{ "data": { "a": { "slug": "drex-max-v1", "name": "Drex Max v1", "vendor": "Nace AI, Inc. (Nace.AI)", "vendorUrl": "https://www.nace.ai", "kind": "weights", "category": "decision-models", "summary": "Drex Max v1 is Nace.AI's open-weight 27B decision model, a fine-tune of Qwen3.8-27B that takes text, JSON and images with typed questions and returns a probability for every option without generating text.", "url": "https://www.anchorterminal.com/tools/drex-max-v1", "markdownUrl": "https://www.anchorterminal.com/tools/drex-max-v1.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/drex-max-v1.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/drex-max-v1.json", "repo": "https://huggingface.co/nace-ai/drex-max-v1", "license": "Nace.AI Open RAIL-M License (modified), version 1.0, October 2026, in the repository's LICENSE. Not an open-source licence. Use is free only for individuals and organisations under $1 million in revenue in the prior 12 months and under $1 million in total funding (personal and research use excepted), never by anyone with a product that competes with Nace's, decision models included, and under use restrictions that bind the model, its derivatives and its outputs. Built on Qwen/Qwen3.8-27B (Apache-2.0), whose licence text isn't carried in the repository", "transports": [], "packages": [ { "registry": "huggingface", "name": "nace-ai/drex-max-v1" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository and both quantised builds are not gated (checked 2026-10-30). The licence says downloading binds you to its terms. Nace's hosted API for the same model needs a nace_sk_ key.", "pricing": "free", "pricingNotes": "Free to download. Commercial use by anyone above $1 million in revenue or funding, or with a product that competes with Nace's, needs a separate licence from Nace.AI. Running it costs the hardware, from 14.5 GB of files (affine 4-bit, text only) to 52.2 GB (BF16). Nace's API serves it as drex-max at $0.03 per million input tokens, with output not billed (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://github.com/nace-ai/drex-decision-models/blob/main/models/drex-max-v1", "capabilities": [ "inference.decision", "model.open-weights" ], "tags": [ "open-weights", "model", "decision-model", "full-fine-tune", "calibrated-probabilities", "vision", "open-rail-m", "safetensors", "gguf", "self-hosted", "apple-silicon" ], "lastRelease": "2026-10-30", "graded": true, "anchor": { "graded": true, "score": 55.5, "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": 1243, "categoryRank": 17, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 80, "reach": 65, "risk": 45, "upkeep": 41 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-30" }, "negative": -2, "negativeNotes": [ "19 October 2026, still on the page on 30 October. Nace's blog says Drex Max is 'First among decision models on the public Decision Index 0.4.0'. The index (multimodalart/jev-decision-index, data generated 29 October 2026) ranks it 23rd of 247 by public score at 67.52, behind mammoth-1 (75.34) among others, and 24th of 247 by the Full score it ranks by (66.6). Its data of 16 October, before the blog, already had StartLux-Decision v1.1 27B (72.88), KnowLine 35B-A3B Gen1 (69.49) and Perplexity Decider v1.2 (67.74) above 67.52 on the public score. The Vision board ranks it 11th of 62. Sources: https://www.nace.ai/blog/introducing-drex-max and https://huggingface.co/spaces/multimodalart/jev-decision-index/blob/main/data/index.json (-2)" ], "verdict": "A 27B cousin for typed decisions over text and screenshots, several questions per request with a probability for each option. Images need an NVIDIA GPU and Nace's loader; on Apple silicon only the text backbone runs, from 24 GB. The licence isn't open source, with free commercial use only under $1 million in revenue and funding.", "bestFor": "Agents that want self-hosted routing, triage or screen decisions with probabilities, on a CUDA GPU with 48 GB or more for images or a 24 GB Mac for text, where the Open RAIL-M terms fit.", "strengths": [ "Probabilities for choice, yes or no and score questions over text, JSON and images, with nothing generated", "Full score 66.6 on the Jev Decision