# Drex Max v1 vs mammoth-1

> Drex Max v1 scores 55.5 (C) to mammoth-1's 47.7 (D) for decision models. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/drex-max-v1-vs-mammoth-1
- Markdown: https://www.anchorterminal.com/compare/drex-max-v1-vs-mammoth-1.md (~2,100 tokens)
- Slim: https://www.anchorterminal.com/compare/drex-max-v1-vs-mammoth-1.min.md (~680 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/drex-max-v1-vs-mammoth-1.json (this page as data, same URL with Accept: application/json)
- Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-10-30

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.

- 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
- mammoth-1: grade D, 47.7/100, rank #115 of 120 open-weight models. Markdown https://www.anchorterminal.com/tools/mammoth-1.md · JSON https://www.anchorterminal.com/api/v1/tools/mammoth-1.json
- Best decision models for AI agents: https://www.anchorterminal.com/best/decision-models/index.md
- All 200 decisions comparisons: https://www.anchorterminal.com/compare/decision-models/index.md

## Which one, for what

### Drex Max v1 (C)

Good 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

### mammoth-1 (D)

Good 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 | Drex Max v1 | mammoth-1 | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 65 | 40 | Drex Max v1 +25 |
| Risk | 20% (20 this run) | 45 | 50 | mammoth-1 +5 |
| Agent interface | 10% (10 this run) | 80 | 55 | Drex Max v1 +25 |
| Documentation and upkeep | 10% (10 this run) | 41 | 55 | mammoth-1 +14 |
| Negative events | ≤15 | -2 | 0 | |
| **Total** | | **55.5 · C** | **47.7 · D** | |

## 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

1. 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
2. Plan for about 49 GB of GPU memory in BF16, or about 26 GB with --8bit on CUDA, before any long context
3. 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
4. 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
5. Raise the 16,384-token default with DREX_MAX_CONTEXT=N or --max-length N, up to 262,144, and expect more memory
6. Check the licence first. Above $1 million in revenue or funding, or with a competing product, you need Nace's commercial licence

### mammoth-1

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
2. 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
3. Send one state and many questions per request, each choice, yes or no (noul) or score, and read a probability per option
4. Keep requests within the trained length, the report trained at up to 16,384 tokens and engine.py refuses requests over the trained limits
5. The index's scores are for revision b9f2f2cacef2ec178ed25c9ef43e987d3422a639. Pin that revision to get the scored weights, or main for the later ones
6. 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.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/drex-max-v1-vs-mammoth-1.json, and with the fewest tokens: https://www.anchorterminal.com/compare/drex-max-v1-vs-mammoth-1.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "drex-max-v1", "b": "mammoth-1"}`. From a terminal: `anchor compare drex-max-v1 mammoth-1`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/drex-max-v1.json and https://www.anchorterminal.com/api/v1/tools/mammoth-1.json

## Other comparisons with Drex Max v1 or mammoth-1

- [Drex Max v1 vs elephant-1](https://www.anchorterminal.com/compare/drex-max-v1-vs-elephant-1.md)
- [Drex Max v1 vs JEV-27B-VL](https://www.anchorterminal.com/compare/drex-max-v1-vs-jev-27b-vl.md)
- [Drex Max v1 vs joey-1](https://www.anchorterminal.com/compare/drex-max-v1-vs-joey-1.md)
- [elephant-1 vs mammoth-1](https://www.anchorterminal.com/compare/elephant-1-vs-mammoth-1.md)
- [JEV-27B-VL vs mammoth-1](https://www.anchorterminal.com/compare/jev-27b-vl-vs-mammoth-1.md)
- [joey-1 vs mammoth-1](https://www.anchorterminal.com/compare/joey-1-vs-mammoth-1.md)

---

Query this catalogue directly over MCP: https://www.anchorterminal.com/mcp (Streamable HTTP, no key). How: https://www.anchorterminal.com/agents/#mcp