lingshu-medical-mllm

Website    🤖 7B Model    🤖 32B Model    MedEvalKit    Technical Report    Lingshu MCP Lingshu - SOTA Multimodal Large Language Models for Medical Domain BIG NEWS: Lingshu is released with state-of-the-art performance on medical VQA tasks and report generation. This repository contains the model of the paper Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning. We also release a comprehensive medical evaluation toolkit in MedEvalKit, which supports fast evaluation of major multimodal and textual medical tasks. Highlights Lingshu models achieve SOTA on most medical multimodal/textual QA and report generation tasks for 7B and 32 model sizes. Lingshu-32B outperforms GPT-4.1 and Claude Sonnet 4 in most multimodal QA and report generation tasks. Lingshu supports more than 12 medical imaging modalities, including X-Ray, CT Scan, MRI, Microscopy, Ultrasound, Histopathology, Dermoscopy, Fundus, OCT, Digital Photography, Endoscopy, and PET. Release - Technical report: Arxiv: Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning. - Model weights: - Lingshu-7B - Lingshu-32B > Disclaimer: > We must note that even though the weights, codes, and demos are released in an open manner, similar to other pre-trained language models, and despite our best efforts in red teaming and safety fine-tuning and enforcement, our models come with potential risks, including but not limited to inaccurate, misleading or potentially harmful generation. > Developers and stakeholders should perform their own red teaming and provide related security measures before deployment, and they must abide by and comply with local governance and regulations. > In no event shall the authors be held liable for any claim, damages, or other liability arising from the use of the released weights, codes, or demos. Models MMMU-Med VQA-RAD SLAKE PathVQA PMC-VQA OmniMedVQA MedXpertQA Avg. Claude Sonnet 4 74.6 67.6 70.6 54.2 54.4 65.5 43.3 61.5 Gemini-2.5-Flash 76.9 68.5 75.8 55.4 55.4 71.0 52.8 65.1 MedVLM-R1-2B 35.2 48.6 56.0 32.5 47.6 77.7 20.4 45.4 MedGemma-4B-IT 43.7 72.5 76.4 48.8 49.9 69.8 22.3 54.8 LLaVA-Med-7B 29.3 53.7 48.0 38.8 30.5 44.3 20.3 37.8 HuatuoGPT-V-7B 47.3 67.0 67.8 48.0 53.3 74.2 21.6 54.2 BioMediX2-8B 39.8 49.2 57.7 37.0 43.5 63.3 21.8 44.6 Qwen2.5VL-7B 50.6 64.5 67.2 44.1 51.9 63.6 22.3 52.0 InternVL2.5-8B 53.5 59.4 69.0 42.1 51.3 81.3 21.7 54.0 InternVL3-8B 59.2 65.4 72.8 48.6 53.8 79.1 22.4 57.3 HealthGPT-14B 49.6 65.0 66.1 56.7 56.4 75.2 24.7 56.2 HuatuoGPT-V-34B 51.8 61.4 69.5 44.4 56.6 74.0 22.1 54.3 InternVL3-14B 63.1 66.3 72.8 48.0 54.1 78.9 23.1 58.0 Qwen2.5V-32B 59.6 71.8 71.2 41.9 54.5 68.2 25.2 56.1 InternVL2.5-38B 61.6 61.4 70.3 46.9 57.2 79.9 24.4 57.4 InternVL3-38B 65.2 65.4 72.7 51.0 56.6 79.8 25.2 59.4 Lingshu-32B 62.3 76.5 89.2 65.9 57.9 83.4 30.9 66.6 Models MMLU-Med PubMedQA MedMCQA MedQA Medbullets MedXpertQA SuperGPQA-Med Avg. Claude Sonnet 4 91.3 78.6 79.3 92.1 80.2 33.6 56.3 73.1 