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license: apache-2.0
base_model:
- Qwen/Qwen3.6-27B
pipeline_tag: image-text-to-text
---
<p align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/685ea8ff7b4139b6845ce395/_66bkNH630dGeIt2Uuctd.png" alt="logo" width="500">
</p>
<div align="center">
<a href="https://huggingface.co/OrionLLM/GRM-2.6-Plus/" style="text-decoration: none;">
<img src="https://img.shields.io/badge/🤗-HuggingFace-FC926C?style=for-the-badge" alt="HuggingFace">
</a>
<a href="https://huggingface.co/collections/OrionLLM/grm-26" style="text-decoration: none;">
<img src="https://img.shields.io/badge/📚-Collection-3B82F6?style=for-the-badge" alt="Collection">
</a>
<a href="https://www.apache.org/licenses/LICENSE-2.0" style="text-decoration: none;">
<img src="https://img.shields.io/badge/📜-License-E343BD?style=for-the-badge" alt="License">
</a>
</div>
## 1. Introduction
GRM-2.6-Plus is a **27B-parameter reasoning model** built for **general-purpose AI** and optimized for **difficult, high-complexity tasks**. It is designed to deliver stronger performance for its size while remaining practical, efficient, and accessible for advanced local and research-oriented use.
The model focuses on **structured reasoning**, helping it produce more accurate, coherent, and reliable responses across demanding problems. GRM-2.6-Plus brings **elite-level reasoning** to complex workloads, making it suitable for users who need a capable model for advanced problem-solving, coding, agents, and everyday intelligence.
## 2. Key Capabilities
- **Elite-Level Reasoning for Hard Tasks:** GRM-2.6-Plus is optimized to handle difficult reasoning workloads with clarity, consistency, and strong step-by-step problem-solving ability.
- **High Performance for Its Size:** With **27B parameters**, the model is designed to deliver excellent capability relative to its scale, balancing strong intelligence with practical deployment.
- **Advanced Coding and Agentic Use:** GRM-2.6-Plus is well suited for code generation, structured problem-solving, tool-style workflows, and local agentic applications.
- **Optimized for Practical Deployment:** The model aims to remain efficient and usable across capable consumer and workstation hardware while offering strong performance for advanced tasks.
## 3. Performance
GRM-2.6-Plus is designed to be a highly capable **27B local AI model** for complex reasoning, coding, everyday chat, and agentic workflows. It focuses on delivering **better performance for its size**, making it a strong option for users who want powerful reasoning without relying only on massive-scale models.
Its core strength is **practical intelligence**: elite-level reasoning, strong task understanding, stable responses, and the ability to handle difficult problems across multiple domains.
### Detailed Benchmarks
<table>
<tr>
<th style="background: rgba(128,128,128,0.1); text-align: center;"> </th>
<th style="background: rgba(128,128,128,0.1); text-align: center;">GRM-2.6-Plus</th>
<th style="background: rgba(128,128,128,0.1); text-align: center;">Qwen3.6-27B</th>
<th style="background: rgba(128,128,128,0.1); text-align: center;">google/gemma-4-31B-it</th>
<th style="background: rgba(128,128,128,0.1); text-align: center;">GPT-5.4-Mini</th>
<th style="background: rgba(128,128,128,0.1); text-align: center;">Claude-4.5-Haiku</th>
</tr>
<tr>
<td align="center" colspan="6" style="background: linear-gradient(90deg, rgba(124,58,237,0.45) 0%, rgba(99,102,241,0.42) 50%, rgba(59,130,246,0.45) 100%); font-weight: bold; height:32px; padding-top:2px; padding-bottom:2px;"><i>Knowledge & STEM</i></td>
</tr>
<tr>
<td align="center">MMLU-Pro</td>
<td align="center"><b>86.8</b></td>
<td align="center">86.2</td>
<td align="center">85.2</td>
<td align="center">--</td>
<td align="center">80.0</td>
</tr>
<tr>
<td align="center">MMLU-Redux</td>
<td align="center"><b>94.2</b></td>
<td align="center">93.5</td>
<td align="center">93.7</td>
<td align="center">--</td>
<td align="center">--</td>
</tr>
<tr>
<td align="center">C-Eval</td>
<td align="center"><b>92.0</b></td>
<td align="center">91.4</td>
<td align="center">82.6</td>
<td align="center">--</td>
