Azure OpenAI Service - 2025 Models and Features

Complete knowledge base for Azure OpenAI Service with latest 2025 models including GPT-5, GPT-4.1, reasoning models, and Azure AI Foundry integration.

Overview

Azure OpenAI Service provides REST API access to OpenAI's most powerful models with enterprise-grade security, compliance, and regional availability.

Latest Models (2025)

GPT-5 Series (GA August 2025)

Registration Required Models:

• gpt-5-pro : Highest capability, complex reasoning

• gpt-5 : Balanced performance and cost

• gpt-5-codex : Optimized for code generation

No Registration Required:

• gpt-5-mini : Faster, more affordable

• gpt-5-nano : Ultra-fast for simple tasks

• gpt-5-chat : Optimized for conversational use

GPT-4.1 Series

• gpt-4.1 : 1 million token context window

• gpt-4.1-mini : Efficient version with 1M context

• gpt-4.1-nano : Fastest variant

Key Improvements:

• 1,000,000 token context (vs 128K in GPT-4 Turbo)

• Better instruction following

• Reduced hallucinations

• Improved multilingual support

Reasoning Models

o4-mini: Lightweight reasoning model

• Faster inference

• Lower cost

• Suitable for structured reasoning tasks

o3: Advanced reasoning model

• Complex problem solving

• Mathematical reasoning

• Scientific analysis

o1: Original reasoning model

• General-purpose reasoning

• Step-by-step explanations

o1-mini: Efficient reasoning

• Balanced cost and performance

Image Generation

GPT-image-1 (2025-04-15)

• DALL-E 3 successor

• Higher quality images

• Better prompt understanding

• Improved safety filters

Video Generation

Sora (2025-05-02)

• Text-to-video generation

• Realistic and imaginative scenes

• Up to 60 seconds of video

• Multiple camera angles and styles

Audio Models

gpt-4o-transcribe: Speech-to-text powered by GPT-4o

• High accuracy transcription

• Multiple languages

• Speaker diarization

gpt-4o-mini-transcribe: Faster, more affordable transcription

• Good accuracy

• Lower latency

• Cost-effective

Deploying Azure OpenAI

Create Azure OpenAI Resource

Create OpenAI account

az cognitiveservices account create
--name myopenai
--resource-group MyRG
--kind OpenAI
--sku S0
--location eastus
--custom-domain myopenai
--public-network-access Disabled
--identity-type SystemAssigned

Get endpoint and key

az cognitiveservices account show
--name myopenai
--resource-group MyRG
--query "properties.endpoint"
--output tsv

az cognitiveservices account keys list
--name myopenai
--resource-group MyRG
--query "key1"
--output tsv

Deploy GPT-5 Model

Deploy gpt-5

az cognitiveservices account deployment create
--resource-group MyRG
--name myopenai
--deployment-name gpt-5
--model-name gpt-5
--model-version latest
--model-format OpenAI
--sku-name Standard
--sku-capacity 100
--scale-type Standard

Deploy gpt-5-pro (requires registration)

az cognitiveservices account deployment create
--resource-group MyRG
--name myopenai
--deployment-name gpt-5-pro
--model-name gpt-5-pro
--model-version latest
--model-format OpenAI
--sku-name Standard
--sku-capacity 50

Deploy Reasoning Models

Deploy o3 reasoning model

az cognitiveservices account deployment create
--resource-group MyRG
--name myopenai
--deployment-name o3-reasoning
--model-name o3
--model-version latest
--model-format OpenAI
--sku-name Standard
--sku-capacity 50

Deploy o4-mini

az cognitiveservices account deployment create
--resource-group MyRG
--name myopenai
--deployment-name o4-mini
--model-name o4-mini
--model-version latest
--model-format OpenAI
--sku-name Standard
--sku-capacity 100

