--- title: SenseNova-SI-1.5-InternVL3-8B canonical_url: "https://www.modelscope.cn/models/SenseNova/SenseNova-SI-1.5-InternVL3-8B" md_url: "https://www.modelscope.cn/models/SenseNova/SenseNova-SI-1.5-InternVL3-8B.md" repository: SenseNova/SenseNova-SI-1.5-InternVL3-8B last_updated: 2026-05-13 license: apache-2.0 pipeline_tag: image-text-to-text tasks: - image-text-to-text model_type: - internvl_chat architectures: - InternVLChatModel base_model: - sensenova/SenseNova-SI-1.4-InternVL3-8B base_model_relation: finetune parameters: 7.9B tensor_type: - BF16 library_name: - safetensors - pytorch frameworks: - pytorch downloads: 53 stars: 0 --- # SenseNova-SI-1.5-InternVL3-8B > SenseNova-SI-1.5-InternVL3-8B - SenseNova 在 ModelScope 开源的模型。SenseNova-SI: Scaling Spatial Intelligence with Multimodal Foundation Models SenseNova/SenseNova-SI-1.5-InternVL3-8B 是 ModelScope 魔搭社区上的 7.9B 参数image-text-to-text模型，采用 apache-2.0 许可，基于 sensenova/SenseNova-SI-1.4-InternVL3-8B 构建。 - **Repository**: SenseNova/SenseNova-SI-1.5-InternVL3-8B - **License**: apache-2.0 - **Tasks**: image-text-to-text - **Parameters**: 7.9B - **Base model**: sensenova/SenseNova-SI-1.4-InternVL3-8B - **Downloads**: 53 - **Stars**: 0 - **Last updated**: 2026-05-13 Source: https://www.modelscope.cn/models/SenseNova/SenseNova-SI-1.5-InternVL3-8B --- **EN** | [中文](README_CN.md) # SenseNova-SI: Scaling Spatial Intelligence with Multimodal Foundation Models Code arXiv Code Leaderboard ## Overview Despite remarkable progress, multimodal foundation models still exhibit surprising deficiencies in spatial intelligence. In this work, we explore scaling up multimodal foundation models to cultivate spatial intelligence within the **SenseNova-SI family**, built upon established multimodal foundations including visual understanding models (i.e., Qwen3-VL and InternVL3) and unified understanding and generation models (i.e., Bagel). We take a principled approach to constructing high-performing and robust spatial intelligence by systematically curating SenseNova-SI-8M: eight million diverse data samples under a rigorous taxonomy of spatial capabilities. SenseNova-SI demonstrates unprecedented performance across a broad range of spatial intelligence benchmarks, while maintaining strong general multimodal understanding. More importantly, we analyze the impact of data scaling, discuss early signs of emergent generalization capabilities enabled by diverse data training, analyze the risk of overfitting and language shortcuts, present a preliminary study on spatial chain-of-thought reasoning, and validate the potential downstream application. SenseNova-SI is an ongoing project, and this report will be updated continuously. All newly trained multimodal foundation models are publicly released to facilitate further research in this direction. *In the future, SenseNova-SI will be integrated with larger-scale in-house models.* ## Models Zoo
Model Base Architecture SI Dataset Scale EASI-8 Other Remarks
SenseNova-SI-1.5-InternVL3-8B SenseNova-SI-1.4-InternVL3-8B 1.5M 64.4 Enhanced capability in solid geometry
SenseNova-SI-1.4-InternVL3-8B InternVL3 29M 63.7 Enhanced capability in grounding and depth estimation
SenseNova-SI-1.3-InternVL3-8B InternVL3 14M 65.2 Best in spatial intelligence, with enhanced capabilities for open-ended short QA
SenseNova-SI-1.2-InternVL3-8B InternVL3 10M 64.5 -
SenseNova-SI-1.1-InternVL3-8B InternVL3 8M 61.5 -
SenseNova-SI-1.1-InternVL3-2B InternVL3 8M 49.4 -
SenseNova-SI-1.1-Qwen3-VL-8B Qwen3-VL 8M 58.1 -
SenseNova-SI-1.1-Qwen2.5-VL-7B Qwen2.5-VL 8M 51.0 -
SenseNova-SI-1.1-Qwen2.5-VL-3B Qwen2.5-VL 8M 45.7 -
SenseNova-SI-1.1-BAGEL-7B-MoT BAGEL 8M 48.6 Unified understanding and generation model
