Update README.md

README.md

@@ -40,6 +40,22 @@ Dhanishtha is designed for:
 - **Deep problem-solving and strategic reasoning**
 - **Engaging and thought-provoking conversations**
 - **Use in AI-driven research and advanced dialogue systems**
+- 
+## Benchmarks
+We report Pass@1 accuracy averaged over 16 samples for each problem.
+
+| Model                         | AIME 2024 | MATH 500 | AMC 2023 | Minerva Math | OlympiadBench | Avg. |
+|-------------------------------|-----------|----------|----------|--------------|----------------|------|
+| 2.5-7B-Instruct               | 13.3      | 79.8     | 50.6     | 34.6         | 40.7           | 43.8 |
+| rStar-Math-7B                 | 26.7      | 78.4     | 47.5     | -            | 47.1           | -    |
+| Eurus-2.7B-PRIME              | 26.7      | 79.2     | 57.8     | 38.6         | 42.1           | 48.9 |
+| Qwen2.5-7B-SimpleRL           | 26.7      | 82.4     | 62.5     | 39.7         | 43.3           | 50.9 |
+| DeepSeek-R1-Distill-Qwen-1.5B | 28.8      | 82.8     | 62.9     | 26.5         | 43.3           | 48.9 |
+| Still-1.5B                    | 32.5      | 84.4     | 66.7     | 29.0         | 45.4           | 51.6 |
+| DeepScaleR-1.5B-Preview       | 43.1      | 87.8     | 73.6     | 30.2         | 50.0           | 57.0 |
+| O1-Preview                    | 40.0      | 81.4     | -        | -            | -              | -    |
+| **Dhanishta**                    | 38.2      | 85.1     | 70.3     | 30.5         | 42.0           | 53.2 |
+
 
 ## Credits & License
 Dhanishtha is developed and maintained by **HelpingAI**, pushing the boundaries of AI-driven introspection and structured reasoning. The model is open-source and community-driven, encouraging contributions and collaborative innovation.