# Drex Max v1 vs joey-1

> joey-1 scores 57.1 (C) to Drex Max v1's 55.5 (C) for decision models. Prices, MCP, x402, uptime and agent notes side by side.

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- Updated: 2026-10-30

joey-1 scores 57.1 (C) on agent readiness against Drex Max v1's 55.5 (C), and leads in 2 of 4 scored categories. Drex Max v1 leads on reach and agent interface. 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
- joey-1: grade C, 57.1/100, rank #80 of 120 open-weight models. Markdown https://www.anchorterminal.com/tools/joey-1.md · JSON https://www.anchorterminal.com/api/v1/tools/joey-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 60
- Agent interface, 80 against 70

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

### joey-1 (C)

Good for: Agents that want self-hosted, low-cost ticket routing or guardrail checks with probabilities to threshold, on a GPU with 8 GB, and can accept a patched runtime.

Ahead on:
- Risk, 55 against 45
- Documentation and upkeep, 54 against 41

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 | joey-1 | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 65 | 60 | Drex Max v1 +5 |
| Risk | 20% (20 this run) | 45 | 55 | joey-1 +10 |
| Agent interface | 10% (10 this run) | 80 | 70 | Drex Max v1 +10 |
| Documentation and upkeep | 10% (10 this run) | 41 | 54 | joey-1 +13 |
| Negative events | ≤15 | -2 | -2 | |
| **Total** | | **55.5 · C** | **57.1 · C** | |

## Facts side by side

| Fact | Drex Max v1 | joey-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 in the card's metadata (license: mit) since the upload of 27 October 2026, with no LICENSE file. The base Qwen/Qwen3.5-2B is Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-30 | 2026-10-29 |
| 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 |  |  |

## 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.

**joey-1.** A very small cousin for routing, triage and guardrail checks, answering several typed questions about one state, with text or images, as probabilities from one prefill. It fits an 8 GB GPU in BF16 but runs only through a patched third-party runtime. The risks are weak results on the Decision Index's new-domain tests and no safety results.

## 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

### joey-1

1. Start on a CUDA GPU in pytorch/pytorch:2.8.0-cuda12.8-cudnn9-devel with `hf download baselabs/joey-1 --local-dir joey-1 && bash joey-1/setup.sh`, which clones OneJev at 81ce62f, applies serve.patch and installs the Decision Index kit
2. Then call `DecisionEngine("baselabs/joey-1", device="cuda:0").decide(SystemOneRequest(state=..., questions=...))` from qev, as the card shows
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. Expect weaker answers on domains unlike its training mix, and threshold on the returned probability before acting
6. Without hardware, Baseten's /v1/systemone serves it as decision-models/baseten-joey-2b-latest with a Baseten key

## Questions

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

joey-1 scores 57.1 (C) on agent readiness against Drex Max v1's 55.5 (C), and leads in 2 of 4 scored categories. Drex Max v1 leads on reach and agent interface.


## For agents

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

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

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