both do decision models.", "tutorials": [], "verdicts": { "elephant-1": "A small cousin for routing, triage and judging, answering several typed questions about one state with probabilities in one prefill. It fits a 24 GB GPU in BF16 but runs only through a patched third-party runtime. The risks are a licence switched from proprietary to MIT on release day with no licence file, and a thin card.", "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." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/elephant-1-vs-joey-1", "json": "https://www.anchorterminal.com/compare/elephant-1-vs-joey-1.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/elephant-1-vs-joey-1.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/elephant-1-vs-joey-1.min.md" }, "markdown": "joey-1 scores 57.1 (C) on agent readiness against elephant-1's 47.6 (D), and leads in every scored category. Both do decision models.\n\n- elephant-1: grade D, 47.6/100, rank #113 of 117 open-weight models. Markdown https://www.anchorterminal.com/tools/elephant-1.md · JSON https://www.anchorterminal.com/api/v1/tools/elephant-1.json\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- 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### elephant-1 (D)\n\nGood for: Agents that want self-hosted ticket routing, guardrail checks or rubric scoring with probabilities to threshold, on one 24 GB GPU, and can accept a patched runtime.\n\nWatch for: Serving it needs OneJev at a pinned commit plus the repository's serve.patch, with no released runtime support\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 55\n- Risk, 55 against 50\n- Agent interface, 70 against 45\n- Documentation and upkeep, 54 against 40\n\nWatch for: Serving it needs OneJev at a pinned commit plus the repository's serve.patch, with no released runtime support\n\n\n## Score by category\n\n| Category | Weight | elephant-1 | joey-1 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 55 | 60 | joey-1 +5 |\n| Risk | 20% (20 this run) | 50 | 55 | joey-1 +5 |\n| Agent interface | 10% (10 this run) | 45 | 70 | joey-1 +25 |\n| Documentation and upkeep | 10% (10 this run) | 40 | 54 | joey-1 +14 |\n| Negative events | ≤15 | -2 | -2 | |\n| **Total** | | **47.6 · D** | **57.1 · C** | |\n\n## Facts side by side\n\n| Fact | elephant-1 | joey-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 only in the card's metadata (license: other, license_name: mit) since 21 October 2026, with no LICENSE file. Until then the card said proprietary, all rights reserved, access for Decision Index evaluation only. The base Qwen/Qwen3.5-9B is Apache-2.0 | 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 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-21 | 2026-10-29 |\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 | 2.3/5 (4) | none |\n\n## Verdicts\n\n**elephant-1.** A small cousin for routing, triage and judging, answering several typed questions about one state with probabilities in one prefill. It fits a 24 GB GPU in BF16 but runs only through a patched third-party runtime. The risks are a licence switched from proprietary to MIT on release day with no licence file, and a thin card.\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## Before you call either\n\n### elephant-1\n\n1. Start on a CUDA GPU in pytorch/pytorch:2.8.0-cuda12.8-cudnn9-devel with `git clone https://huggingface.co/baselabs/elephant-1 \u0026\u0026 cd elephant-1 \u0026\u0026 bash setup.sh /tmp/w`, which clones OneJev at 81ce62f, applies serve.patch and installs the Decision Index kit\n2. Then `qev serve --model . --port 8000` is OneJev's documented server. Base Labs doesn't document it for this checkpoint, so check the answers before relying on them\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. Group the questions about one state in one request, since the state is read once and shared\n6. Without hardware, Baseten's /v1/systemone serves it as decision-models/baseten-elephant-9b-latest with a Baseten key, free until the end of the launch week\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## Questions\n\n### Which is better for AI agents, elephant-1 or joey-1?\n\njoey-1 scores 57.1 (C) on agent readiness against elephant-1's 47.6 (D), and leads in every scored category.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/elephant-1-vs-joey-1.json, and with the fewest tokens: https://www.anchorterminal.com/compare/elephant-1-vs-joey-1.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"elephant-1\", \"b\": \"joey-1\"}`. From a terminal: `anchor compare elephant-1 joey-1`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/elephant-1.json and https://www.anchorterminal.com/api/v1/tools/joey-1.json\n\n## Other comparisons with elephant-1 or joey-1\n\n- [elephant-1 vs JEV-27B-VL](https://www.anchorterminal.com/compare/elephant-1-vs-jev-27b-vl.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- [joey-1 vs mammoth-1](https://www.anchorterminal.com/compare/joey-1-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": "elephant-1 vs joey-1", "url": "" } ], "description": "joey-1 scores 57.1 (C) to elephant-1's 47.6 (D) for decision models. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "elephant-1 D 47.6", "joey-1 C 57.1", "scores" ], "h1": "elephant-1 vs joey-1", "image": "https://www.anchorterminal.com/assets/og/compare-elephant-1-vs-joey-1.png", "path": "/compare/elephant-1-vs-joey-1", "published": "2026-10-01", "section": "tools", "title": "elephant-1 vs joey-1 for AI agents in 2026: scores and prices", "toc": null, "updated": "2026-10-30", "url": "https://www.anchorterminal.com/compare/elephant-1-vs-joey-1" }, "tokens": { "markdown": 1900, "slim": 530 }, "version": 1 }