Deploy GPT-4.1 with 1M Context

az cognitiveservices account deployment create
--resource-group MyRG
--name myopenai
--deployment-name gpt-4-1
--model-name gpt-4.1
--model-version latest
--model-format OpenAI
--sku-name Standard
--sku-capacity 100

Deploy Image Generation Model

az cognitiveservices account deployment create
--resource-group MyRG
--name myopenai
--deployment-name image-gen
--model-name gpt-image-1
--model-version 2025-04-15
--model-format OpenAI
--sku-name Standard
--sku-capacity 10

Deploy Sora Video Generation

az cognitiveservices account deployment create
--resource-group MyRG
--name myopenai
--deployment-name sora
--model-name sora
--model-version 2025-05-02
--model-format OpenAI
--sku-name Standard
--sku-capacity 5

Using Azure OpenAI Models

Python SDK (GPT-5)

from openai import AzureOpenAI import os

Initialize client

client = AzureOpenAI( api_key=os.getenv("AZURE_OPENAI_API_KEY"), api_version="2025-02-01-preview", azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT") )

GPT-5 completion

response = client.chat.completions.create( model="gpt-5", # deployment name messages=[ {"role": "system", "content": "You are a helpful AI assistant."}, {"role": "user", "content": "Explain quantum computing in simple terms."} ], max_tokens=1000, temperature=0.7, top_p=0.95 )

print(response.choices[0].message.content)

Python SDK (o3 Reasoning Model)

o3 reasoning with chain-of-thought

response = client.chat.completions.create( model="o3-reasoning", messages=[ {"role": "system", "content": "You are an expert problem solver. Show your reasoning step-by-step."}, {"role": "user", "content": "If a train travels 120 km in 2 hours, then speeds up to travel 180 km in the next 2 hours, what is the average speed for the entire journey?"} ], max_tokens=2000, temperature=0.2 # Lower temperature for reasoning tasks )

print(response.choices[0].message.content)

Python SDK (GPT-4.1 with 1M Context)

Read a large document

with open('large_document.txt', 'r') as f: document = f.read()

GPT-4.1 can handle up to 1M tokens

response = client.chat.completions.create( model="gpt-4-1", messages=[ {"role": "system", "content": "You are a document analysis expert."}, {"role": "user", "content": f"Analyze this document and provide key insights:\n\n{document}"} ], max_tokens=4000 )

print(response.choices[0].message.content)

Image Generation (GPT-image-1)

Generate image with DALL-E 3 successor

response = client.images.generate( model="image-gen", prompt="A futuristic city with flying cars and vertical gardens, cyberpunk style, highly detailed, 4K", size="1024x1024", quality="hd", n=1 )

image_url = response.data[0].url print(f"Generated image: {image_url}")

Video Generation (Sora)

Generate video with Sora

response = client.videos.generate( model="sora", prompt="A serene lakeside at sunset with birds flying overhead and gentle waves on the shore", duration=10, # seconds resolution="1080p", fps=30 )

video_url = response.data[0].url print(f"Generated video: {video_url}")

Audio Transcription

Transcribe audio file

audio_file = open("meeting_recording.mp3", "rb")

response = client.audio.transcriptions.create( model="gpt-4o-transcribe", file=audio_file, language="en", response_format="verbose_json" )

print(f"Transcription: {response.text}") print(f"Duration: {response.duration}s")

Speaker diarization

for segment in response.segments: print(f"[{segment.start}s - {segment.end}s] {segment.text}")

Azure AI Foundry Integration

Model Router (Automatic Model Selection)

from azure.ai.foundry import ModelRouter

Initialize model router

router = ModelRouter( endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"), credential=os.getenv("AZURE_OPENAI_API_KEY") )

Automatically select optimal model

response = router.complete( prompt="Analyze this complex scientific paper...", optimization_goals=["quality", "cost"], available_models=["gpt-5", "gpt-5-mini", "gpt-4-1"] )

print(f"Selected model: {response.model_used}") print(f"Response: {response.content}") print(f"Cost: ${response.cost}")