Gemini-2.5-Flash 84.2 73.8 73.6 91.2 77.6 35.6 53.3 69.9 MedVLM-R1-2B 51.8 66.4 39.7 42.3 33.8 11.8 19.1 37.8 MedGemma-4B-IT 66.7 72.2 52.2 56.2 45.6 12.8 21.6 46.8 LLaVA-Med-7B 50.6 26.4 39.4 42.0 34.4 9.9 16.1 31.3 HuatuoGPT-V-7B 69.3 72.8 51.2 52.9 40.9 10.1 21.9 45.6 BioMediX2-8B 68.6 75.2 52.9 58.9 45.9 13.4 25.2 48.6 Qwen2.5VL-7B 73.4 76.4 52.6 57.3 42.1 12.8 26.3 48.7 InternVL2.5-8B 74.2 76.4 52.4 53.7 42.4 11.6 26.1 48.1 InternVL3-8B 77.5 75.4 57.7 62.1 48.5 13.1 31.2 52.2 HealthGPT-14B 80.2 68.0 63.4 66.2 39.8 11.3 25.7 50.7 HuatuoGPT-V-34B 74.7 72.2 54.7 58.8 42.7 11.4 26.5 48.7 InternVL3-14B 81.7 77.2 62.0 70.1 49.5 14.1 37.9 56.1 Qwen2.5VL-32B 83.2 68.4 63.0 71.6 54.2 15.6 37.6 56.2 InternVL2.5-38B 84.6 74.2 65.9 74.4 55.0 14.7 39.9 58.4 InternVL3-38B 83.8 73.2 64.9 73.5 54.6 16.0 42.5 58.4 Lingshu-32B 84.7 77.8 66.1 74.7 65.4 22.7 41.1 61.8 ROUGE-L CIDEr RaTE SembScore RadCliQ-v1 -1 ROUGE-L CIDEr RaTE SembScore RadCliQ-v1 -1 ROUGE-L CIDEr RaTE SembScore RadCliQ-v1 -1 GPT-4.1 9.0 82.8 51.3 23.9 57.1 24.5 78.8 45.5 23.2 45.5 30.2 124.6 51.3 47.5 80.3 Claude Sonnet 4 20.0 56.6 45.6 19.7 53.4 22.0 59.5 43.5 18.9 43.3 25.4 88.3 55.4 41.0 72.1 Gemini-2.5-Flash 25.4 80.7 50.3 29.7 59.4 23.6 72.2 44.3 27.4 44.0 33.5 129.3 55.6 50.9 91.6 Med-R1-2B 19.3 35.4 40.6 14.8 42.4 18.6 37.1 38.5 17.8 37.6 16.1 38.3 41.4 12.5 43.6 MedVLM-R1-2B 20.3 40.1 41.6 14.2 48.3 20.9 43.5 38.9 15.5 40.9 22.7 61.1 46.1 22.7 54.3 MedGemma-4B-IT 25.6 81.0 52.4 29.2 62.9 27.1 79.0 47.2 29.3 46.6 30.8 103.6 57.0 46.8 86.7 LLaVA-Med-7B 15.0 43.4 12.8 18.3 52.9 18.4 45.5 38.8 23.5 44.0 18.8 68.2 40.9 16.0 58.1 HuatuoGPT-V-7B 23.4 69.5 48.9 20.0 48.2 21.3 64.7 44.2 19.3 39.4 29.6 104.3 52.9 40.7 63.6 BioMediX2-8B 20.0 52.8 44.4 17.7 53.0 18.1 47.9 40.8 21.6 43.3 19.6 58.8 40.1 11.6 53.8 Qwen2.5VL-7B 24.1 63.7 47.0 18.4 55.1 22.2 62.0 41.0 17.2 43.1 26.5 78.1 48.4 36.3 66.1 InternVL2.5-8B 23.2 61.8 47.0 21.0 56.2 20.6 58.5 43.1 19.7 42.7 24.8 75.4 51.1 36.7 67.0 InternVL3-8B 22.9 66.2 48.2 21.5 55.1 20.9 65.4 44.3 25.2 43.7 22.9 76.2 51.2 31.3 59.9 Lingshu-7B 30.8 109.4 52.1 30.0 69.2 26.5 79.0 45.4 26.8 47.3 41.2 180.7 57.6 48.4 108.1 HealthGPT-14B 21.4 64.7 48.4 16.5 52.7 20.6 66.2 44.4 22.7 42.6 22.9 81.9 50.8 16.6 56.9 HuatuoGPT-V-34B 23.5 68.5 48.5 23.0 47.1 22.5 62.8 42.9 22.1 39.7 28.2 108.3 54.4 42.2 59.3 MedDr-40B 15.7 62.3 45.2 12.2 47.0 24.1 66.1 44.7 24.2 44.7 19.4 62.9 40.3 7.3 48.9 InternVL3-14B 22.0 63.7 48.6 17.4 46.5 20.4 60.2 44.1 20.7 39.4 24.8 93.7 55.0 38.7 55.0 Qwen2.5VL-32B 15.7 50.2 47.5 17.1 45.2 15.2 54.8 43.4 18.5 40.3 18.9 73.3 51.3 38.1 54.0 InternVL2.5-38B 22.7 61.4 47.5 18.2 54.9 21.6 60.6 42.6 20.3 45.4 28.9 96.5 53.5 38.5 69.7 InternVL3-38B 22.8 64.6 47.9 18.1 47.2 20.5 62.7 43.8 20.2 39.4 25.5 90.7 53.5 33.1 55.2 Lingshu-32B 28.8 96.4 50.8 30.1 67.1 25.3 75.9 43.4 24.2 47.1 42.8 189.2 63.5 54.6 130.4 If you find our project useful, we hope you would kindly star our repo and cite our work as follows:

Website    🤖 7B Model    🤖 32B Model    MedEvalKit    Technical Report    Lingshu MCP Lingshu - SOTA Multimodal Large Language Models for Medical Domain BIG NEWS: Lingshu is released with state-of-the-art performance on medical VQA tasks and report generation. This repository contains the model of the paper Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning. We also release a comprehensive medical evaluation toolkit in MedEvalKit, which supports fast evaluation of major multimodal and textual medical tasks. Highlights Lingshu models achieve SOTA on most medical multimodal/textual QA and report generation tasks for 7B and 32 model sizes. Lingshu-32B outperforms GPT-4.1 and Claude Sonnet 4 in most multimodal QA and report generation tasks. Lingshu supports more than 12 medical imaging modalities, including X-Ray, CT Scan, MRI, Microscopy, Ultrasound, Histopathology, Dermoscopy, Fundus, OCT, Digital Photography, Endoscopy, and PET. Release - Technical report: Arxiv: Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning. - Model weights: - Lingshu-7B - Lingshu-32B > Disclaimer: > We must note that even though the weights, codes, and demos are released in an open manner, similar to other pre-trained language models, and despite our best efforts in red teaming and safety fine-tuning and enforcement, our models come with potential risks, including but not limited to inaccurate, misleading or potentially harmful generation. > Developers and stakeholders should perform their own red teaming and provide related security measures before deployment, and they must abide by and comply with local governance and regulations. > In no event shall the authors be held liable for any claim, damages, or other liability arising from the use of the released weights, codes, or demos. Models MMMU-Med VQA-RAD SLAKE PathVQA PMC-VQA OmniMedVQA MedXpertQA Avg. Claude Sonnet 4 74.6 67.6 70.6 54.2 54.4 65.5 43.3 61.5 Gemini-2.5-Flash 76.9 68.5 75.8 55.4 55.4 71.0 52.8 65.1 MedVLM-R1-2B 35.2 48.6 56.0 32.5 47.6 77.7 20.4 45.4 MedGemma-4B-IT 43.7 72.5 76.4 48.8 49.9 69.8 22.3 54.8 LLaVA-Med-7B 29.3 53.7 48.0 38.8 30.5 44.3 20.3 37.8 HuatuoGPT-V-7B 47.3 67.0 67.8 48.0 53.3 74.2 21.6 54.2 BioMediX2-8B 39.8 49.2 57.7 37.0 43.5 63.3 21.8 44.6 Qwen2.5VL-7B 50.6 64.5 67.2 44.1 51.9 63.6 22.3 52.0 InternVL2.5-8B 53.5 59.4 69.0 42.1 51.3 81.3 21.7 54.0 InternVL3-8B 59.2 65.4 72.8 48.6 53.8 79.1 22.4 57.3 HealthGPT-14B 49.6 65.0 66.1 56.7 56.4 75.2 24.7 56.2 HuatuoGPT-V-34B 51.8 61.4 69.5 44.4 56.6 74.0 22.1 54.3 InternVL3-14B 63.1 66.3 72.8 48.0 54.1 78.9 23.1 58.0 Qwen2.5V-32B 59.6 71.8 71.2 41.9 54.5 68.2 25.2 56.1 InternVL2.5-38B 61.6 61.4 70.3 46.9 57.2 79.9 24.4 57.4 InternVL3-38B 65.2 65.4 72.7 51.0 56.6 79.8 25.2 59.4 Lingshu-32B 62.3 76.5 89.2 65.9 57.9 83.4 30.9 66.6 Models MMLU-Med PubMedQA MedMCQA MedQA Medbullets MedXpertQA SuperGPQA-Med Avg. Claude Sonnet 4 91.3 78.6 79.3 92.1 80.2 33.6 56.3 73.1 Gemini-2.5-Flash 