Index 0.4.1, 24th of 247, and 11th of 62 on its Vision board, from the index's private tests", "Nace publishes a Q8_0 GGUF and an affine 4-bit build, the smaller at 14.5 GB for Macs with 24 GB", "Not gated, and Nace's own API serves it as drex-max at $0.03 per million input tokens", "262,144-token window in config.json and the docs, with a 16,384-token default you can raise" ], "weaknesses": [ "The licence bars commercial use above $1 million in revenue or funding and by anyone with a competing product, and restricts the outputs too", "Every path that runs it as trained is Nace's own code or forks. Stock llama.cpp, Ollama, vLLM and Transformers run it causally", "Images need the Python loader on a CUDA GPU; the GGUF and 4-bit builds read text only", "The card gives no limitations, languages, cut-off or training data, and dropped its Qwen3.8-27B base on 30 October 2026", "No safety results and no prompt-injection guidance on the card" ], "agentNotes": [ "Clone github.com/nace-ai/drex-decision-models, download nace-ai/drex-max-v1 and run `python -m drex_max --model weights/drex-max-v1 --request examples/request.json` on an NVIDIA GPU", "Plan for about 49 GB of GPU memory in BF16, or about 26 GB with --8bit on CUDA, before any long context", "Don't load it with stock Transformers, vLLM or llama.cpp. Its full-attention layers must run non-causally with readout.safetensors applied, which only Nace's loader and forks do", "On a Mac with 24 GB or more, run `kapitsa-systemone --model ./drex-max-v1-affine-q4 --port 8000 --context 2048` for text-only decisions at POST /v1/systemone", "Raise the 16,384-token default with DREX_MAX_CONTEXT=N or --max-length N, up to 262,144, and expect more memory", "Check the licence first. Above $1 million in revenue or funding, or with a competing product, you need Nace's commercial licence" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 55.5 } ], "editorialScores": { "interface": 80, "reach": 65, "risk": 45, "upkeep": 41 }, "provenanceScore": 58, "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": "git clone https://github.com/nace-ai/drex-decision-models \u0026\u0026 cd drex-decision-models\nhf download nace-ai/drex-max-v1 --local-dir weights/drex-max-v1\npip install -r models/drex-max-v1/requirements.txt\npython -m drex_max --model weights/drex-max-v1 --request examples/request.json # NVIDIA GPU, Python 3.12+; add --8bit for about 26 GB", "http": "kapitsa-systemone --model ./drex-max-v1-affine-q4 --port 8000 --context 2048 # Apple silicon, text only, after brew install nace-ai/tap/kapitsa and hf download nace-ai/drex-max-v1-affine-q4" }, "letme": { "capability": "https://letme.dev/inference.decision", "tool": "https://letme.dev/drex-max-v1" }, "sameModel": [ "nace-drex" ], "weights": { "trainedBy": "Nace.AI, on Alibaba's Qwen3.8-27B", "builtOn": "Qwen/Qwen3.8-27B (full fine-tune with a 255-way readout head, Apache-2.0)", "params": "27B", "context": 262144, "goodFor": "Typed decisions with a probability per option over text, JSON and images, such as routing, triage and the next click in a computer-use loop, several questions per request, with no text generated.", "runtimes": [ "Transformers (Nace's loader)", "llama.cpp (Nace fork, text only)", "Ollama (Nace fork, text only)", "Kapitsa (Nace, text only)", "MLX (Nace script, text only)" ], "builds": [ { "name": "drex-max-v1-affine-q4", "url": "https://huggingface.co/nace-ai/drex-max-v1-affine-q4", "who": "publisher", "precision": "affine 4-bit, group 64", "sizeGB": 14.5, "memoryGB": 24, "format": "safetensors (MLX)", "runsOn": [ "Kapitsa 0.2.25+", "MLX (drex_max_mlx.py)" ], "note": "model.safetensors (14.45 GB), pointer.bin (the readout, raw KPS1 format), tokenizer and configs, 14.47 GB summed from the Hub's file list. 14.47 × 1.2 = 17.4 GB. Text only, added on 30 October 2026" }, { "name": "drex-max-v1-Q8_0", "url": "https://huggingface.co/nace-ai/drex-max-v1-Q8_0", "who": "publisher", "precision": "q8_0", "sizeGB": 27.3, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp (Nace fork, branch drex-max-v1)", "Ollama (Nace fork, branch drex-max-v1)", "Kapitsa" ], "note": "drex-max-v1-Q8_0.gguf, 27,250,125,216 bytes, with causal attention off and the readout as pointer tensors. 