
Overview
The 14B parameter model represents the pinnacle of our medical language modeling capabilities, offering unparalleled depth in medical knowledge processing and clinical reasoning.
Building on that foundation, it introduces a dedicated reasoning mode that can follow multi-step clinical logic and justify its answers. Trained on an expanded, carefully curated corpus of medical literature and reinforced with chain-of-thought supervision, it excels at differential diagnosis, guideline-aware care planning and complex patient-note summarization. This powerful model also handles the most complex medical cases, rare conditions and sophisticated clinical analyses, demonstrating exceptional accuracy in interpreting complicated medical literature, producing detailed clinical summaries and providing comprehensive responses to intricate medical queries. While requiring more computational resources, it delivers superior performance in critical medical tasks where accuracy and depth of understanding are paramount. Its advanced RAG optimization enables sophisticated integration with extensive medical databases and research repositories. Choose this model for specialized medical institutions, research facilities, and scenarios where premium performance in complex medical tasks justifies the additional computational investment.
IMPORTANT USAGE INFORMATION:
After subscribing to this product and creating a SageMaker endpoint, billing occurs on an HOURLY BASIS for as long as the endpoint is running.
-Charges apply even if the endpoint is idle and not actively processing requests.
-To stop charges, you MUST DELETE the endpoint in your SageMaker console.
-Simply stopping requests will NOT stop billing.
This ensures you are only billed for the time you actively use the service.
Highlights
- **Real-Time Inference** * Instance Type: **ml.g5.12xlarge** * Maximum Model Length: 16K tokens Tokens per Second during real-time inference: * **Summarization**: up to 18 tokens per second * **QA**: up to 27 tokens per second
- **Batch Transform** * Instance Type: **ml.g5.12xlarge** * Maximum Model Length: 16K tokens Tokens per Second during batch transform operations: * **Summarization**: up to 34 tokens per second * **QA**: up to 207 tokens per second
- "**Accuracy** * Achieves 81.42% average score vs GPT-4s 82.85% and Med-PaLM-2s 84.08% * Clinical knowledge score of 92.36% vs Med-PaLM-2s 88.3% * Medical reasoning at 90% matches Med-PaLM-2s performance * Higher accuracy than Meditron-70B while using 5x less parameters * Suitable for deployment scenarios with compute constraints
Details
Unlock automation with AI agent solutions

Features and programs
Financing for AWS Marketplace purchases
Pricing
Free trial
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.g5.12xlarge Inference (Batch) Recommended | Model inference on the ml.g5.12xlarge instance type, batch mode | $9.98 |
ml.g5.12xlarge Inference (Real-Time) Recommended | Model inference on the ml.g5.12xlarge instance type, real-time mode | $9.98 |
Vendor refund policy
No refunds are possible.
How can we make this page better?
Legal
Vendor terms and conditions
Content disclaimer
Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Version release notes
OpenAI compatible.
Additional details
Inputs
- Summary
Input Format
1. Chat Completion Example Payload { "model": "/opt/ml/model", "messages": [ {"role": "system", "content": "You are a helpful medical assistant."}, {"role": "user", "content": "What should I do if I have a fever and body aches?"} ], "max_tokens": 1024, "temperature": 0.7 } For additional parameters: * ChatCompletionRequest * OpenAI's Chat APIÂ
2. Text Completion
Single Prompt Example { "model": "/opt/ml/model", "prompt": "How can I maintain good kidney health?", "max_tokens": 512, "temperature": 0.6 }
Multiple Prompts Example { "model": "/opt/ml/model", "prompt": [ "How can I maintain good kidney health?", "What are the best practices for kidney care?" ], "max_tokens": 512, "temperature": 0.6 } Reference: * CompletionRequest * OpenAI's Completions APIÂ
Important Notes:
- Streaming Responses: Add "stream": true to your request payload to enable streaming
- Model Path Requirement: Always set "model": "/opt/ml/model" (SageMaker's fixed model location)
- Input MIME type
- application/json
Resources
Support
Vendor support
For any assistance, please reach out to support@johnsnowlabs.com .
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Similar products


