Using Phi-3 Mini 4k on Microsoft Foundry
Implementation guide · Phi-3 · Microsoft Research
Quick Start
- 1
- 2Use the Microsoft Foundry SDK or REST API to call
phi-3-mini-4k— see the documentation for request format. - 3
Code Examples
About Microsoft Foundry
Microsoft Foundry offers a comprehensive platform-as-a-service for enterprise AI operations. It provides multiple deployment options including Serverless APIs (pay-as-you-go), Global Standard (shared managed capacity), Provisioned Throughput Units (reserved capacity), batch processing, and bring-your-own model deployments. The platform features a unified control plane for models, agents, tools, and observability. Its Agent Service enables building and deploying AI agents with built-in tracing, monitoring, and governance. Evaluation and monitoring tools assess model performance, safety, and groundedness. Foundry supports seamless upgrades from Azure OpenAI with non-destructive migration, maintaining existing deployments while unlocking multi-provider model access and advanced platform capabilities.
Microsoft Foundry is a unified Azure platform-as-a-service offering for enterprise AI operations, model builders, and application development. It provides access to over 1,900 models from Microsoft, OpenAI, Anthropic, Mistral, xAI, Meta, DeepSeek, Hugging Face, and more. Foundry unifies agents, models, and tools under a single management grouping with built-in enterprise-readiness capabilities including tracing, monitoring, evaluations, and customizable enterprise setup configurations.
Pricing on Microsoft Foundry
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.28 |
| Output tokens | $0.84 |
Capabilities
No model capability flags are currently sourced.
About Phi-3 Mini 4k
The Phi-3 Mini-4K-Instruct model by Microsoft is an advanced, lightweight language model boasting 3.8 billion parameters, optimized for environments with limited computational resources. It excels in various natural language processing tasks, especially in reasoning, text generation, and maintaining multi-turn conversations. Trained on a mix of synthetic and high-quality data, the model is tailored for effective instruction-following. Despite its capabilities, it has limitations in factual knowledge and multilingual support, often requiring external resources to enhance accuracy. The model is ideal for commercial and research applications that demand efficient processing, such as mobile apps and real-time systems.