27.25 × 1.2 = 32.7 GB, and the card says it needs 48 GB. Text only" }, { "name": "drex-max-v1", "url": "https://huggingface.co/nace-ai/drex-max-v1", "who": "publisher", "precision": "bf16", "sizeGB": 52.2, "memoryGB": 64, "format": "safetensors", "runsOn": [ "Transformers 5.17.0 with Nace's loader (CUDA)" ], "note": "11 shards (52.17 GB), readout.safetensors (2.6 MB), tokenizer, configs, images and loader code, 52.19 GB summed from the Hub's file list. 52.19 × 1.2 = 62.6 GB. The only build that reads images. Loading the backbone in 8-bit on CUDA takes about 26 GB, per the card" } ], "run": "python -m drex_max --model weights/drex-max-v1 --request examples/request.json", "runBuild": "drex-max-v1", "checked": "2026-10-30", "minMemoryGB": 24 }, "family": { "lab": "Nace.AI", "family": "Drex", "url": "https://www.anchorterminal.com/open-weights/#nace", "others": [ { "name": "Drex v1.5", "url": "https://huggingface.co/nace-ai/drex-v1.5", "params": "9B (8,953,803,264 by the Hub's safetensors count)", "listing": "https://www.anchorterminal.com/tools/nace-drex" }, { "name": "Drex DLM", "url": "https://huggingface.co/nace-ai/drex-dlm", "params": "8B (8,190,735,360 by the Hub's safetensors count)" }, { "name": "Drex v2.0", "url": "https://huggingface.co/nace-ai/drex-v2.0", "params": "35B-A3B (35,107,181,936 by the Hub's safetensors count)" } ], "olderCount": 1 }, "area": "models", "provenance": { "legalEntity": "Nace AI, Inc.", "domain": "nace.ai", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "valid", "checked": "2026-10-30", "notes": [ "The legal entity is from the nace-drex listing's check of Nace's terms on 10 October 2026.", "The nace-ai Hugging Face organisation is unverified, with 19 members, among them the two accounts that committed to this repository. nace.ai/drex links to huggingface.co/nace-ai and to github.com/nace-ai, whose drex-decision-models repository links these weights.", "Nothing is hosted under this listing, so the licence stands in for terms of service and no privacy policy is scored.", "security.txt at www.nace.ai is valid until 30 October 2027 and points to trust.nace.ai for contact and policy." ], "score": 58 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/drex-max-v1.json" }, "answer": "Drex Max v1 scores 55.5 (C) on agent readiness against elephant-1's 47.6 (D), and leads in 3 of 4 scored categories. elephant-1 leads on risk.", "b": { "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": 1243, "categoryRank": 23, "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" } }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Nace AI, Inc. (Nace.AI)", "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": "Nace.AI Open RAIL-M License (modified), version 1.0, October 2026, in the repository's LICENSE. Not an open-source licence. Use is free only for individuals and organisations under $1 million in revenue in the prior 12 months and under $1 million in total funding (personal and research use excepted), never by anyone with a product that competes with Nace's, decision models included, and under use restrictions that bind the model, its derivatives and its outputs. Built on Qwen/Qwen3.8-27B (Apache-2.0), whose licence text isn't carried in the repository", "b": "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", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-30", "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": "Drex Max v1 scores 55.5 (C) on agent readiness against elephant-1's 47.6 (D), and leads in 3 of 4 scored categories. elephant-1 leads on risk.", "question": "Which is better for AI agents, Drex Max v1 or elephant-1?" } ], "goodFor": [ { "aheadOn": [ "Reach, 65 against 55", "Agent interface, 80 against 45" ], "also": null, "goodFor": "Agents that want self-hosted routing, triage or screen decisions with probabilities, on a CUDA GPU with 48 GB or more for images or a 24 GB Mac for text, where the Open RAIL-M terms fit.", "slug": "drex-max-v1", "watchFor": "The licence bars commercial use above $1 million in revenue or funding and by anyone with a competing product, and restricts the outputs too" }, { "aheadOn": [ "Risk, 50 against 