Nex-AGI announces the Nex-N2.5 agentic family

Nex-AGI on September 8 introduced Nex-N2.5, a three-size family of open-source agentic models aimed at long-horizon work in real-world environments. The @NexEcosystem account framed Mini (35B) and Pro (397B) as multimodal models with computer use, and Max (1.6T) as a text-only mixture-of-experts model for complex reasoning, coding, and agent workflows.

That is the announcement, not a completed weight drop. The Nex-N2.5-Pro model card on Hugging Face still banners that weights are coming soon. Nex-AGI says family weights will be released as open source alongside hosted services. The same card links GitHub, a Hugging Face collection, ModelScope pages, and OpenRouter listings for Mini and Pro.

What the models are for

Mini and Pro continue the multimodal line of Nex-N2, with claimed gains in computer use, web browsing, and visually grounded agents. The card says the models can operate computers and browsers, execute and test programs, and self-correct through visual feedback—vision as an interface for perceiving the environment and verifying outcomes, not only as an input modality. Max is described as a 1.6-trillion-parameter text-only MoE and Nex-AGI's first complete post-training effort at trillion-parameter scale.

Vendor-reported scores

Nex-AGI published its own comparison tables. On AutomationBench v1.0.6, Max scores 50.2 against Claude Opus 5 at 50.3. On OSWorld-2, Pro scores 56.4 against Qwen3.8-Max at 46.7 (Claude Opus 5 is listed at 68.3). Those figures come from the vendor card, which notes a mix of public leaderboards, provider reports, and Nex-AGI's own evaluations. Computer-use and browser-use benches, including OSWorld, used Nex-AGI's NexCUA harness, which the card says will be open-sourced soon.

How you would run them

The Pro card documents Docker serve commands on a customized SGLang image (nexagi/sglang:v0.5.18-nex-patch), with recommended sampling of temperature 0.7, top_p 0.95, and top_k 40, plus reasoning_effort modes (none, medium, high) and a Qwen3-coder tool-call parser. Those recipes only matter once weights actually land.

If you are building a local computer-use stack, watch the Pro card and the Nex-N2.5 Hugging Face collection for the weight release, or try the hosted Mini and Pro endpoints linked from that card.