Benefits:

• Automatic model selection based on prompt complexity

• Balance quality vs cost

• Reduce costs by up to 40% while maintaining quality

Agentic Retrieval (Azure AI Search Integration)

from azure.search.documents import SearchClient from azure.core.credentials import AzureKeyCredential

Initialize search client

search_client = SearchClient( endpoint=os.getenv("SEARCH_ENDPOINT"), index_name="documents", credential=AzureKeyCredential(os.getenv("SEARCH_KEY")) )

Agentic retrieval with Azure OpenAI

response = client.chat.completions.create( model="gpt-5", messages=[ {"role": "system", "content": "You have access to a document search system."}, {"role": "user", "content": "What are the company's revenue projections for Q3?"} ], tools=[{ "type": "function", "function": { "name": "search_documents", "description": "Search company documents", "parameters": { "type": "object", "properties": { "query": {"type": "string", "description": "Search query"} }, "required": ["query"] } } }], tool_choice="auto" )

Process tool calls

if response.choices[0].message.tool_calls: for tool_call in response.choices[0].message.tool_calls: if tool_call.function.name == "search_documents": query = json.loads(tool_call.function.arguments)["query"] results = search_client.search(query) # Feed results back to model for final answer

Improvements:

• 40% better on complex, multi-part questions

• Automatic query decomposition

• Relevance ranking

• Citation generation

Foundry Observability (Preview)

from azure.ai.foundry import FoundryObservability

Enable observability

observability = FoundryObservability( workspace_id=os.getenv("AI_FOUNDRY_WORKSPACE_ID"), enable_tracing=True, enable_metrics=True )

Monitor agent execution

with observability.trace_agent("customer_support_agent") as trace: response = client.chat.completions.create( model="gpt-5", messages=messages )

trace.log_tool_call("search_kb", {"query": "refund policy"})
trace.log_reasoning_step("Retrieved refund policy document")
trace.log_token_usage(response.usage.total_tokens)

View in Azure AI Foundry portal:

- End-to-end trace logs

- Reasoning steps and tool calls

- Performance metrics

- Cost analysis

Capacity and Quota Management

Check Quota

List deployments with usage

az cognitiveservices account deployment list
--resource-group MyRG
--name myopenai
--output table

Check usage metrics

az monitor metrics list
--resource $(az cognitiveservices account show -g MyRG -n myopenai --query id -o tsv)
--metric "TokenTransaction"
--start-time 2025-01-01T00:00:00Z
--end-time 2025-01-31T23:59:59Z
--interval PT1H
--aggregation Total

Update Capacity

Scale up deployment capacity

az cognitiveservices account deployment update
--resource-group MyRG
--name myopenai
--deployment-name gpt-5
--sku-capacity 200

Scale down during off-peak

az cognitiveservices account deployment update
--resource-group MyRG
--name myopenai
--deployment-name gpt-5
--sku-capacity 50

Request Quota Increase

• Navigate to Azure Portal → Azure OpenAI resource

• Go to "Quotas" blade

• Select model and region

• Click "Request quota increase"

• Provide justification and target capacity

Security and Networking

Private Endpoint

Create private endpoint

az network private-endpoint create
--name openai-private-endpoint
--resource-group MyRG
--vnet-name MyVNet
--subnet PrivateEndpointSubnet
--private-connection-resource-id $(az cognitiveservices account show -g MyRG -n myopenai --query id -o tsv)
--group-id account
--connection-name openai-connection

Create private DNS zone

az network private-dns zone create
--resource-group MyRG
--name privatelink.openai.azure.com

Link to VNet

az network private-dns link vnet create
--resource-group MyRG
--zone-name privatelink.openai.azure.com
--name openai-dns-link
--virtual-network MyVNet
--registration-enabled false

