Early September brought a sizable move in the agent space: Nex AGI shipped its next-generation model family, Nex-N2.5, in three tiers at once — mini, Pro, and Max. The headline is Max, a 1.6-trillion-parameter, text-only Mixture-of-Experts model that the company describes as its "first complete post-training effort at trillion-parameter scale." Weights for all three tiers are open, downloadable from Hugging Face and ModelScope, with mini and Pro also available through hosted OpenRouter endpoints.

Vision as an interface, not an input

Nex-N2.5 targets long-horizon tasks in real environments: operating computers, browsing the web, and writing and testing programs autonomously. mini and Pro build on the multimodal foundations of the earlier Nex-N2 generation, with focused gains in computer use, web browsing, and visually grounded agent capabilities. The README puts it crisply: vision is no longer merely an input modality — it is the interface through which an agent perceives its environment, verifies outcomes, and moves a task forward. The model watches the screen to act, then watches the result to self-correct.

Benchmarks: wins on specific fronts, chasing overall

The official evaluation tables put the three tiers against Claude Opus 5, GPT-5.6 Sol, Kimi-K3, GLM-5.3, DeepSeek-V4-Pro, and Qwen3.8-Max. The highlights: Max scores 92.6 on BrowseComp, ahead of Claude Opus 5 (90.8), GPT-5.6 Sol (90.4), and Kimi-K3 (91.2) — the top score in that table; Pro takes 87.4 on OSWorld-G, above Qwen3.8-Max (84.9) and Claude Opus 5 (76.8). The gaps are equally clear: on Terminal-Bench 2.1, Max's 86.1 trails Opus 5's 89.1; on SWE-Bench Pro it is 65.7 versus 79.2. The honest summary: it can beat frontier closed models on individual agentic tests while still chasing them overall.

Deployment: a trillion parameters is not free

The reference deployment for Max spans 2 nodes with 32 H200s in total (a custom SGLang fork shipped as a Docker image, with a 262,144-token context); Pro drops to a single node of 8×H100; mini serves on two H100s. The family supports three thinking modes via reasoning_effort (off / adaptive / always-on) and uses the qwen3_coder tool-call parser. Multiple independent trackers confirm all three tiers ship under Apache-2.0.

So what

The story here is not "another set of open weights." It is that after walking a trillion-parameter MoE through a complete post-training pipeline, Nex AGI published the evaluation setup, deployment commands, and thinking-mode switches along with it — one more referenceable public recipe for agent-native training. For app teams, mini runs on two cards and OpenRouter access is a low-friction starting point. The question worth watching next: can this visual-feedback self-correction recipe travel down to much smaller models?

Full benchmark tables and deployment commands are in the official repository: https://github.com/nex-agi/Nex-N2.5