{ "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": 1199, "categoryRank": 21, "methodology": "0.6", "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": "0.6", "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": 40, "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", "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 }, "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": 40 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/elephant-1.json" }, "answer": "mammoth-1 and elephant-1 score within a point of each other on agent readiness, 47.7 (D) and 47.6 (D). elephant-1 leads on reach.", "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": 1199, "categoryRank": 20, "methodology": "0.6", "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": "0.6", "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": 13, "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" ], "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 }, "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": 13 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/mammoth-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 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": "2026-10-21", "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": "2.3/5 (4)", "b": "2.3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "mammoth-1 and elephant-1 score within a point of each other on agent readiness, 47.7 (D) and 47.6 (D). elephant-1 leads on reach.", "question": "Which is better for AI agents, elephant-1 or mammoth-1?" } ], "goodFor": [ { "aheadOn": [ "Reach, 55 against 40" ], "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": [ "Agent interface, 55 against 45", "Documentation and upkeep, 55 against 40" ], "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-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/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": 15, "edge": "elephant-1", "elephant-1": 55, "key": "reach", "mammoth-1": 40, "name": "Reach", "weight": 25 }, { "by": 0, "edge": "", "elephant-1": 50, "key": "risk", "mammoth-1": 50, "name": "Risk", "weight": 20 }, { "by": 10, "edge": "mammoth-1", "elephant-1": 45, "key": "interface", "mammoth-1": 55, "name": "Agent interface", "weight": 10 }, { "by": 15, "edge": "mammoth-1", "elephant-1": 40, "key": "upkeep", "mammoth-1": 55, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "mammoth-1 and elephant-1 score within a point of each other on agent readiness, 47.7 (D) and 47.6 (D). elephant-1 leads on reach. 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.", "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/elephant-1-vs-mammoth-1", "json": "https://www.anchorterminal.com/compare/elephant-1-vs-mammoth-1.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/elephant-1-vs-mammoth-1.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/elephant-1-vs-mammoth-1.min.md" }, "markdown": "mammoth-1 and elephant-1 score within a point of each other on agent readiness, 47.7 (D) and 47.6 (D). elephant-1 leads on reach. Both do decision models.\n\n- elephant-1: grade D, 47.6/100, rank #98 of 102 open-weight models. Markdown https://www.anchorterminal.com/tools/elephant-1.md · JSON https://www.anchorterminal.com/api/v1/tools/elephant-1.json\n- mammoth-1: grade D, 47.7/100, rank #97 of 102 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 193 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\nAhead on:\n- Reach, 55 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### 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\nAhead on:\n- Agent interface, 55 against 45\n- Documentation and upkeep, 55 against 40\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 | elephant-1 | mammoth-1 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 55 | 40 | elephant-1 +15 |\n| Risk | 20% (20 this run) | 50 | 50 | even |\n| Agent interface | 10% (10 this run) | 45 | 55 | mammoth-1 +10 |\n| Documentation and upkeep | 10% (10 this run) | 40 | 55 | mammoth-1 +15 |\n| Negative events | ≤15 | -2 | 0 | |\n| **Total** | | **47.6 · D** | **47.7 · D** | |\n\n## Facts side by side\n\n| Fact | elephant-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 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 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 | 2026-10-21 | 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 | 2.3/5 (4) | 2.3/5 (4) |\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**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### 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### 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, elephant-1 or mammoth-1?\n\nmammoth-1 and elephant-1 score within a point of each other on agent readiness, 47.7 (D) and 47.6 (D). elephant-1 leads on reach.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/elephant-1-vs-mammoth-1.json, and with the fewest tokens: https://www.anchorterminal.com/compare/elephant-1-vs-mammoth-1.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"elephant-1\", \"b\": \"mammoth-1\"}`. From a terminal: `anchor compare elephant-1 mammoth-1`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/elephant-1.json and https://www.anchorterminal.com/api/v1/tools/mammoth-1.json\n\n## Other comparisons with elephant-1 or mammoth-1\n\n- [elephant-1 vs JEV-27B-VL](https://www.anchorterminal.com/compare/elephant-1-vs-jev-27b-vl.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": "2026-10-22", "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": "elephant-1 vs mammoth-1", "url": "" } ], "description": "mammoth-1 and elephant-1 score within a point of each other for decision models, 47.7 and 47.6 out of 100. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "elephant-1 D 47.6", "mammoth-1 D 47.7", "scores" ], "h1": "elephant-1 vs mammoth-1", "image": "https://www.anchorterminal.com/assets/og/compare-elephant-1-vs-mammoth-1.png", "path": "/compare/elephant-1-vs-mammoth-1", "published": "2026-10-01", "section": "tools", "title": "elephant-1 vs mammoth-1 for AI agents in 2026: scores and prices", "toc": null, "updated": "2026-10-22", "url": "https://www.anchorterminal.com/compare/elephant-1-vs-mammoth-1" }, "tokens": { "markdown": 1900, "slim": 530 }, "version": 1 }