<td align="center">--</td>
</tr>
<tr>
<td align="center">GPQA Diamond</td>
<td align="center"><b>88.3</b></td>
<td align="center">87.8</td>
<td align="center">84.3</td>
<td align="center">88.0</td>
<td align="center">73.0</td>
</tr>
<tr>
<td align="center">SuperGPQA</td>
<td align="center"><b>66.4</b></td>
<td align="center">66.0</td>
<td align="center">65.7</td>
<td align="center">--</td>
<td align="center">--</td>
</tr>
<tr>
<td align="center" colspan="6" style="background: linear-gradient(90deg, rgba(124,58,237,0.45) 0%, rgba(99,102,241,0.42) 50%, rgba(59,130,246,0.45) 100%); font-weight: bold; height:32px; padding-top:2px; padding-bottom:2px;"><i>Reasoning & Coding</i></td>
</tr>
<tr>
<td align="center">LiveCodeBench v6</td>
<td align="center"><b>84.8</b></td>
<td align="center">83.9</td>
<td align="center">80.0</td>
<td align="center">--</td>
<td align="center">51.1</td>
</tr>
<tr>
<td align="center">HMMT Feb 26</td>
<td align="center"><b>84.8</b></td>
<td align="center">84.3</td>
<td align="center">77.2</td>
<td align="center">--</td>
<td align="center">--</td>
</tr>
<tr>
<td align="center">AIME26</td>
<td align="center"><b>95.1</b></td>
<td align="center">94.1</td>
<td align="center">89.2</td>
<td align="center">--</td>
<td align="center">--</td>
</tr>
<tr>
<td align="center" colspan="6" style="background: linear-gradient(90deg, rgba(124,58,237,0.45) 0%, rgba(99,102,241,0.42) 50%, rgba(59,130,246,0.45) 100%); font-weight: bold; height:32px; padding-top:2px; padding-bottom:2px;"><i>General Agent</i></td>
</tr>
<tr>
<td align="center">SWE-bench Verified</td>
<td align="center"><b>77.7</b></td>
<td align="center">77.2</td>
<td align="center">52.0</td>
<td align="center">--</td>
<td align="center">73.3</td>
</tr>
<tr>
<td align="center">SWE-bench Pro</td>
<td align="center"><b>54.0</b></td>
<td align="center">53.5</td>
<td align="center">35.7</td>
<td align="center">54.4</td>
<td align="center">--</td>
</tr>
<tr>
<td align="center">Terminal-Bench 2.0</td>
<td align="center"><b>59.8</b></td>
<td align="center">59.3</td>
<td align="center">42.9</td>
<td align="center">60.0</td>
<td align="center">41.0</td>
</tr>
</table>
## 4. Family
The GRM-2.6 family is available in various sizes to suit every case.
<table>
<tr>
<th style="background: rgba(128,128,128,0.1); text-align: center;">Model</th>
<th style="background: rgba(128,128,128,0.1); text-align: center;">Size</th>
<th style="background: rgba(128,128,128,0.1); text-align: center;">Domain</th>
</tr>
<tr>
<td align="center">GRM-2.6-Plus</td>
<td align="center">27B</td>
<td align="center">Powerful model for extremely difficult tasks</td>
</tr>
<tr>
<td align="center">GRM-2.6</td>
<td align="center">9B</td>
<td align="center">Powerful on-device deployment for difficult tasks</td>
</tr>
<tr>
<td align="center">GRM-2.6-Air</td>
<td align="center">2B</td>
<td align="center">Any-device deployment for everyday chat</td>
</tr>
</table>
## 5. Architecture
GRM-2.6 is built on the Qwen3.6 architecture and is optimized for complex tasks, agent environments, and everyday chat.
GRM-2.6 applies the same principle to a stronger, larger foundation, resulting in a model that punches above its weight class on structured reasoning tasks while remaining deployable on consumer hardware.
## 6. Quick start
Before starting, make sure it is installed and the API key and the API base URL is configured, e.g.:
```shell
pip install -U openai
# Set the following accordingly
export OPENAI_BASE_URL="http://localhost:8000/v1"
export OPENAI_API_KEY="EMPTY"
```
#### Text-Only Input
```python
from openai import OpenAI
# Configured by environment variables
client = OpenAI()
messages = [
{"role": "user", "content": "Create an calculator in a single HTML file backwards"},
]
chat_response = client.chat.completions.create(
model="OrionLLM/GRM-2.5-Plus",
messages=messages,
max_tokens=81920,
temperature=1.0,
top_p=0.95,
presence_penalty=0.0,
extra_body={
"top_k": 20,
},
)
print("Chat response:", chat_response)
```
---
<div align="center">
**GRM-2.6-Plus** is developed by **[OrionLLM](https://huggingface.co/OrionLLM)** and released under the Apache 2.0 License.
</div>
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