84.2 73.8 73.6 91.2 77.6 35.6 53.3 69.9 MedVLM-R1-2B 51.8 66.4 39.7 42.3 33.8 11.8 19.1 37.8 MedGemma-4B-IT 66.7 72.2 52.2 56.2 45.6 12.8 21.6 46.8 LLaVA-Med-7B 50.6 26.4 39.4 42.0 34.4 9.9 16.1 31.3 HuatuoGPT-V-7B 69.3 72.8 51.2 52.9 40.9 10.1 21.9 45.6 BioMediX2-8B 68.6 75.2 52.9 58.9 45.9 13.4 25.2 48.6 Qwen2.5VL-7B 73.4 76.4 52.6 57.3 42.1 12.8 26.3 48.7 InternVL2.5-8B 74.2 76.4 52.4 53.7 42.4 11.6 26.1 48.1 InternVL3-8B 77.5 75.4 57.7 62.1 48.5 13.1 31.2 52.2 HealthGPT-14B 80.2 68.0 63.4 66.2 39.8 11.3 25.7 50.7 HuatuoGPT-V-34B 74.7 72.2 54.7 58.8 42.7 11.4 26.5 48.7 InternVL3-14B 81.7 77.2 62.0 70.1 49.5 14.1 37.9 56.1 Qwen2.5VL-32B 83.2 68.4 63.0 71.6 54.2 15.6 37.6 56.2 InternVL2.5-38B 84.6 74.2 65.9 74.4 55.0 14.7 39.9 58.4 InternVL3-38B 83.8 73.2 64.9 73.5 54.6 16.0 42.5 58.4 Lingshu-32B 84.7 77.8 66.1 74.7 65.4 22.7 41.1 61.8 ROUGE-L CIDEr RaTE SembScore RadCliQ-v1 -1 ROUGE-L CIDEr RaTE SembScore RadCliQ-v1 -1 ROUGE-L CIDEr RaTE SembScore RadCliQ-v1 -1 GPT-4.1 9.0 82.8 51.3 23.9 57.1 24.5 78.8 45.5 23.2 45.5 30.2 124.6 51.3 47.5 80.3 Claude Sonnet 4 20.0 56.6 45.6 19.7 53.4 22.0 59.5 43.5 18.9 43.3 25.4 88.3 55.4 41.0 72.1 Gemini-2.5-Flash 25.4 80.7 50.3 29.7 59.4 23.6 72.2 44.3 27.4 44.0 33.5 129.3 55.6 50.9 91.6 Med-R1-2B 19.3 35.4 40.6 14.8 42.4 18.6 37.1 38.5 17.8 37.6 16.1 38.3 41.4 12.5 43.6 MedVLM-R1-2B 20.3 40.1 41.6 14.2 48.3 20.9 43.5 38.9 15.5 40.9 22.7 61.1 46.1 22.7 54.3 MedGemma-4B-IT 25.6 81.0 52.4 29.2 62.9 27.1 79.0 47.2 29.3 46.6 30.8 103.6 57.0 46.8 86.7 LLaVA-Med-7B 15.0 43.4 12.8 18.3 52.9 18.4 45.5 38.8 23.5 44.0 18.8 68.2 40.9 16.0 58.1 HuatuoGPT-V-7B 23.4 69.5 48.9 20.0 48.2 21.3 64.7 44.2 19.3 39.4 29.6 104.3 52.9 40.7 63.6 BioMediX2-8B 20.0 52.8 44.4 17.7 53.0 18.1 47.9 40.8 21.6 43.3 19.6 58.8 40.1 11.6 53.8 Qwen2.5VL-7B 24.1 63.7 47.0 18.4 55.1 22.2 62.0 41.0 17.2 43.1 26.5 78.1 48.4 36.3 66.1 InternVL2.5-8B 23.2 61.8 47.0 21.0 56.2 20.6 58.5 43.1 19.7 42.7 24.8 75.4 51.1 36.7 67.0 InternVL3-8B 22.9 66.2 48.2 21.5 55.1 20.9 65.4 44.3 25.2 43.7 22.9 76.2 51.2 31.3 59.9 Lingshu-7B 30.8 109.4 52.1 30.0 69.2 26.5 79.0 45.4 26.8 47.3 41.2 180.7 57.6 48.4 108.1 HealthGPT-14B 21.4 64.7 48.4 16.5 52.7 20.6 66.2 44.4 22.7 42.6 22.9 81.9 50.8 16.6 56.9 HuatuoGPT-V-34B 23.5 68.5 48.5 23.0 47.1 22.5 62.8 42.9 22.1 39.7 28.2 108.3 54.4 42.2 59.3 MedDr-40B 15.7 62.3 45.2 12.2 47.0 24.1 66.1 44.7 24.2 44.7 19.4 62.9 40.3 7.3 48.9 InternVL3-14B 22.0 63.7 48.6 17.4 46.5 20.4 60.2 44.1 20.7 39.4 24.8 93.7 55.0 38.7 55.0 Qwen2.5VL-32B 15.7 50.2 47.5 17.1 45.2 15.2 54.8 43.4 18.5 40.3 18.9 73.3 51.3 38.1 54.0 InternVL2.5-38B 22.7 61.4 47.5 18.2 54.9 21.6 60.6 42.6 20.3 45.4 28.9 96.5 53.5 38.5 69.7 InternVL3-38B 22.8 64.6 47.9 18.1 47.2 20.5 62.7 43.8 20.2 39.4 25.5 90.7 53.5 33.1 55.2 Lingshu-32B 28.8 96.4 50.8 30.1 67.1 25.3 75.9 43.4 24.2 47.1 42.8 189.2 63.5 54.6 130.4 If you find our project useful, we hope you would kindly star our repo and cite our work as follows: