Azure OpenAI Pricing Calculator 2026
Estimate Azure OpenAI token costs by model, request volume, input tokens, and output tokens. Compare Azure-hosted OpenAI deployments with direct OpenAI API pricing signals from the AI Pricing Hub database.
Quick answer · Pricing data refreshed 2026-03-13 12:45:29
Azure OpenAI cost is usually driven by token mix, deployment type, and region.
For standard pay-as-you-go workloads, estimate cost from input tokens and output tokens first, then validate the exact regional meter in the Azure pricing page or Azure pricing calculator before production. Provisioned throughput, regional availability, fine-tuning, batch processing, and enterprise discounts can change the final bill.
Azure OpenAI monthly cost estimator
Use this for planning. The formula is token-based and excludes taxes, enterprise discounts, provisioned reservations, fine-tuning training, storage, networking, and Azure AI Search or app hosting costs.
Azure OpenAI model pricing signals
The table normalizes prices to USD per 1M tokens. Use the official Azure pricing page for the final regional quote and billing meter.
Azure provider rows
Azure-hosted rows available in the AI Pricing Hub database.
| Model | Provider | Input | Output | Cached input | Batch | Context |
|---|---|---|---|---|---|---|
|
GPT-3.5 Turbo (older v0613)
chat,tool_use
|
Azure | $1.00 | $2.00 | - | - | 4k |
|
GPT-4o (2024-08-06)
chat,vision,tool_use
|
Azure | $2.50 | $10.00 | - | - | 128k |
Direct OpenAI API benchmark rows
Use these as a comparison baseline when deciding between direct OpenAI and Azure OpenAI.
| Model | Provider | Input | Output | Cached input | Batch | Context |
|---|---|---|---|---|---|---|
|
GPT-4o-mini
chat,vision,tool_use
|
OpenAI | $0.15 | $0.60 | - | - | 128k |
|
GPT-4.1 Mini
chat,vision,tool_use
|
OpenAI | $0.40 | $1.60 | - | - | 1.0M |
|
o4 Mini
chat,vision,reasoning,tool_use
|
OpenAI | $1.10 | $4.40 | - | - | 200k |
|
GPT-4.1
chat,vision,tool_use
|
OpenAI | $2.00 | $8.00 | - | - | 1.0M |
|
o3
chat,vision,reasoning,tool_use
|
OpenAI | $2.00 | $8.00 | - | - | 200k |
|
GPT-4o (extended)
chat,vision,tool_use
|
OpenAI | $6.00 | $18.00 | - | - | 128k |
|
Text Embedding 3 Small
chat,tool_use
|
OpenAI | $0.02 | Free | - | - | 8k |
|
Text Embedding Ada 002
chat,tool_use
|
OpenAI | $0.10 | Free | - | - | 8k |
|
gpt-oss-20b
chat,reasoning,tool_use
|
Chutes | $0.02 | $0.10 | - | - | 131k |
|
Text Embedding 3 Large
chat,tool_use
|
OpenAI | $0.13 | Free | - | - | 8k |
|
gpt-oss-20b
chat,reasoning,tool_use
|
DeepInfra | $0.03 | $0.14 | - | - | 131k |
|
gpt-oss-120b (exacto)
chat,reasoning,tool_use
|
DeepInfra | $0.04 | $0.19 | - | - | 131k |
Lower-cost non-Azure alternatives
These are not Azure OpenAI replacements for every enterprise requirement, but they help frame the premium for Azure controls.
| Model | Provider | Input | Output | Cached input | Batch | Context |
|---|---|---|---|---|---|---|
|
GTE-Base
chat,tool_use
|
DeepInfra | $0.0050 | Free | - | - | 512 |
|
E5-Base-v2
chat,tool_use
|
DeepInfra | $0.0050 | Free | - | - | 512 |
|
paraphrase-MiniLM-L6-v2
chat,tool_use
|
DeepInfra | $0.0050 | Free | - | - | 512 |
|
all-MiniLM-L12-v2
chat,tool_use
|
DeepInfra | $0.0050 | Free | - | - | 512 |
|
bge-base-en-v1.5
chat,tool_use
|
DeepInfra | $0.0050 | Free | - | - | 512 |
|
multi-qa-mpnet-base-dot-v1
chat,tool_use
|
DeepInfra | $0.0050 | Free | - | - | 512 |
|
all-mpnet-base-v2
chat,tool_use
|
DeepInfra | $0.0050 | Free | - | - | 512 |
|
all-MiniLM-L6-v2
chat,tool_use
|
DeepInfra | $0.0050 | Free | - | - | 512 |
|
Qwen3 Embedding 8B
chat,tool_use,code
|
DeepInfra | $0.01 | Free | - | - | 32k |
|
Qwen3 Embedding 8B
chat,tool_use,code
|
Nebius | $0.01 | Free | - | - | 32k |
Azure OpenAI pricing vs direct OpenAI API
The model family can be the same, but buying through Azure changes operations, governance, and sometimes the bill.
