nex-agi/Nex-N2.5-mini — new model trending #30 on Hugging Face
Nex-AGI releases Nex-N2.5 agentic model family (mini, Pro, Max) with a 1.6-trillion-parameter MoE Max, open weights, and hosted access via OpenRouter.
Nex-AGI launched Nex-N2.5, a family of agentic models in mini, Pro, and Max sizes, with the Max version built on a 1.6-trillion-parameter text-only Mixture-of-Experts foundation and the company's first complete post-training effort at trillion-parameter scale. The models target long-horizon computer use, web browsing, and visually grounded agentic tasks, with expanded agent training environments. Reported benchmarks include Max scoring 86.1 on Terminal-Bench 2.1 and 65.7 on SWE-Bench Pro, trailing Claude Opus 5. Weights are being released openly on Hugging Face and ModelScope, with hosted access through OpenRouter.
- Three sizes: mini, Pro, and 1.6T-parameter MoE Max with open weights.
- Max scores 86.1 on Terminal-Bench 2.1, 65.7 on SWE-Bench Pro.
- Focus on computer use, web browsing, and visually grounded agentic capabilities.
- Weights on Hugging Face and ModelScope; hosted access via OpenRouter.
Full article1,400 words · extracted from huggingface.co · click to collapse
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💻 <a href="https://github.com/nex-agi/Nex-N2.5">GitHub</a> ·
🤗 <a href="https://huggingface.co/collections/nex-agi/nex-n25">Hugging Face</a> ·
🌐 <a href="https://nex-agi.com/">Website</a>
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🔀 <a href="https://openrouter.ai/nex-agi/nex-n2.5-pro">OpenRouter (Pro)</a> ·
🔀 <a href="https://openrouter.ai/nex-agi/nex-n2.5-mini">OpenRouter (mini)</a>
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# Nex-N2.5
**A next-generation family of agentic models built for long-horizon tasks in real-world environments.**
Today, Nex-AGI officially introduces **Nex-N2.5**, its next-generation family of agentic models.
Nex-N2.5 is available in three sizes: **mini**, **Pro**, and **Max**. Nex-N2.5-mini and Nex-N2.5-Pro continue to build on the multimodal foundations of Nex-N2, with focused improvements in computer use, web browsing, and visually grounded agentic capabilities. Nex-N2.5-Max is built on a 1.6-trillion-parameter, text-only Mixture-of-Experts (MoE) foundation model, marking our first complete post-training effort at trillion-parameter scale.
For long-horizon tasks in real-world environments, Nex-N2.5 further strengthens its ability to act continuously and self-correct through visual feedback. The models can operate computers and browsers, as well as autonomously execute and test programs. Vision is therefore no longer merely an input modality; it has become a critical interface through which an agent perceives its environment, verifies outcomes, and moves a task forward.
Building on this foundation, we have further expanded the range of agent training environments, task types, and productivity scenarios, while completing systematic post-training at trillion-parameter scale for the first time. Through broader task coverage and richer environmental feedback, Nex-N2.5 delivers further gains in scientific research, knowledge work, and complex productivity tasks. This work also provides valuable practical experience for training agentic capabilities in even larger models.
By jointly advancing model training, infrastructure, and real-world agent scenarios, Nex-AGI aims to continue driving progress in agentic intelligence.
## Open Source
Model weights for the Nex-N2.5 family will be released as open source, alongside hosted online services.
- **Nex-N2.5-Max:** [Hugging Face](https://huggingface.co/nex-agi/Nex-N2.5-Max) | [ModelScope](https://modelscope.cn/models/nex-agi/Nex-N2.5-Max)
- **Nex-N2.5-Pro:** [Hugging Face](https://huggingface.co/nex-agi/Nex-N2.5-Pro) | [ModelScope](https://modelscope.cn/models/nex-agi/Nex-N2.5-Pro)
- **Nex-N2.5-mini:** [Hugging Face](https://huggingface.co/nex-agi/Nex-N2.5-mini) | [ModelScope](https://modelscope.cn/models/nex-agi/Nex-N2.5-mini)
- **Hosted Access:** [OpenRouter (Nex-N2.5-Pro)](https://openrouter.ai/nex-agi/nex-n2.5-pro) | [OpenRouter (Nex-N2.5-mini)](https://openrouter.ai/nex-agi/nex-n2.5-mini)
- **Websites:** [Global](https://nex-agi.com/)
We welcome developers and enterprises to integrate and try Nex-N2.5 and share their feedback.