## Release Information Currently, we build SenseNova-SI upon popular open-source foundation models to maximize compatibility with existing research pipelines. In this release, we present [**SenseNova-SI-1.5-InternVL3-8B**](https://huggingface.co/sensenova/SenseNova-SI-1.5-InternVL3-8B), [**SenseNova-SI-1.4-InternVL3-8B**](https://huggingface.co/sensenova/SenseNova-SI-1.4-InternVL3-8B), [**SenseNova-SI-1.3-InternVL3-8B**](https://huggingface.co/sensenova/SenseNova-SI-1.3-InternVL3-8B), [**SenseNova-SI-1.2-InternVL3-8B**](https://huggingface.co/sensenova/SenseNova-SI-1.2-InternVL3-8B), [**SenseNova-SI-1.1-Qwen2.5-VL-3B**](https://huggingface.co/sensenova/SenseNova-SI-1.1-Qwen2.5-VL-3B), [**SenseNova-SI-1.1-Qwen2.5-VL-7B**](https://huggingface.co/sensenova/SenseNova-SI-1.1-Qwen2.5-VL-7B), and [**SenseNova-SI-1.1-Qwen3-VL-8B**](https://huggingface.co/sensenova/SenseNova-SI-1.1-Qwen3-VL-8B). **SenseNova-SI-1.5-InternVL3-8B** demonstrates strong spatial intelligence across a wide range of benchmarks, with notable improvements in analyzing and solving **solid geometric** problems, achieving an accuracy of **63.5** on SolidGeo MCQ, **72.7** on SolidMath and **68.9** on Math3D.
Model VSI MMSI MindCube-Tiny ViewSpatial SITE BLINK 3DSRBench EmbSpatial-Bench
Open-source Models (~2B)
InternVL3-2B32.926.537.532.530.050.847.760.1
Qwen3-VL-2B-Instruct50.328.934.536.935.653.247.570.1
MindCube-3B-RawQA-SFT17.21.751.724.16.335.12.837.0
SpatialLadder-3B44.827.443.439.827.943.042.858.2
SpatialMLLM-4B46.326.133.434.618.040.536.250.0
VST-3B-SFT57.930.235.952.835.858.854.169.0
Cambrian-S-3B57.325.232.539.028.337.750.963.5
Open-source Models (~8B)
InternVL3-8B42.128.041.538.641.153.544.376.4
Qwen3-VL-8B-Instruct57.931.129.442.245.866.753.977.7
BAGEL-7B-MoT31.431.034.741.337.063.750.273.1
SpaceR-7B41.527.437.935.834.249.640.566.9
ViLaSR-7B44.630.235.135.738.751.446.667.3
VST-7B-SFT60.632.039.750.539.661.954.673.7
Cambrian-S-7B67.525.839.640.933.037.954.872.8
SenseNova-SI-1.5-InternVL3-8B 67.3 38.3 92.1 59.0 47.5 69.5 61.3 80.3
Proprietary Models
Gemini-2.5-pro-2025-0653.538.057.646.057.073.559.378.9
Grok-4-2025-07-0947.937.863.543.247.056.454.975.7
GPT-5-2025-08-0755.041.856.345.561.868.060.381.6
For solid geometry benchmarks, we report the following results:
Model SolidGeo MCQ SpatialViz-Bench SolidMath Math3D
InternVL3-8B36.432.042.543.7
SenseNova-SI-1.3-InternVL3-8B36.529.639.640.3
SenseNova-SI-1.5-InternVL3-8B 63.5 33.0 72.7 68.9
The SolidMath and Math3D are internal benchmarks constructed from K12 question banks, containing multiple-choice problems in Chinese on solid geometry. SolidMath is built from in-domain data and Math3D is derived from out-of-domain data. ## 🛠️ QuickStart ### Installation We recommend using [uv](https://docs.astral.sh/uv/) to manage the environment. > uv installation guide: ```bash git clone git@github.com:OpenSenseNova/SenseNova-SI.git cd SenseNova-SI/ uv sync --extra cu124 # or one of [cu118|cu121|cu124|cu126|cu128|cu129], depending on your CUDA version uv sync source .venv/bin/activate ``` #### Hello World A simple image-free test to verify environment setup and download the model. ```bash python example.py \ --question "Hello" \ --model_path sensenova/SenseNova-SI-1.5-InternVL3-8B ``` ### Examples #### Example 1 This example is from [SITE-Bench](https://github.com/wenqi-wang20/SITE-Bench): ```bash python example.py \ --image_paths examples/Q1_1.png \ --question "Consider the real-world 3D locations of the objects. Which is closer to the sink, the toilet paper or the towel?\nOptions: \nA. toilet paper\nB. towel\nGive me the answer letter directly. The best answer is:" \ --model_path sensenova/SenseNova-SI-1.5-InternVL3-8B ```
Details of Example 1

