Introduction
There is currently an enormous paradigm shift taking place in modern sparse open-weight architectures in how they process multimodal streams. Rather than treating graphical streams as passive data input or using text-based command chains only, today’s architectures such as Nex-N2.5 take advantage of optical perception streams for ongoing runtime error correction. This architectural advance provides a way of overcoming the frailties of traditional automation pipeline design, which cannot cope with minor interface rendering errors or unexpected terminal outputs during multi-turn processes.
It is important to strike a balance between selective parameter passing and efficiency when running autonomous extended processes in the enterprise environment. Being able to adapt the computing infrastructure for advanced computation to work for workflow is key to achieving efficient execution in software engineering, web navigation, and interface control. It is through making such hardware optimization work in combination with closed-loop image validation to provide self-rectification in an autonomous manner that modern architectures such as Nex-N2.5 achieve desktop agency and multi-step reasoning without dense latency cost.
What is Nex-N2.5?
Nex-N2.5 is a family of Mixture-of-Experts (MoE) models developed by Nex-AGI, explicitly purpose-built for visually grounded agency, native desktop and web GUI navigation, autonomous software development, and closed-loop execution. Transitioning from traditional text-heavy agent frameworks, the model utilizes continuous visual perception as a real-time verification interface to execute code, manage operating systems, navigate complex web applications, and self-correct system actions based on observed environmental states.
Model Variants
- Nex-N2.5-mini: Runs on 35 Billion total parameters (MoE) with 3 Billion active parameters per token (A3B). Built on top of the Qwen3.5-35B-A3B-Base multimodal foundation, this model is designed to achieve fast instruction-following, low-latency API serving, and on-the-fly tool execution. Efficiently running on a single node featuring 2×H100 GPUs with Tensor Parallelism (TP=2).
- Nex-N2.5-Pro: Equipped with 397 Billion total parameters (MoE) with 17 Billion active parameters per token (A17B) and built on top of the Qwen3.5-397B-A17B multimodal base, this is the main workhorse model for complex reasoning, multi-agent orchestration, and all-in-one developer stack. Designed for efficient running on a single node with 8×H100 GPUs (TP=8) using the dedicated NexRT inference engine.
- Nex-N2.5-Max: This is the flagship variant equipped with 1.6 Trillion total parameters (MoE) with 49 Billion active parameters per token (A49B). In contrast to other variants, this one uses the DeepSeek-V4-Pro-Base text-only foundation model for deep reasoning, scientific research modeling, and high-level architectural code generation. Efficiently running on multi-node clusters.
Key Features of Nex-N2.5
- Visually Grounded Agency & Closed Loop Self Correction: Controls web browsers and desktop operating systems through a visually grounded feedback loop. This model performs visual action execution, visual UI update evaluation, detects rendering bugs/page anomalies, and dynamically re-plans execution in real time, without any human involvement.
- Native GUI Navigation & Normalized Grounding: Can perform exact cursor-mouse actions in both desktop and web applications. For achieving spatial precision in different resolutions (OSWorld, WebArena, and WebTest benchmarks), normalized grounding is used in a standardized 0–1000 spatial grid.
- Coherent Logic & Multi-Step State Traversal: Maintains coherent reasoning capabilities across the entire process of task decomposition, strategic changes, visual state traversal, and self assessment within complex context switches.
- Trillion Parameter Scale Post Training Pipeline: Represents the first time Nex-AGI is attempting a post training process at a 1.6 trillion parameter scale. The pipeline receives live terminal output, web DOM tree, and screenshot streams directly into the training loop.
- Heterogeneous Base Foundation Models: Uses a combination approach by utilizing multimodal Qwen3.5 models with visual spatial interactions (Mini and Pro) as well as a 1.6T text only DeepSeek-V4-Pro base (Max) for logical synthesis.
Use Cases of Nex-N2.5
- Dual Model Autonomous Code Refactoring & Visual Playtesting: Facilitates automation of CI/CD pipelines where the state-of-the-art reasoning model rewrites complex codebase, and a multimodal model serves as the workhorse that builds up the software, opens the GUI, validates the UI rendering against design specifications and sends the screenshot differences to the code refactoring model to perform corrections before merging.
- Closed Loop Visual GUI Navigation with Autonomous Corrections: Navigates through browser and desktop applications checking the visual state after each click, and autonomously changes the path execution if unexpected popups appear or sites are broken.
- Multi-Backbone Models Inference under a Single Gateway: Simplifies enterprise inference management through serving of lightweight, medium, and ultra-heavy models under one standardized API gateway with custom server-side reasoning and tool-parsing routers.
- Zero-Script Legacy ERP and Desktop Workflow Automation: Automates multi-step workflows within enterprise applications including legacy ERP and desktop applications as well as mainframes emulators, using screen vision and normal coordinates control, without the use of unreliable APIs and RPA scripts.
- Engineering Task Criticality-Based SLA-Allocation of Compute Resources: Minimizes company compute cost by allocating developer traffic depending on the criticality of their tasks, sending code completion to high performance 2× H100 machines, feature requests to mid-performance 8× H100 machines and architecture-related tasks to high end multi-node machines.