## Performance
We evaluate Nex-N2.5 across coding, agentic workflows, computer use, and multimodal understanding.
The tables below compare **Nex-N2.5-mini**, **Nex-N2.5-Pro**, and **Nex-N2.5-Max** with leading models across our evaluation suite.<sup><a href="#benchmark-note-1">1</a>, <a href="#benchmark-note-2">2</a></sup> **Bold** marks the best result in each benchmark, including ties; — indicates unavailable data.<sup><a href="#benchmark-note-10">10</a></sup>
### Text Benchmarks
<!-- Keep benchmark rows first in each tbody; only section labels receive GitHub's alternating row background. -->
<table>
<thead>
<tr>
<th align="left">Benchmark</th>
<th align="center">Nex-N2.5-mini</th>
<th align="center">Nex-N2.5-Pro</th>
<th align="center">Nex-N2.5-Max</th>
<th align="center">Claude Opus 5</th>
<th align="center">GPT-5.6 Sol</th>
<th align="center">Kimi-K3</th>
<th align="center">GLM-5.3</th>
<th align="center">DeepSeek-V4-Pro-0813<sup><a href="#benchmark-note-4">4</a></sup></th>
<th align="center">Qwen3.8-Max</th>
</tr>
<tr>
<th colspan="10" align="left">CODING<sup><a href="#benchmark-note-3">3</a></sup></th>
</tr>
</thead>
<tbody>
<tr><td>Terminal-Bench 2.1</td><td align="center">73.4</td><td align="center">82.7</td><td align="center">86.1</td><td align="center"><b>89.1</b></td><td align="center">88.8</td><td align="center">88.3</td><td align="center">88.2</td><td align="center">87.9</td><td align="center">86.6</td></tr>
</tbody>
<tbody>
<tr><td>SWE-Bench Pro</td><td align="center">43.8</td><td align="center">61.2</td><td align="center">65.7</td><td align="center"><b>79.2</b></td><td align="center">64.6</td><td align="center">63.3</td><td align="center">64.6</td><td align="center">55.4</td><td align="center">67.7</td></tr>
</tbody>
<tbody>
<tr><td>DeepSWE v1.1</td><td align="center">36.1</td><td align="center">55.8</td><td align="center">65.6</td><td align="center"><b>73.7</b></td><td align="center">72.7</td><td align="center">67.5</td><td align="center">66.9</td><td align="center">62.8</td><td align="center">69.3</td></tr>
<tr><th colspan="10" align="left">AGENTIC</th></tr>
</tbody>
<tbody>
<tr><td>AutomationBench v1.0.6<sup><a href="#benchmark-note-5">5</a></sup></td><td align="center">32.3</td><td align="center">44.2</td><td align="center">50.2</td><td align="center"><b>50.3</b></td><td align="center">45.8</td><td align="center">46.7</td><td align="center">48.2</td><td align="center">43.2</td><td align="center">39.8</td></tr>
</tbody>
<tbody>
<tr><td>Toolathlon Verified</td><td align="center">54.6</td><td align="center">68.5</td><td align="center">74.7</td><td align="center"><b>76.5</b></td><td align="center">74.9</td><td align="center"><b>76.5</b></td><td align="center">73.0</td><td align="center">74.1</td><td align="center">72.5</td></tr>
</tbody>
<tbody>
<tr><td>GDPval-AA v2</td><td align="center">1446</td><td align="center">1628</td><td align="center">1713</td><td align="center"><b>1831</b></td><td align="center">1711</td><td align="center">1675</td><td align="center">1763</td><td align="center">1580</td><td align="center">1717</td></tr>
</tbody>
<tbody>
<tr><td>Job Bench</td><td align="center">28.5</td><td align="center">41.4</td><td align="center">53.6</td><td align="center"><b>65.7</b></td><td align="center">45.4</td><td align="center">52.9</td><td align="center">58.2</td><td align="center">54.1</td><td align="center">53.4</td></tr>
</tbody>
<tbody>
