---
title: "Introducing Drex Max, a multimodal decision model for computer use"
description: "Drex Max is a decision model that sees the screen. It reads text, images and video, ranks first among decision models on the public Decision Index 0.3.2, and is generally available today."
canonical: "https://nace.ai/blog/introducing-drex-max"
updated: 2026-10-19
---

# Introducing Drex Max, a multimodal decision model for computer use

> Drex Max is a decision model that sees the screen. It reads text, images and video, ranks first among decision models on the public Decision Index 0.3.2, and is generally available today.

Blog · Oct 19, 2026 · Nace.AI

**Drex Max, a decision model that sees the screen.** Generally available. First among decision models on the public Decision Index 0.3.2, and it reads text, images and video.

- [Explore](https://nace.ai/drex)
- [Get started](https://console.nace.ai?utm_campaign=drex-max-launch&utm_content=announcement&utm_medium=website&utm_source=nace.ai)

[Recording: Ticket purchase](https://media.nace.ai/drex/drex-tickets.mp4)

*Drex Max finding the cheapest nonstop New York to San Francisco round trip on Google Flights. Every click is one decision.*

A computer-use agent spends most of its time on small decisions. Which button to click. Which field to fill. Which search result to open. Whether the page has finished loading. A single task can take thirty of them, and today each one usually means sending a screenshot to a large frontier model and waiting seconds while it writes an answer.

Today we are releasing **Drex Max**. It is our most capable decision model, and the first one that sees. You give it a screenshot, a screencast, a scanned document, a chart or a photo, along with a set of typed questions, and one forward pass returns a probability for every option you supplied. Drex Max is built to sit inside that loop and make each of those small decisions in milliseconds, at a fraction of what a frontier model costs.

## Benchmarks

### Decision models benchmark

| Benchmark | Drex Max | Drex v2.0 | Drex v1.5 | Jev 1.14 | GPT-6 Luna Decisions | GPT-6 Sol | Claude Opus 5.5 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| **Decision benchmarks** |  |  |  |  |  |  |  |
| Decision Index (public) | **67.6** | 61.3 | 58.1 | 60.7 | 47.8 | – | – |
| JevBench | **92.2** | 85.7 | 84.8 | 86.7 | 91.0 | 99.1 | 100.0 |
| Decision Bench | **94.3** | 88.3 | 86.8 | 92.3 | 90.5 | 94.7 | 95.6 |
| DecideBench (item) | **98.5** | 96.5 | 90.0 | 98.0 | 97.2 | 99.2 | 98.8 |
| DecideBench (pair) | **97.0** | 93.5 | 80.5 | 96.0 | 94.5 | 98.5 | 98.5 |
| Game Bench (win %) | **64.0** | 60.3 | 58.7 | 50.0 | 57.5 | 65.0 | 63.0 |
| **Knowledge** |  |  |  |  |  |  |  |
| MMLU-Pro | 72.5 | 72.8 | 58.6 | **82.1** | 58.1 | 93.0 | 94.0 |
| HLE | 19.0 | 12.8 | 11.4 | **19.4** | 12.3 | 39.0 | 56.0 |
| CRUXEval | **85.8** | 84.6 | 76.7 | 74.7 | 66.4 | 100.0 | 95.0 |
| **Reasoning** |  |  |  |  |  |  |  |
| ARC-Challenge | 97.4 | 96.8 | 95.9 | **97.6** | 96.9 | 99.0 | 98.0 |
| ARC-Easy | **99.4** | 98.6 | 97.9 | 99.3 | 99.0 | 100.0 | 100.0 |
| HellaSwag | **97.9** | 95.9 | 92.1 | 94.3 | 91.4 | 93.0 | 96.0 |
| **Agentic benchmarks** |  |  |  |  |  |  |  |
| OSWorld-Verified (automation) | **0.780** | 0.698 | 0.676 | 0.734 | 0.747 | 0.756 | 0.790 |
| OSWorld-V2 (automation) | **0.737** | 0.636 | 0.640 | 0.687 | 0.659 | 0.744 | 0.761 |
| BFCL (tool calling) | **98.2** | 96.6 | 94.9 | 96.4 | 94.2 | 94.7 | 97.3 |
| ToolRet (tool retrieval) | **70.6** | 67.3 | 70.2 | 65.0 | 60.4 | 72.7 | 74.8 |
| When2Call | 85.2 | 86.5 | **89.7** | 81.7 | 70.2 | 78.3 | 86.7 |
| RAGTruth | **84.8** | 82.5 | 82.7 | 75.1 | 77.0 | 72.5 | 69.8 |
| **Efficiency** |  |  |  |  |  |  |  |
| Median latency | 183 ms | 151 ms | **145 ms** | 196 ms | 326 ms | ~2.0 s | ~3.5 s |
| Cost (per 1k decisions) | $0.06 | $0.06 | **$0.04** | $0.042 | $0.10 | $3.33 | $10.17 |

