# Phi-3 API Rate Limits, Pricing & Performance (July 2026)

- Tool: AI API Limits & Performance Matrix
- Last updated: July 2026

## TL;DR

Phi-3 is Microsoft's original small-language-model family, now superseded by Phi-4 / Phi-4-mini. The current recommended successor, Phi-4-mini, is available on Azure AI Foundry (serverless MaaS) at approximately $0.07 input / $0.23 output per million tokens — per third-party trackers as of May 2026, confirm on Azure AI Foundry pricing page. Rate limits are project-specific and not individually published; quota increases require an Azure support ticket for most Phi serverless deployments.

## Frequently asked questions

### Is Phi-3 still available?

Yes — Phi-3 open weights remain available on Hugging Face under the MIT license, and some Phi-3 variants remain in the Azure AI Foundry model catalog. However, Microsoft has moved to Phi-4 / Phi-4-mini as the current recommended generation. New projects should use Phi-4-mini rather than Phi-3.

### What is Phi-3's successor, and where can I access it?

The current recommended successor is Phi-4-mini (3.8B parameters, 128K context). Access it via Azure AI Foundry Serverless (pay-as-you-go MaaS) at approximately $0.07 input / $0.23 output per million tokens — verify current pricing on the Azure AI Foundry Models pricing page. Phi-4-mini is also available as MIT-licensed open weights on Hugging Face for self-hosting.

### How much does the Phi-3 / Phi-4-mini API cost?

Phi-4-mini on Azure AI Foundry MaaS is approximately $0.07 per million input tokens and $0.23 per million output tokens under Global Standard (cross-region) routing — per third-party trackers as of May 2026 (verify on Azure pricing page). Regional or Data Zone deployments carry a residency premium. No caching or batch discount is confirmed for Phi on Azure MaaS.

### What are the rate limits for Phi-4-mini on Azure AI Foundry?

Azure AI Foundry does not publish fixed RPM/TPM values for Phi-4-mini — limits are project-specific and displayed in your Azure portal under AI Foundry → Deployments. To increase limits, use the quota-increase request form; for most Phi serverless deployments, this may require a Microsoft support ticket with a 3–10 business day review period.

### Is Phi-3 / Phi-4-mini free to use?

Azure AI Foundry offers a free real-time deployment tier for development and evaluation — not for production. For production use, the MaaS tier charges per token (~$0.07/$0.23 for Phi-4-mini). Self-hosting is completely free: Phi-3-mini (3.8B) and Phi-4-mini run on 8GB RAM under the MIT license with no per-token fee — ideal for edge or high-volume workloads where infrastructure costs beat per-token pricing.

### Phi-3 vs Gemma 3 vs Llama 4 Scout: which small model should I use?

For Azure-native deployments: Phi-4-mini wins on native integration, MIT license, and on-device capability. For lowest per-token cost: Gemma 3 (from $0.04/MTok) is slightly cheaper. For longest context: Llama 4 Scout offers up to 1M tokens hosted. All three are competitive on parameter efficiency. If your stack is Azure-first and on-device capability matters, Phi-4-mini is the clearest choice.

### How do I increase my Phi-4-mini rate limit on Azure?

Go to Azure portal → AI Foundry → Deployments → your Phi-4-mini deployment → Request quota increase. For most Phi serverless deployments, self-serve instant increases are not available — a Microsoft support ticket is typically required, with a processing time of 3–10 business days. As a faster alternative, deploy Phi-4-mini in additional Azure regions to aggregate independent per-region quotas. RapidDev helps teams design multi-region quota strategies for Azure AI workloads — book a scoping call at rapidevelopers.com/contact.

### Can I self-host Phi-3 or Phi-4-mini?

Yes — both are released under the MIT license, which permits commercial self-hosting. Phi-3-mini (3.8B) and Phi-4-mini (3.8B) can run on a single GPU or CPU with 8GB RAM, making them viable for on-device, edge, and private-cloud deployments. Download from huggingface.co/microsoft.

### What does a 429 error mean on Azure AI Foundry for Phi?

A 429 means you have exceeded your TPM (tokens per minute) or RPM (requests per minute) quota for your Phi deployment in that Azure region. Implement exponential backoff (wait = min(60, 2**attempt) seconds + jitter), and check your Azure AI Foundry quota dashboard immediately. To prevent recurrence: request a quota increase via the portal, deploy to multiple Azure regions, or switch high-throughput workloads to Azure Managed Compute with dedicated provisioned throughput.

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Source: https://www.rapidevelopers.com/ai-api-limits-performance-matrix/phi-3
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