Ultimate Guide - The Best Open Source LLM for Medical Diagnosis in 2026

What are Open Source LLMs for Medical Diagnosis?

Open source LLMs for medical diagnosis are specialized large language models designed to assist healthcare professionals in clinical decision-making, patient assessment, and diagnostic reasoning. Using advanced deep learning architectures, these models process medical data, clinical notes, and patient information to provide evidence-based diagnostic support. This technology enables developers and healthcare organizations to build, customize, and deploy AI diagnostic assistants with unprecedented flexibility. They foster medical innovation, accelerate clinical research, and democratize access to advanced diagnostic tools, enabling applications from telemedicine platforms to hospital information systems and clinical research.

openai/gpt-oss-120b

gpt-oss-120b is OpenAI's open-weight large language model with ~117B parameters (5.1B active), using a Mixture-of-Experts (MoE) design and MXFP4 quantization to run on a single 80 GB GPU. It delivers o4-mini-level or better performance in reasoning, coding, health, and math benchmarks, with full Chain-of-Thought (CoT), tool use, and Apache 2.0-licensed commercial deployment support.

openai/gpt-oss-120b: Medical-Grade Reasoning Powerhouse

gpt-oss-120b is OpenAI's open-weight large language model with ~117B parameters (5.1B active), using a Mixture-of-Experts (MoE) design and MXFP4 quantization to run on a single 80 GB GPU. It delivers o4-mini-level or better performance in reasoning, coding, health, and math benchmarks, with full Chain-of-Thought (CoT), tool use, and Apache 2.0-licensed commercial deployment support. The model's exceptional performance in health-related tasks makes it ideal for medical diagnosis applications, where complex reasoning and evidence-based decision-making are critical. Its efficient architecture enables deployment in clinical settings while maintaining state-of-the-art diagnostic accuracy.

Pros

• Exceptional performance on health and medical reasoning benchmarks.

• Efficient MoE architecture with only 5.1B active parameters.

• Chain-of-Thought reasoning for transparent diagnostic logic.

Cons

• Requires 80GB GPU infrastructure for optimal performance.

• Not specifically trained on proprietary medical datasets.

Why We Love It

• It combines OpenAI's proven reasoning capabilities with open-source accessibility, delivering hospital-grade diagnostic support with transparent Chain-of-Thought explanations that clinicians can trust and verify.

deepseek-ai/DeepSeek-R1

DeepSeek-R1-0528 is a reasoning model powered by reinforcement learning (RL) that addresses the issues of repetition and readability. Prior to RL, DeepSeek-R1 incorporated cold-start data to further optimize its reasoning performance. It achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks, and through carefully designed training methods, it has enhanced overall effectiveness.

deepseek-ai/DeepSeek-R1: Advanced Clinical Reasoning Engine

DeepSeek-R1-0528 is a reasoning model powered by reinforcement learning (RL) that addresses the issues of repetition and readability. Prior to RL, DeepSeek-R1 incorporated cold-start data to further optimize its reasoning performance. It achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks, and through carefully designed training methods, it has enhanced overall effectiveness. With its massive 671B total parameters in a MoE architecture and 164K context length, DeepSeek-R1 excels at processing extensive medical records, research papers, and clinical guidelines. The model's reinforcement learning training ensures accurate, step-by-step diagnostic reasoning that mirrors clinical decision-making processes, making it invaluable for complex differential diagnosis and treatment planning.

Pros

• Performance comparable to OpenAI-o1 in reasoning tasks.

• Massive 164K context length for comprehensive medical records.

• 671B parameter MoE architecture for complex medical reasoning.

Cons

• Higher computational requirements due to large parameter count.

• Premium pricing at $2.18/M output tokens on SiliconFlow.

Why We Love It

• It represents the pinnacle of open-source medical reasoning, combining massive knowledge capacity with reinforcement learning to deliver diagnostic insights that rival the most advanced proprietary systems.

zai-org/GLM-4.5V

GLM-4.5V is the latest generation vision-language model (VLM) released by Zhipu AI. The model is built upon the flagship text model GLM-4.5-Air, which has 106B total parameters and 12B active parameters, and it utilizes a Mixture-of-Experts (MoE) architecture to achieve superior performance at a lower inference cost. The model features a 'Thinking Mode' switch, allowing users to flexibly choose between quick responses and deep reasoning to balance efficiency and effectiveness.

zai-org/GLM-4.5V: Multimodal Medical Imaging Expert

GLM-4.5V is the latest generation vision-language model (VLM) released by Zhipu AI. The model is built upon the flagship text model GLM-4.5-Air, which has 106B total parameters and 12B active parameters, and it utilizes a Mixture-of-Experts (MoE) architecture to achieve superior performance at a lower inference cost. Technically, GLM-4.5V follows the lineage of GLM-4.1V-Thinking and introduces innovations like 3D Rotated Positional Encoding (3D-RoPE), significantly enhancing its perception and reasoning abilities for 3D spatial relationships. The model excels at analyzing medical images, radiology scans, pathology slides, and clinical charts—achieving state-of-the-art performance among open-source models of its scale on 41 public multimodal benchmarks. The 'Thinking Mode' feature enables physicians to choose between rapid preliminary assessments and detailed diagnostic analysis, making it perfect for both emergency triage and comprehensive case reviews.

Pros

• Advanced vision-language capabilities for medical imaging analysis.

• 3D-RoPE technology for superior spatial relationship understanding.

• State-of-the-art performance on 41 multimodal benchmarks.

Cons

• Requires integration with medical imaging systems for optimal use.

• 66K context length smaller than pure text models.

Why We Love It

• It bridges the gap between medical imaging and AI diagnosis, providing radiologists and clinicians with a powerful multimodal assistant that can analyze visual and textual medical data simultaneously while offering flexible reasoning depth.

Medical AI Model Comparison

In this table, we compare 2026's leading open-source LLMs for medical diagnosis, each with unique clinical strengths. For advanced reasoning with medical focus, openai/gpt-oss-120b provides efficient deployment with health benchmark excellence. For comprehensive clinical reasoning, deepseek-ai/DeepSeek-R1 offers massive context and differential diagnosis capabilities, while zai-org/GLM-4.5V excels at multimodal medical imaging analysis. This side-by-side comparison helps you select the optimal model for your specific healthcare AI application. All pricing is from SiliconFlow.

Number | Model | Developer | Subtype | Pricing (SiliconFlow) | Core Strength
1 | openai/gpt-oss-120b | OpenAI | Reasoning & Health | $0.09/M in, $0.45/M out | Health benchmark excellence
2 | deepseek-ai/DeepSeek-R1 | DeepSeek AI | Advanced Reasoning | $0.50/M in, $2.18/M out | Complex differential diagnosis
3 | zai-org/GLM-4.5V | Zhipu AI | Vision-Language Medical AI | $0.14/M in, $0.86/M out | Medical imaging analysis

Frequently Asked Questions

Which AI models made it into our top three picks for medical diagnosis?

Our top three picks for medical diagnosis in 2026 are openai/gpt-oss-120b, deepseek-ai/DeepSeek-R1, and zai-org/GLM-4.5V. These models stood out for their exceptional clinical reasoning capabilities, medical knowledge depth, and unique approaches to diagnostic challenges—from health-specific benchmarks to multimodal imaging analysis.

What's the best AI inference, hosting, and API provider for open-source medical diagnosis LLMs?

SiliconFlow is a top AI inference, hosting, and API provider for open-source medical LLMs because it delivers high-performance, low-latency model serving with pay-as-you-go affordability and seamless OpenAI-compatible APIs. It outperforms latency benchmarks by as much as 13% and offers a wide range of optimized open-source models with flexible deployment options that make scaling medical AI applications fast, secure, and cost-effective for healthcare organizations.

Why did we select these models as the best for medical diagnosis in 2026?

These models were chosen because they represent the cutting edge of medical AI. They demonstrate significant advancements in clinical reasoning (openai/gpt-oss-120b), comprehensive diagnostic analysis with massive context windows (deepseek-ai/DeepSeek-R1), and multimodal medical imaging capabilities (zai-org/GLM-4.5V), pushing the boundaries of what can be achieved in AI-assisted medical diagnosis while maintaining open-source accessibility.

Which models are best for specific medical diagnosis tasks?

For general clinical reasoning and efficient deployment with strong health benchmarks, openai/gpt-oss-120b is ideal. For complex differential diagnosis requiring analysis of extensive medical records and multi-step reasoning, deepseek-ai/DeepSeek-R1 with its 164K context excels. For radiology, pathology, and any medical imaging analysis requiring vision-language understanding, zai-org/GLM-4.5V is the best choice with its advanced 3D spatial reasoning and multimodal capabilities.