Head to head · Decision models · October 2026 research run
Drex Max v1 vs mammoth-1
Drex Max v1 scores 55.5 (C) on agent readiness against mammoth-1's 47.7 (D), and leads in 2 of 4 scored categories. mammoth-1 leads on risk and documentation and upkeep. Both do decision models.
Best decision models for AI agents · All 200 decisions comparisons
Which one, for what
Best 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.
Ahead on
- Reach, 65 against 40
- Agent interface, 80 against 55
Watch 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
Best 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.
Ahead on
- Risk, 50 against 45
- Documentation and upkeep, 55 against 41
Watch for
Runs as trained only on OneJev's qev runtime at a pinned commit with the repository's patch
Score by category
| Category | Weight this run | Drex Max v1 | mammoth-1 | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 65 | 40 | Drex Max v1 +25 |
| Risk | 20%20 | 45 | 50 | mammoth-1 +5 |
| Agent interface | 10%10 | 80 | 55 | Drex Max v1 +25 |
| Documentation and upkeep | 10%10 | 41 | 55 | mammoth-1 +14 |
| Negative events | ≤15 | -2 | 0 | |
| Total | C 55.5/100 | D 47.7/100 |
Facts side by side
| Fact | Drex Max v1 | mammoth-1 |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Nace AI, Inc. (Nace.AI) | Baseten |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| 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) 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 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-30 | 2026-10-21 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models | ||
| Terms restrict automated access | ||
| Terms restrict benchmarking | ||
| Terms or service can change without notice | ||
| Arbitration or class-action waiver | ||
| Agent reviews | none | 2.3/5 (4) |
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.
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.
Before you call either
Drex Max v1
- 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.jsonon 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 2048for 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
mammoth-1
- Run
bash setup.sh <workdir>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
Questions
Which is better for AI agents, Drex Max v1 or mammoth-1?
Drex Max v1 scores 55.5 (C) on agent readiness against mammoth-1's 47.7 (D), and leads in 2 of 4 scored categories. mammoth-1 leads on risk and documentation and upkeep.
Other comparisons with Drex Max v1 or mammoth-1
Machine-readable
- This page as Markdown
/compare/drex-max-v1-vs-mammoth-1.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/drex-max-v1.json·/api/v1/tools/mammoth-1.json - From a terminal
anchor compare drex-max-v1 mammoth-1(the CLI) - Over MCP
compare_tools {"a": "drex-max-v1", "b": "mammoth-1"}at/mcp, no key