Update README.md

README.md

@@ -36,11 +36,32 @@ Built upon a 8B dense language model (Qwen3) and a 400M Vision encoder (InternVi
 
 ## Performance
 
-We evaluate the Intern-S1-mini  on various benchmarks including general datasets and scientifc datasets. We report the performance comparsion with the recent VLMs and LLMs below. 
+We evaluate the Intern-S1-mini  on various benchmarks including general datasets and scientific datasets. We report the performance comparison with the recent VLMs and LLMs below. 
+
+
+|            |                | Intern-S1-mini    | Qwen3-8B | GLM-4.1V | MiMo-VL-7B-RL-2508 |
+|------------|----------------|-------------------|----------|----------|--------------------|
+| General    | MMLU-Pro       | **74.78**         | 73.7     | 57.1     | 73.93              |
+| 　         | MMMU           | **72.33**         | N/A      | 69.9     | 70.4               |
+| 　         | MMStar         | 65.2              | N/A      | 71.5     | 72.9               |
+| 　         | GPQA           | **65.15**         | 62       | 50.32    | 60.35              |
+| 　         | AIME2024       | **84.58**         | 76       | 36.2     | 72.6               |
+| 　         | AIME2025       | **80**            | 67.3     | 32       | 64.4               |
+| 　         | MathVision     | 51.41             | N/A      | 53.9     | 54.5               |
+| 　         | MathVista      | 70.3              | N/A      | 80.7     | 79.4               |
+| 　         | IFEval         | 81.15             | 85       | 71.53    | 71.4               |
+|            |                |                   |          |          |                    |
+| Scientific | SFE            | 35.84             | N/A      | 43.2     | 43.9               |
+| 　         | Physics        | **28.76**         | N/A      | 4.3      | 23.9               |
+| 　         | SmolInstruct   | **32.2**          | 17.6     | 18.1     | 16.11              |
+| 　         | ChemBench      | **76.47**         | 61.1     | 56.2     | 66.78              |
+| 　         | MatBench       | **61.55**         | 45.24    | 54.3     | 46.9               |
+| 　         | MicroVQA       | **56.62**         | N/A      | 50.2     | 50.96              |
+| 　         | ProteinLMBench | 58.47             | 59.1     | 58.3     | 59.8               |
+| 　         | MSEarthMCQ     | **58.12**         | N/A      | 50.3     | 47.3               |
+| 　         | XLRS-Bench     | **51.63**         | N/A      | 49.8     | 12.29              |
 
 
-> **Note**: ✅ means the best performance among open-sourced models, 👑 indicates the best performance among all models.
-
 We use the [OpenCompass](https://github.com/open-compass/OpenCompass/) and [VLMEvalkit](https://github.com/open-compass/vlmevalkit) to evaluate all models.