Create DNS zone group

az network private-endpoint dns-zone-group create
--resource-group MyRG
--endpoint-name openai-private-endpoint
--name default
--private-dns-zone privatelink.openai.azure.com
--zone-name privatelink.openai.azure.com

Managed Identity Access

Enable system-assigned identity

az cognitiveservices account identity assign
--name myopenai
--resource-group MyRG

Grant role to managed identity

PRINCIPAL_ID=$(az cognitiveservices account show -g MyRG -n myopenai --query identity.principalId -o tsv)

az role assignment create
--assignee $PRINCIPAL_ID
--role "Cognitive Services OpenAI User"
--scope /subscriptions/<sub-id>/resourceGroups/MyRG

Content Filtering

Configure content filtering

az cognitiveservices account update
--name myopenai
--resource-group MyRG
--set properties.customContentFilter='{ "hate": {"severity": "medium", "enabled": true}, "violence": {"severity": "medium", "enabled": true}, "sexual": {"severity": "medium", "enabled": true}, "selfHarm": {"severity": "high", "enabled": true} }'

Cost Optimization

Model Selection Strategy

Use GPT-5-mini or GPT-5-nano for:

• Simple questions

• Classification tasks

• Content moderation

• Summarization

Use GPT-5 or GPT-4.1 for:

• Complex reasoning

• Long-form content generation

• Document analysis

• Code generation

Use Reasoning Models (o3, o4-mini) for:

• Mathematical problems

• Scientific analysis

• Step-by-step reasoning

• Logic puzzles

Implement Caching

Use semantic cache to reduce duplicate requests

from azure.ai.cache import SemanticCache

cache = SemanticCache( similarity_threshold=0.95, ttl_seconds=3600 )

Check cache before API call

cached_response = cache.get(user_query) if cached_response: return cached_response

response = client.chat.completions.create( model="gpt-5", messages=messages )

cache.set(user_query, response)

Token Management

import tiktoken

Count tokens before API call

encoding = tiktoken.get_encoding("cl100k_base") tokens = len(encoding.encode(prompt))

if tokens > 100000: print(f"Warning: Prompt has {tokens} tokens, this will be expensive!")

Use shorter max_tokens when appropriate

response = client.chat.completions.create( model="gpt-5", messages=messages, max_tokens=500 # Limit output tokens )

Monitoring and Alerts

Set Up Cost Alerts

Create budget alert

az consumption budget create
--budget-name openai-monthly-budget
--resource-group MyRG
--amount 1000
--category Cost
--time-grain Monthly
--start-date 2025-01-01
--end-date 2025-12-31
--notifications '{ "actual_GreaterThan_80_Percent": { "enabled": true, "operator": "GreaterThan", "threshold": 80, "contactEmails": ["billing@example.com"] } }'

Application Insights Integration

from opencensus.ext.azure.log_exporter import AzureLogHandler import logging

Configure logging

logger = logging.getLogger(name) logger.addHandler(AzureLogHandler( connection_string=os.getenv("APPLICATIONINSIGHTS_CONNECTION_STRING") ))

Log API calls

logger.info("OpenAI API call", extra={ "custom_dimensions": { "model": "gpt-5", "tokens": response.usage.total_tokens, "cost": calculate_cost(response.usage.total_tokens), "latency_ms": response.response_ms } })

Best Practices

✓ Use Model Router for automatic cost optimization ✓ Implement caching to reduce duplicate requests ✓ Monitor token usage and set budgets ✓ Use private endpoints for production workloads ✓ Enable managed identity instead of API keys ✓ Configure content filtering for safety ✓ Right-size capacity based on actual demand ✓ Use Foundry Observability for monitoring ✓ Implement retry logic with exponential backoff ✓ Choose appropriate models for task complexity

References

• Azure OpenAI Documentation

• What's New in Azure OpenAI

• GPT-5 Announcement

• Azure AI Foundry

• Model Pricing

Azure OpenAI Service with GPT-5 and reasoning models brings enterprise-grade AI to your applications!