45" ], "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" } ], "job": { "capability": "inference.decision", "name": "Decision models" }, "others": [ { "json": "https://www.anchorterminal.com/compare/drex-max-v1-vs-jev-27b-vl.json", "title": "Drex Max v1 vs JEV-27B-VL", "url": "https://www.anchorterminal.com/compare/drex-max-v1-vs-jev-27b-vl" }, { "json": "https://www.anchorterminal.com/compare/drex-max-v1-vs-joey-1.json", "title": "Drex Max v1 vs joey-1", "url": "https://www.anchorterminal.com/compare/drex-max-v1-vs-joey-1" }, { "json": "https://www.anchorterminal.com/compare/drex-max-v1-vs-mammoth-1.json", "title": "Drex Max v1 vs mammoth-1", "url": "https://www.anchorterminal.com/compare/drex-max-v1-vs-mammoth-1" }, { "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-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" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 10, "drex-max-v1": 65, "edge": "drex-max-v1", "elephant-1": 55, "key": "reach", "name": "Reach", "weight": 25 }, { "by": 5, "drex-max-v1": 45, "edge": "elephant-1", "elephant-1": 50, "key": "risk", "name": "Risk", "weight": 20 }, { "by": 35, "drex-max-v1": 80, "edge": "drex-max-v1", "elephant-1": 45, "key": "interface", "name": "Agent interface", "weight": 10 }, { "by": 1, "drex-max-v1": 41, "edge": "drex-max-v1", "elephant-1": 40, "key": "upkeep", "name": "Documentation and upkeep", "weight": 10 } ], "summary": "Drex Max v1 scores 55.5 (C) on agent readiness against elephant-1's 47.6 (D), and leads in 3 of 4 scored categories. elephant-1 leads on risk. Both do decision models.", "tutorials": [], "verdicts": { "drex-max-v1": "A 27B cousin for typed decisions over text and screenshots, several questions per request with a probability for each option. Images need an NVIDIA GPU and Nace's loader; on Apple silicon only the text backbone runs, from 24 GB. The licence isn't open source, with free commercial use only under $1 million in revenue and funding.", "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." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/drex-max-v1-vs-elephant-1", "json": "https://www.anchorterminal.com/compare/drex-max-v1-vs-elephant-1.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/drex-max-v1-vs-elephant-1.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/drex-max-v1-vs-elephant-1.min.md" }, "markdown": "Drex Max v1 scores 55.5 (C) on agent readiness against elephant-1's 47.6 (D), and leads in 3 of 4 scored categories. elephant-1 leads on risk. Both do decision models.\n\n- Drex Max v1: grade C, 55.5/100, rank #90 of 120 open-weight models. Markdown https://www.anchorterminal.com/tools/drex-max-v1.md · JSON https://www.anchorterminal.com/api/v1/tools/drex-max-v1.json\n- elephant-1: grade D, 47.6/100, rank #116 of 120 open-weight models. Markdown https://www.anchorterminal.com/tools/elephant-1.md · JSON https://www.anchorterminal.com/api/v1/tools/elephant-1.json\n- Best decision models for AI agents: https://www.anchorterminal.com/best/decision-models/index.md\n- All 200 decisions comparisons: https://www.anchorterminal.com/compare/decision-models/index.md\n\n## Which one, for what\n\n### Drex Max v1 (C)\n\nGood for: Agents that want self-hosted routing, triage or screen decisions with probabilities, on a CUDA GPU with 48 GB or more for images or a 24 GB Mac for text, where the Open RAIL-M terms fit.\n\nAhead on:\n- Reach, 65 against 55\n- Agent interface, 80 against 45\n\nWatch for: The licence bars commercial use above $1 million in revenue or funding and by anyone with a competing product, and restricts the outputs too\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- Risk, 50 against 45\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 | Drex Max v1 | elephant-1 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 65 | 55 | Drex Max v1 +10 |\n| Risk | 20% (20 this run) | 45 | 50 | elephant-1 +5 |\n| Agent interface | 10% (10 this run) | 80 | 45 | Drex Max v1 +35 |\n| Documentation and upkeep | 10% (10 this run) | 41 | 40 | Drex Max v1 +1 |\n| Negative events | ≤15 | -2 | -2 | |\n| **Total** | | **55.5 · C** | **47.6 · D** | |\n\n## Facts side by side\n\n| Fact | Drex Max v1 | elephant-1 