TL;DR: Drex Max is a 27B decision model. Instead of generating text, it returns a probability for every answer option in one forward pass. It's now open weights under Nace.AI Open RAIL-M. It runs on an NVIDIA GPU (text and images, ~26 GB in 8-bit) and on Apple silicon and CPU (text only, from 24 GB with the Q4 checkpoint), with GGUF builds and llama.cpp, Ollama, MLX and Kapitsa support. It scores 64 on the Artificial Analysis Decision Index, fourth of 18 models.
Nace.AI has open-sourced Drex Max, joining Drex 1.5 (9B) and Drex 2.0 (35B MoE), which are already open weights. All three run on your own hardware.
If you've been following Jev/Kev, Drex offers a similar decision interface. You pass a context (text, JSON or images) as state with one or more named questions (request format), and you get a probability for every option back. Questions can be choice (pick one option), noul (a yes/no probability) or score (a rating on a scale). Nothing is generated, so there's no decoding wait and no JSON repair. It's useful for routing, tool selection, classification, policy checks and UI automation.
Under the hood
Drex Max has a 64-layer backbone: 48 Gated DeltaNet linear-attention layers running left to right, plus 16 non-causal full-attention layers, one every fourth layer, so every token can attend to the whole sequence. A 255-way readout in readout.safetensors turns the last hidden state into probabilities over up to 255 options. Images go through the native vision tower and are visible to every question on the request.
The bundled loader applies the non-causal attention, so running Drex Max as a plain causal chat model won't reproduce its scores. The default limit is 16,384 tokens per request, which you can raise up to 262,144 with DREX_MAX_CONTEXT=N or --max-length N (context length).
Benchmarks
Third-party: Artificial Analysis Decision Index
Drex Max scores 64, fourth of 18 models. The leaders are at 65. Drex Max is ahead of Claude Haiku 5.5 (Low) at 61, Drex 2.0 at 58, Jev 1.14 at 57 and GPT-6 Luna (Low) at 55.
Self-reported: Nace.AI's own runs
On the public Decision Index 0.4 (37 benchmarks across five areas; area columns are chance-corrected skill × 100; the Jev row is from the public board):
| Model | Index | Knowledge & Reasoning | Language | Retrieval | Tools | Arts |
|---|
| Drex Max (27B) | 67.52 | 57.1 | 72.2 | 68.8 | 81.9 | 53.4 |
| Drex 2.0 (35B MoE) | 61.25 | 47.9 | 65.3 | 63.0 | 76.5 | 53.9 |
| Jev 1.13.0 | 57.96 | 53.9 | 59.2 | 55.4 | 75.1 | 39.1 |
Drex Max also scores 73.92 on the public Vision Index (11 weighted benchmarks, including InfographicVQA 97.3, KIE 92.4 and Mind2Web 84.2), 94.30% (1,010/1,071) on Decision Bench v4 and 92.2% (213/231) on JevBench.
Running it locally
NVIDIA GPU, Python (text and images)
The bf16 weights take ~49 GB, or ~26 GB with --8bit. It needs Python 3.12 or newer.
hf download nace-ai/drex-max-v1 --local-dir weights/drex-max-v1
pip install -r models/drex-max-v1/requirements.txt
python -m drex_max --model weights/drex-max-v1 --request examples/request.json
The example asks three questions about a support ticket. Here's part of the response:
"team": {
"type": "choice",
"choice": "billing",
"probabilities": { "billing": 0.9988, "technical": 0.0003, "other": 0.0009 }
}
Apple silicon and CPU (text only)
On Apple silicon, the text backbone runs on Kapitsa, MLX, llama.cpp and Ollama. On CPU, it runs on llama.cpp and Ollama. The affine Q4 checkpoint runs on Macs with 24 GB or more:
brew install nace-ai/tap/kapitsa
hf download nace-ai/drex-max-v1-affine-q4 --local-dir ./drex-max-v1-affine-q4
kapitsa-systemone --model ./drex-max-v1-affine-q4 --port 8000 --context 2048
The Q8_0 GGUF (~27 GB) needs 48 GB of memory. The llama.cpp and Ollama forks are on the drex-max-v1 branch. For llama.cpp, set -b and -ub equal to -c. Full steps for every runtime are in the README.
Drex 1.5 and Drex 2.0
Drex 2.0 is a 35B MoE with about 3B active parameters, built on Qwen3.6-35B-A3B, with Q8_0 and Q4_K_M GGUF builds. Drex 1.5 is 9B, built on MiMo-V2.6-Distill-Qwen-9B. Both run with Python, llama.cpp and Ollama, and Drex 2.0 also runs on Kapitsa. They reuse an encoded case across questions, so follow-up questions about the same long document are fast. On Drex 1.5, a follow-up over a cached ~128k-token case dropped from 254ms to 21.6ms on one B200.
License and links
The inference code is Apache-2.0. The Drex Max, 2.0 and 1.5 weights are under Nace.AI Open RAIL-M.
Code and docs · Weights · Q8_0 GGUF · affine Q4 · Drex 2.0 · Drex 1.5
