# Drex Max v1 vs JEV-27B-VL

> JEV-27B-VL scores 59.5 (C) to Drex Max v1's 55.5 (C) for decision models. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/drex-max-v1-vs-jev-27b-vl
- Markdown: https://www.anchorterminal.com/compare/drex-max-v1-vs-jev-27b-vl.md (~2,100 tokens)
- Slim: https://www.anchorterminal.com/compare/drex-max-v1-vs-jev-27b-vl.min.md (~630 tokens, same facts, less prose, for token-sensitive contexts)
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- 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
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- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-10-30

JEV-27B-VL scores 59.5 (C) on agent readiness against Drex Max v1's 55.5 (C), and leads in 1 of 4 scored categories. Drex Max v1 leads on reach. 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
- JEV-27B-VL: grade C, 59.5/100, rank #67 of 120 open-weight models. Markdown https://www.anchorterminal.com/tools/jev-27b-vl.md · JSON https://www.anchorterminal.com/api/v1/tools/jev-27b-vl.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 50

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

### JEV-27B-VL (C)

Good for: Agents that want self-hosted routing, triage, ranking or judging over text and screenshots with probabilities to threshold, on one 80 GB GPU or a 96 GB machine.

Ahead on:
- Documentation and upkeep, 92 against 41

Watch for: Trained on labels distilled from TypeSafe's hosted Jev, whose customer agreement forbids distillation


## Score by category

| Category | Weight | Drex Max v1 | JEV-27B-VL | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 65 | 50 | Drex Max v1 +15 |
| Risk | 20% (20 this run) | 45 | 45 | even |
| Agent interface | 10% (10 this run) | 80 | 80 | even |
| Documentation and upkeep | 10% (10 this run) | 41 | 92 | JEV-27B-VL +51 |
| Negative events | ≤15 | -2 | 0 | |
| **Total** | | **55.5 · C** | **59.5 · C** | |

## Facts side by side

| Fact | Drex Max v1 | JEV-27B-VL |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Nace AI, Inc. (Nace.AI) | AutoTrust AI Lab |
| 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 | Apache-2.0 for AutoTrust's System 1 adapter and decision head. The base Qwen3.8-27B weights in the repository are Alibaba's, unchanged, also Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-30 | 2026-10-02 |
| 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 | 2.5/5 (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.

**JEV-27B-VL.** A cousin for typed decisions with probabilities over text and images, answering in one pass with optional thinking. It needs about 67 GB in BF16 on vLLM, with no AutoTrust 4-bit build or hosted API. The main risks are labels distilled from TypeSafe's Jev against its customer terms and serving through a repository script on a vLLM development build.

## 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

### JEV-27B-VL

1. Quickest start is `hf download autotrust/JEV-27B-VL` then `bash JEV-27B-VL/serve.sh`, which serves POST /v1/decide on port 8000, but it runs the repository's serve_decide.py on a September 2026 vLLM development build
2. On a released vLLM use plain `vllm serve` with `--enable-lora --lora-modules jev-decision=JEV-27B-VL/adapter_vllm` and do the card's client-side arithmetic with decision_head.json and calibration.json
3. Keep `--max-num-seqs 8`, the card says larger batches return wrong System 1 probabilities on vLLM's multimodal LoRA path
4. When calling /v1/completions or /v1/chat/completions yourself pass top_k 0 and top_p 1.0, or generation_config.json's top_k 20 and top_p 0.95 truncate the probabilities
5. Choices beyond 16 options use labels the head never saw in training, and the response's adaptation field says wide-labels
6. Downscale images to about 448 px to keep vision tokens and latency down
7. Treat it as AutoTrust's model, not TypeSafe's, and its request shape differs from TypeSafe's /v1/systemone

## Questions

### Which is better for AI agents, Drex Max v1 or JEV-27B-VL?

JEV-27B-VL scores 59.5 (C) on agent readiness against Drex Max v1's 55.5 (C), and leads in 1 of 4 scored categories. Drex Max v1 leads on reach.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/drex-max-v1-vs-jev-27b-vl.json, and with the fewest tokens: https://www.anchorterminal.com/compare/drex-max-v1-vs-jev-27b-vl.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "drex-max-v1", "b": "jev-27b-vl"}`. From a terminal: `anchor compare drex-max-v1 jev-27b-vl`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/drex-max-v1.json and https://www.anchorterminal.com/api/v1/tools/jev-27b-vl.json

## Other comparisons with Drex Max v1 or JEV-27B-VL

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

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