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Nace AI, Inc. (Nace.AI) | 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 | Nace.AI Open RAIL-M License (modified), version 1.0, October 2026, in the repository's LICENSE. Not an open-source licence. Use is free only for individuals and organisations under $1 million in revenue in the prior 12 months and under $1 million in total funding (personal and research use excepted), never by anyone with a product that competes with Nace's, decision models included, and under use restrictions that bind the model, its derivatives and its outputs. Built on Qwen/Qwen3.8-27B (Apache-2.0), whose licence text isn't carried in the repository | 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 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-30 | 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**Drex Max v1.** A 27B cousin for typed decisions over text and screenshots, several questions per request with a probability for each option. Images need an NVIDIA GPU and Nace's loader; on Apple silicon only the text backbone runs, from 24 GB. The licence isn't open source, with free commercial use only under $1 million in revenue and funding.\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## Before you call either\n\n### Drex Max v1\n\n1. Clone github.com/nace-ai/drex-decision-models, download nace-ai/drex-max-v1 and run `python -m drex_max --model weights/drex-max-v1 --request examples/request.json` on an NVIDIA GPU\n2. Plan for about 49 GB of GPU memory in BF16, or about 26 GB with --8bit on CUDA, before any long context\n3. Don't load it with stock Transformers, vLLM or llama.cpp. Its full-attention layers must run non-causally with readout.safetensors applied, which only Nace's loader and forks do\n4. On a Mac with 24 GB or more, run `kapitsa-systemone --model ./drex-max-v1-affine-q4 --port 8000 --context 2048` for text-only decisions at POST /v1/systemone\n5. Raise the 16,384-token default with DREX_MAX_CONTEXT=N or --max-length N, up to 262,144, and expect more memory\n6. Check the licence first. Above $1 million in revenue or funding, or with a competing product, you need Nace's commercial licence\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## Questions\n\n### Which is better for AI agents, Drex Max v1 or elephant-1?\n\nDrex Max v1 scores 55.5 (C) on agent readiness against elephant-1's 47.6 (D), and leads in 3 of 4 scored categories. elephant-1 leads on risk.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/drex-max-v1-vs-elephant-1.json, and with the fewest tokens: https://www.anchorterminal.com/compare/drex-max-v1-vs-elephant-1.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"drex-max-v1\", \"b\": \"elephant-1\"}`. From a terminal: `anchor compare drex-max-v1 elephant-1`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/drex-max-v1.json and https://www.anchorterminal.com/api/v1/tools/elephant-1.json\n\n## Other comparisons with Drex Max v1 or elephant-1\n\n- [Drex Max v1 vs JEV-27B-VL](https://www.anchorterminal.com/compare/drex-max-v1-vs-jev-27b-vl.md)\n- [Drex Max v1 vs joey-1](https://www.anchorterminal.com/compare/drex-max-v1-vs-joey-1.md)\n- [Drex Max v1 vs mammoth-1](https://www.anchorterminal.com/compare/drex-max-v1-vs-mammoth-1.md)\n- [elephant-1 vs JEV-27B-VL](https://www.anchorterminal.com/compare/elephant-1-vs-jev-27b-vl.md)\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", "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": "Drex Max v1 vs elephant-1", "url": "" } ], "description": "Drex Max v1 scores 55.5 (C) to elephant-1's 47.6 (D) for decision models. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Drex Max v1 C 55.5", "elephant-1 D 47.6", "scores" ], "h1": "Drex Max v1 vs elephant-1", "image": "https://www.anchorterminal.com/assets/og/compare-drex-max-v1-vs-elephant-1.png", "path": "/compare/drex-max-v1-vs-elephant-1", "published": "2026-10-01", "section": "tools", "title": "Drex Max v1 vs elephant-1 for AI agents in 2026: scores and prices", "toc": null, "updated": "2026-10-30", "url": "https://www.anchorterminal.com/compare/drex-max-v1-vs-elephant-1" }, "tokens": { "markdown": 2100, "slim": 630 }, "version": 1 }