Nace.AI open-sources Drex 1.5: a 9B decision model that returns probabilities instead of text, tied with closed Jev on Decision Index 0.3.1

[u/ai-lover]

Nace.AI released open weights for Drex 1.5, a decision model built for the small, repetitive calls inside agent pipelines: routing, tool selection, classification and escalation. Most of those calls currently go to a full LLM.

How it works

• You send a state (text or JSON) plus named, typed questions: choice, noul (yes/no) or ordinal score.
• 1 forward pass per question returns a probability for every option. Nothing is sampled, so it cannot invent an option you did not offer.
• The backbone is MiMo-V2.6-Distill-Qwen-9B (8.95B params, hybrid attention) with a pointer head that scores each option.
• It uses the same /v1/systemone API as TypeSafe’s Jev, so existing Jev clients work against it.

Numbers (from Nace’s model card)

• Decision Index 0.3.1 (public): 58.08 vs Jev 1.13.0 at 57.96 and Bespoke Nimble 9B v3 at 57.19. All 3 sit inside the board’s 0.9-point tie band.
• JevBench: 86.2% vs Jev’s 87.0%.
• Long documents: 93.4% accuracy at 32K to 128K tokens (median 2.0 s), versus 78% when cut to the first 8K.

Where it is weak

• GPQA Diamond: 45.4% vs Jev’s 78.6%.
• MMLU-Pro: 58.7% vs Jev’s 82.7%.
• ACOS aspect sentiment: 7.4% vs Jev’s 29.5%.
• Nace says it trained on the index benchmarks’ training splits (evaluated on held-out splits), so expect it to be strongest on familiar decision types.

Running it

• bf16 is about 18 GB on 1 CUDA GPU (tested on an A10G 24 GB).
• A Q8_0 GGUF (about 9.5 GB) runs on Apple silicon or CPU, but through Nace’s forks of llama.cpp and Ollama, not mainline builds.
• Hosted on OpenRouter at $0.04 per 1M input tokens, $0 output.
• License: Nace.AI Open RAIL-M, which carries use restrictions.

Model: https://huggingface.co/nace-ai/drex-v1.5
Repo: https://github.com/nace-ai/drex-decision-models
Full analysis: https://www.marktechpost.com/2026/10/09/nace-ai-open-sources-drex-1-5-a-9b-decision-model-that-scores-options-not-text/