<tr><td>BrowseComp<sup><a href="#benchmark-note-6">6</a></sup></td><td align="center">83.4</td><td align="center">89.7</td><td align="center"><b>92.6</b></td><td align="center">90.8</td><td align="center">90.4</td><td align="center">91.2</td><td align="center">—</td><td align="center">—</td><td align="center">—</td></tr>
</tbody>
</table>
### Multimodal Benchmarks
<table>
<thead>
<tr>
<th align="left">Benchmark</th>
<th align="center">Nex-N2.5-mini</th>
<th align="center">Nex-N2.5-Pro</th>
<th align="center">MiniMax-M3</th>
<th align="center">Claude Opus 5</th>
<th align="center">GPT-5.6 Sol</th>
<th align="center">Kimi-K3</th>
<th align="center">GLM-5.3-Flash</th>
<th align="center">DeepSeek-V4-Flash-Vision</th>
<th align="center">Qwen3.8-Max</th>
</tr>
</thead>
<tbody>
<tr><td>OSWorld-Verified<sup><a href="#benchmark-note-8">8</a></sup></td><td align="center">71.2</td><td align="center">82.2</td><td align="center">75.2</td><td align="center">83.4</td><td align="center">83.2</td><td align="center">84.8</td><td align="center">62.3</td><td align="center">76.7</td><td align="center"><b>86.1</b></td></tr>
</tbody>
<tbody>
<tr><td>OSWorld-2</td><td align="center">30.5</td><td align="center">56.4</td><td align="center">22.3</td><td align="center"><b>68.3</b></td><td align="center">62.7</td><td align="center">58.3</td><td align="center">—</td><td align="center">—</td><td align="center">46.7</td></tr>
</tbody>
<tbody>
<tr><td>WebTest<sup><a href="#benchmark-note-8">8</a>, <a href="#benchmark-note-9">9</a></sup></td><td align="center">48.6</td><td align="center">52.8</td><td align="center">—</td><td align="center">—</td><td align="center"><b>54.0</b></td><td align="center">—</td><td align="center">—</td><td align="center">—</td><td align="center">52.3</td></tr>
</tbody>
<tbody>
<tr><td>WebArena-Verified<sup><a href="#benchmark-note-8">8</a></sup></td><td align="center">63.4</td><td align="center">67.6</td><td align="center">—</td><td align="center">—</td><td align="center">69.7</td><td align="center"><b>71.6</b></td><td align="center">—</td><td align="center">62.3</td><td align="center">66.8</td></tr>
</tbody>
<tbody>
<tr><td>OSWorld-G</td><td align="center">82.9</td><td align="center"><b>87.4</b></td><td align="center">—</td><td align="center">76.8</td><td align="center">77.7</td><td align="center">79.6</td><td align="center">83.3</td><td align="center">59.4</td><td align="center">84.9</td></tr>
</tbody>
<tbody>
<tr><td>Vision2Web<sup><a href="#benchmark-note-7">7</a></sup></td><td align="center">52.9</td><td align="center">68.2</td><td align="center">59.0</td><td align="center">—</td><td align="center"><b>79.8</b></td><td align="center">—</td><td align="center">—</td><td align="center">—</td><td align="center">75.1</td></tr>
</tbody>
<tbody>
<tr><td>SWE-MM</td><td align="center">25.5</td><td align="center">38.2</td><td align="center">—</td><td align="center"><b>59.4</b></td><td align="center">40.2</td><td align="center">37.3</td><td align="center">20.6</td><td align="center">39.2</td><td align="center">39.2</td></tr>
</tbody>
<tbody>
<tr><td>OmniDoc</td><td align="center">89.7</td><td align="center">92.2</td><td align="center">91.6</td><td align="center">—</td><td align="center"><b>92.9</b></td><td align="center">91.1</td><td align="center">—</td><td align="center">—</td><td align="center">92.1</td></tr>
</tbody>
Text extracted automatically; images, tables and formatting may be missing. Original: https://huggingface.co/nex-agi/Nex-N2.5-mini