| Decision area | Azure OpenAI | Direct OpenAI API |
|---|---|---|
| Billing | Azure meters, Azure invoice, region and deployment type matter. | OpenAI platform billing with OpenAI project and organization controls. |
| Enterprise controls | Azure identity, networking, compliance, private connectivity, and Microsoft support can be the deciding factor. | Simpler setup for teams that do not need Azure-native governance. |
| Cost planning | Token pricing plus provisioned throughput, regional availability, commitment terms, and adjacent Azure resources. | Token pricing plus platform features such as batch, cached input, image, audio, or tool-specific fees. |
| Best fit | Enterprises already standardizing on Azure, regulated workloads, and teams with committed Azure spend. | Product teams that prioritize quick model access, direct API docs, and provider-native feature rollout. |
Azure OpenAI cost examples
Use these examples to choose realistic calculator inputs before estimating your own workload.
Support chatbot
Typical inputs: 1,000-3,000 prompt tokens, 300-900 output tokens, many short requests. Cost driver is usually total token volume and context retention.
Estimate a budget chat modelDocument summarization
Typical inputs: 20,000-100,000 tokens, 500-2,000 output tokens. Input price, context window, and caching matter more than raw output price.
Browse long-context text modelsCode or agent workflow
Typical inputs: 2,000-8,000 tokens, 1,000-5,000 output tokens. Output price and retry rate can dominate monthly spend.
Compare code-capable modelsCost controls to check before scaling Azure OpenAI
- Set token budgets per feature. Split chat, extraction, search augmentation, and agent workflows into separate cost centers.
- Cap output length. Output tokens are often more expensive and easier to control with task-specific response limits.
- Reduce repeated context. Summarize conversation history, trim retrieval chunks, and use cached input where supported.
- Compare standard and provisioned deployment economics. Pay-as-you-go is flexible; provisioned throughput can be better for predictable high volume.
- Track adjacent Azure resources. AI Search, storage, logging, private networking, and app hosting can exceed model-token spend in some architectures.
- Re-check region and model availability. Azure pricing and deployment options can differ by region, model version, and capacity constraints.
Accuracy notes and official sources
This page is a planning calculator, not a quote. It normalizes local pricing rows to USD per 1M tokens so teams can compare Azure OpenAI, direct OpenAI, and alternative LLM APIs quickly. Before procurement or production migration, verify the exact Azure meter, region, model version, deployment type, and enterprise agreement in Microsoft's tools.
Authoritative references: Azure OpenAI pricing, Microsoft cost management guidance, OpenAI API pricing, and AI Pricing Hub's cheapest LLM API guide.
Azure OpenAI pricing FAQ
For standard token-based usage, multiply input tokens by the input token price and output tokens by the output token price, then divide by 1,000,000 when prices are expressed per 1M tokens. Azure region, deployment type, provisioned throughput, and adjacent Azure services can change the final bill.
Some model rates may align, but you should not assume every region, deployment type, feature, or enterprise agreement has identical economics. Use direct OpenAI rows as a benchmark and verify Azure meters in the official calculator.
Long prompts, large retrieved context, high output limits, retries, provisioned capacity that is underused, and supporting services such as AI Search or logging can raise total cost.
Consider provisioned throughput when usage is predictable, latency requirements are strict, and the committed capacity will be used consistently. For spiky or early-stage workloads, pay-as-you-go is often safer.
No. It estimates model token cost only. Add Azure AI Search, storage, compute, logging, networking, and support costs separately.
The cheapest option depends on the available model rows, region, and workload. Small models usually win for high-volume chat, while stronger models can be cheaper for complex tasks if they reduce retries.