# Drex Max v1 vs elephant-1

> 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.

- Canonical: https://www.anchorterminal.com/compare/drex-max-v1-vs-elephant-1
- Markdown: https://www.anchorterminal.com/compare/drex-max-v1-vs-elephant-1.md (~2,100 tokens)
- Slim: https://www.anchorterminal.com/compare/drex-max-v1-vs-elephant-1.min.md (~630 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/drex-max-v1-vs-elephant-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 elephant-1's 47.6 (D), and leads in 3 of 4 scored categories. elephant-1 leads on risk. 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
- 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
- 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 55
- Agent interface, 80 against 45

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

### elephant-1 (D)

Good 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.

Ahead on:
- Risk, 50 against 45

Watch for: Serving it needs OneJev at a pinned commit plus the repository's serve.patch, with no released runtime support


## Score by category

| Category | Weight | Drex Max v1 | elephant-1 | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 65 | 55 | Drex Max v1 +10 |
| Risk | 20% (20 this run) | 45 | 50 | elephant-1 +5 |
| Agent interface | 10% (10 this run) | 80 | 45 | Drex Max v1 +35 |
| Documentation and upkeep | 10% (10 this run) | 41 | 40 | Drex Max v1 +1 |
| Negative events | ≤15 | -2 | -2 | |
| **Total** | | **55.5 · C** | **47.6 · D** | |

## Facts side by side

| Fact | Drex Max v1 | elephant-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) 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 |
| 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.

**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.

## 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

### elephant-1

1. Start on a CUDA GPU in pytorch/pytorch:2.8.0-cuda12.8-cudnn9-devel with `git clone https://huggingface.co/baselabs/elephant-1 && cd elephant-1 && bash setup.sh /tmp/w`, which clones OneJev at 81ce62f, applies serve.patch and installs the Decision Index kit
2. 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
3. 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
4. Keep each request under 16,384 tokens, the trained length, or the engine refuses it
5. Group the questions about one state in one request, since the state is read once and shared
6. 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

## Questions

### Which is better for AI agents, Drex Max v1 or elephant-1?

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.


## For agents

- 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
- 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`
- 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

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

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