### Decision Index Vision (public)

| Benchmark                  | Drex Max | GPT-6 Luna Decisions |
| -------------------------- | -------- | -------------------- |
| **Overall**                |          |                      |
| Public Vision Index        | **74.5** | 63.9                 |
| **Perception**             |          |                      |
| CV-Bench                   | **86.3** | 73.8                 |
| BLINK                      | **74.1** | 63.7                 |
| RealWorldQA                | **84.0** | 75.0                 |
| Winoground                 | **93.0** | 83.8                 |
| **Documents & charts**     |          |                      |
| CharXiv                    | **86.1** | 78.8                 |
| InfographicVQA             | **98.8** | 93.8                 |
| KIE (CORD + FUNSD)         | 95.5     | **97.5**             |
| **Agentic**                |          |                      |
| Mind2Web                   | **88.6** | 81.2                 |
| **Reasoning & safety**     |          |                      |
| R-Bench-M                  | **48.9** | 32.5                 |
| MMMU-Pro vision            | **53.6** | 41.2                 |
| Hateful Memes (moderation) | 70.6     | **71.2**             |

## Fast and cheap enough for every step

A generative model pays for every token it writes. Drex Max writes nothing, so a decision costs one pass over the input no matter how many options it scores. That makes it cheap enough to call on every step of every task.

[Recording: Drex's Eye](https://media.nace.ai/drex/drex-eye.mp4)

*Drex Max naming objects held up to a live camera, six calls a second.*

The recordings in this post show what that adds up to: an agent that books a flight or watches a live camera, with Drex Max making each decision along the way.

## Pricing and availability

Drex Max is available today through the Nace API at \$0.06 per million input tokens, with a 262K-token context window. Sign up at [console.nace.ai](https://console.nace.ai?utm_campaign=drex-max-launch&utm_content=post&utm_medium=website&utm_source=nace.ai) and get \$25 in free credits to try it on your own screens.

## About the product

**Drex Max is generally available.** Screens, documents and text in, a probability for every option out. Benchmarks and the API are on the product page, and an API key takes a minute.

- [Get started](https://console.nace.ai?utm_campaign=drex-max-launch&utm_content=announcement&utm_medium=website&utm_source=nace.ai)
- [Explore](https://nace.ai/drex)
- [Talk to sales](https://nace.ai/contact?product=drex&source=blog)

## For agents

- [Drex API guide](https://nace.ai/drex.md): the decision model: request shape, question types, benchmarks and deployment, as markdown
- [Decision endpoint](https://console.nace.ai/v1/systemone): POST with model "drex-latest", a state string and typed questions (choice, noul, score); https://console.nace.ai/v1/models lists checkpoints
- [Document endpoints](https://console.nace.ai/v1/documents/{parse|split|classify|extract|ground}): POST a source (a public URL, an uploaded workspace file or a finished parse job) and poll GET https://console.nace.ai/v1/documents/jobs/{id}; the same key and credit as decisions
- [Console](https://console.nace.ai): API keys, the playground, usage and billing; send `Authorization: Bearer $NACE_API_KEY` on every request
- [Console docs](https://console.nace.ai/docs): quickstart, question types, the document routes, errors, limits and pricing
- [Nace Document Intelligence](https://nace.ai/ndi.md): the Perception series: parse, split, classify, extract and ground any file type with a location on every value
- [Developers](https://nace.ai/developers.md): The execution layer for agents: Perception, Decision and Context behind one key, the Drex Max frontier, install steps, a scripted agent session and the four deployment boundaries
- [Site index](https://nace.ai/llms.txt): every page on nace.ai; append `.md` to any page URL or send `Accept: text/markdown` for the markdown version