- Cost Margin and Latency Management via Reasoning Control: Controls product margins and latencies by configuring API requests to skip the thinking trace, use adaptive thinking trace or deep reasoning trace depending on the criticality of the request.
How Does Nex-N2.5 Work?
Nex-N2.5 integrates an execution-feedback loop into its post-training flow, end-to-end. Instead of making predictions for the next tokens based only on pairs of instruction and response that don’t change during training, the training procedure makes the model experience the live execution environment by receiving terminal output streams, web DOM tree structures, and desktop screenshot streams in real-time. Consequently, the model learns to map raw vision observations directly to useful tool invocations and normalized keyboard/mouse coordinates on a 0-1000 spatial grid.
To enable efficient decoding in real-time on the 8xH100/H200 GPU cluster of Nex-N2.5-Pro, Nex-AGI has developed the dedicated inference engine NexRT. The engine supports Standard Decoding with Multi-Token Prediction (MTP) and DFlash block-diffusion. The engine features custom CUDA kernels, full decode CUDA Graphs, GPU Direct between nodes communication, and Context-Parallel Attention (CP) that allows avoiding duplicate KV cache reads with extended context windows.
Performance Comparison with other Models
In the context of web browsing and long context information synthesis, BrowseComp draws attention to Nex-N2.5-Max which has achieved the #1 rank worldwide and is directly better than some of the best proprietary frontier models like Claude Opus 5 and GPT-5.6 Sol. In addition to that, under the same evaluation paradigm, Pro and mini versions have shown significant generational improvements compared to their predecessors. While showing superiority in web browsing, Nex-N2.5-Pro has achieved the #1 rank worldwide in OSWorld-G spatial grounding benchmarks, beating other best visual and multimodal models like Qwen3.8-Max, GLM-5.3-Flash, GPT-5.6 Sol, and Claude Opus 5.
source - https://nex-agi.com/
When it comes to software engineering for repositories and debugging multiple files, the evaluations on SWE-Bench Pro and DeepSWE v1.1 show how well the model can generate code and solve problems. The model, compared to leading open-weight models such as DeepSeek-V4-Pro and GLM-5.3, shows better results on most of the important agentic, web, and programming benchmarks, while on other benchmarks, such as SWE-Bench Pro and GUI navigation in space, it shows competitive performance with some targeted leads. This good performance showcases the efficiency of the model suite in dealing with large full-stack codebases and autonomous engineering problems.
source - https://nex-agi.com/
In all types of agentic workflows, desktop operating system controls, and multi-turn execution of tools, Nex-N2.5 remains consistently powerful in different evaluation sets. In all evaluation sets for workflow and knowledge performance such as GDPval-AA v2, AutomationBench, and Toolathlon Verified, the Max version is superior to top frontier agents such as GPT-5.6 Sol, GLM-5.3, Kimi-K3, and DeepSeek-V4-Pro. Also, the excellent performance on Terminal-Bench, OSWorld-Verified, and OSWorld-2 shows the cross-domain versatility of the family of models, proving that the feedback loop for perception and execution in reality is successful.
How to Access and Use Nex-N2.5?
Model Weights for Nex-N2.5 are available under the Apache-2.0 License through Hugging Face and ModelScope, while the codebase is available at GitHub. Hosted API endpoints can be accessed through OpenRouter. Reasoning efforts during API calls can be dynamically controlled through 'reasoning_effort'. During SGLang deployment, launching scripts require providing '--tool-call-parser qwen3_coder', as well as corresponding reasoning parsers: '--reasoning-parser qwen3' for Mini/Pro or '--reasoning-parser deepseek-r1' for Max.
Limitations and Future Work
Using the Nex-N2.5 framework involves significant overhead in terms of hardware infrastructure as the Max model needs at least 16× H200 GPUs in 2 nodes with DeepEP/DeepGEMM networking, whereas the Pro model needs an 8× H100 node with SGLang patches for ideal token decoding. Going forward, work will focus on using trillion-scale post-training knowledge for larger foundation backbones and complete open-source availability of the NexCUA evaluation framework.
Conclusion
Nex-N2.5 presents an implementation strategy for open weight agentic architectures in transforming computer vision into a proactive visual execution cycle instead of a passive description method. It illustrates the way in which enterprise automation systems can move away from fragile brittle scripting to visual self-correcting autonomy through the use of precise spatial grounding along with customized inference engines such as NexRT.
Sources:
https://nex-agi.com/
https://github.com/nex-agi/Nex-N2.5
https://huggingface.co/collections/nex-agi/nex-n25
https://huggingface.co/nex-agi/Nex-N2.5-Max
https://huggingface.co/nex-agi/Nex-N2.5-Pro
https://huggingface.co/nex-agi/Nex-N2.5-mini
Disclaimer - This article is intended purely for informational purposes. It is not sponsored or endorsed by any company or organization, nor does it serve as an advertisement or promotion for any product or service. All information presented is based on publicly available resources and is subject to change. Readers are encouraged to conduct their own research and due diligence.
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