Q:Consider the real-world 3D locations of the objects. Which is closer to the sink, the toilet paper or the towel?\nOptions: \nA. toilet paper\nB. towel\nGive me the answer letter directly. The best answer is:

First image

GT: A

#### Example 2 This example is from [MMSI-Bench](https://github.com/InternRobotics/MMSI-Bench): ```bash python example.py \ --image_paths examples/Q2_1.png examples/Q2_2.png \ --question "If the landscape painting is on the east side of the bedroom, where is the window located in the bedroom?\nOptions: A. North side, B. South side, C. West side, D. East side\nAnswer with the option's letter from the given choices directly. Enclose the option's letter within ``." \ --model_path sensenova/SenseNova-SI-1.5-InternVL3-8B ```
Details of Example 2

Q:If the landscape painting is on the east side of the bedroom, where is the window located in the bedroom?\nOptions: A. North side, B. South side, C. West side, D. East side\nAnswer with the option's letter from the given choices directly. Enclose the option's letter within ``.

First image Second image

GT: C

#### Example 3 This example demonstrates the model's capability in **solid geometry(Three views)**: ```bash python example.py \ --image_paths examples/Q3_1.png \ --question "Enclose your thinking process in tags and your final answer in " \ --model_path sensenova/SenseNova-SI-1.5-InternVL3-8B ```
Details of Example 3

Q: Enclose your thinking process in <think> </think> tags and your final answer in <answer> </answer>

First image

GT: D

#### Example 4 This example demonstrates the model's capability in **solid geometry(Nets of 3D Shapes)**: ```bash python example.py \ --image_paths examples/Q4_1.png \ --question "请将你的思考过程放在标签内，并将你的最终答案放在标签内。" \ --model_path sensenova/SenseNova-SI-1.5-InternVL3-8B ```
Details of Example 4

Q: Enclose your thinking process in <think> </think> tags and your final answer in <answer> </answer>

First image

GT: D

#### Example 5 This example demonstrates the model's capability in **solid geometry(Three views)**: ```bash python example.py \ --image_paths examples/Q5_1.png \ --question "请将你的思考过程放在标签内，并将你的最终答案放在标签内。" \ --model_path sensenova/SenseNova-SI-1.5-InternVL3-8B ```
Details of Example 5

Q: Enclose your thinking process in <think> </think> tags and your final answer in <answer> </answer>

First image

GT: B

#### Example 6 This example demonstrates the model's capability in **solid geometry(Three views)**: ```bash python example.py \ --image_paths examples/Q6_1.png \ --question "请将你的思考过程放在标签内，并将你的最终答案放在标签内。" \ --model_path sensenova/SenseNova-SI-1.5-InternVL3-8B ```
Details of Example 6

Q: Enclose your thinking process in <think> </think> tags and your final answer in <answer> </answer>

First image

GT: C

#### Example 7 This example demonstrates the model's capability in **solid geometry(3D graphic reasoning)**: ```bash python example.py \ --image_paths examples/Q7_1.png \ --question "请将你的思考过程放在标签内，并将你的最终答案放在标签内。" \ --model_path sensenova/SenseNova-SI-1.5-InternVL3-8B ```
Details of Example 7

Q: Enclose your thinking process in <think> </think> tags and your final answer in <answer> </answer>

First image

GT: C

#### Example 8 This example demonstrates the model's capability in **solid geometry(Three views)**: ```bash python example.py \ --image_paths examples/Q8_1.png \ --question "请将你的思考过程放在标签内，并将你的最终答案放在标签内。" \ --model_path sensenova/SenseNova-SI-1.5-InternVL3-8B ```
Details of Example 8

Q: Enclose your thinking process in <think> </think> tags and your final answer in <answer> </answer>

First image

GT: A

### Evaluation To reproduce the benchmark results above, please refer to [EASI](https://github.com/EvolvingLMMs-Lab/EASI) to evaluate SenseNova-SI on mainstream spatial intelligence benchmarks. ## 🖊️ Citation ```bib @InProceedings{sensenova-si, title = {Scaling Spatial Intelligence with Multimodal Foundation Models}, author = {Cai, Zhongang and Wang, Ruisi and Gu, Chenyang and Pu, Fanyi and Xu, Junxiang and Wang, Yubo and Yin, Wanqi and Yang, Zhitao and Wei, Chen and Sun, Qingping and Zhou, Tongxi and Li, Jiaqi and Pang, Hui En and Qian, Oscar and Wei, Yukun and Lin, Zhiqian and Shi, Xuanke and Deng, Kewang and Han, Xiaoyang and Chen, Zukai and Fan, Xiangyu and Deng, Hanming and Lu, Lewei and Pan, Liang and Li, Bo and Liu, Ziwei and Wang, Quan and Lin, Dahua and Yang, Lei}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2026} } ```