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Microsoft Azure: Models Intelligence, Performance & Price

Analysis of Microsoft Azure's models across key metrics including quality, price, output speed, latency, context window & more. This analysis is intended to support you in choosing the best model provided by Microsoft Azure for your use-case.

Microsoft Azure

Most Intelligent

#1
Claude Opus 4.6 (max)Claude Opus 4.6 (max)
53
#2
Claude Sonnet 4.6 (max)Claude Sonnet 4.6 (max)
52
#3
GPT-5.2 (xhigh)GPT-5.2 (xhigh)
51
#4
Claude Opus 4.5Claude Opus 4.5
50
#5
GPT-5.2 Codex (xhigh)GPT-5.2 Codex (xhigh)
49

Intelligence index

Total 66 models

Fastest

#1
gpt-oss-120B (low)gpt-oss-120B (low)
296 t/s
#2
gpt-oss-120B (high)gpt-oss-120B (high)
289 t/s
#3
GPT-4.1 nanoGPT-4.1 nano
280 t/s
#4
Grok 4 FastGrok 4 Fast
220 t/s
#5
Llama 3.1 8BLlama 3.1 8B
207 t/s

Output speed

Total 66 models

Lowest Price

#1
GPT-5 nano (high)GPT-5 nano (high)
$0.14
#2
GPT-5 nano (medium)GPT-5 nano (medium)
$0.14
#3
GPT-4.1 nanoGPT-4.1 nano
$0.17
#4
Phi-4Phi-4
$0.22
#5
gpt-oss-120B (high)gpt-oss-120B (high)
$0.26

Blended price (per 1M tokens)

Total 66 models

Azure offers 66 models, each with different intelligence, performance, and pricing characteristics. Below is a comparison of the key metrics across models.

  • For intelligence, the top models on Azure are Claude Opus 4.6 (max) (53), Claude Sonnet 4.6 (max) (52), GPT-5.2 (xhigh) (51).
  • For output speed, the fastest models are gpt-oss-120B (low) (296 t/s), gpt-oss-120B (high) (289 t/s), GPT-4.1 nano (280 t/s).
  • For latency, Llama 3.1 8B (0.56s), Grok 3 mini Reasoning (high) (0.59s), gpt-oss-120B (low) (0.65s) offer the lowest time to first token.
  • For pricing, GPT-5 nano (high) ($0.14), GPT-5 nano (medium) ($0.14), GPT-4.1 nano ($0.17) offer the lowest blended prices per 1M tokens.
  • For context window size, Grok 4 Fast (2m), Grok 4 Fast (2m), GPT-4.1 (1m) support the largest context windows on Azure.
Intelligence
Artificial Analysis Intelligence Index; Higher is better
Speed
Output Tokens per Second; Higher is better
Price
USD per 1M Tokens; Lower is better

Navigation

Intelligence EvaluationsContext WindowJSON Mode & Function CallingPricingPerformance SummarySpeedLatencyEnd-to-End Response Time

Intelligence Evaluations

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index; Higher is better

Artificial Analysis Intelligence Index v4.0 includes: GDPval-AA, 𝜏²-Bench Telecom, Terminal-Bench Hard, SciCode, AA-LCR, AA-Omniscience, IFBench, Humanity's Last Exam, GPQA Diamond, CritPt. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).

Intelligence Evaluations

Intelligence evaluations measured independently by Artificial Analysis; Higher is better
Results claimed by AI Lab (not yet independently verified)
GDPval-AA (Agentic Real-World Work Tasks, (ELO-500)/2000)
Terminal-Bench Hard (Agentic Coding & Terminal Use)
𝜏²-Bench Telecom (Agentic Tool Use)
AA-LCR (Long Context Reasoning)
AA-Omniscience Accuracy (Knowledge)
AA-Omniscience Non-Hallucination Rate (1 - Hallucination Rate)
Humanity's Last Exam (Reasoning & Knowledge)
GPQA Diamond (Scientific Reasoning)
SciCode (Coding)
IFBench (Instruction Following)
CritPt (Physics Reasoning)
MMMU Pro (Visual Reasoning)

Artificial Analysis Intelligence Index v4.0 includes: GDPval-AA, 𝜏²-Bench Telecom, Terminal-Bench Hard, SciCode, AA-LCR, AA-Omniscience, IFBench, Humanity's Last Exam, GPQA Diamond, CritPt. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Intelligence vs. Price

Artificial Analysis Intelligence Index; Price: USD per 1M Tokens
Most attractive quadrant
Azure

Artificial Analysis Intelligence Index v4.0 includes: GDPval-AA, 𝜏²-Bench Telecom, Terminal-Bench Hard, SciCode, AA-LCR, AA-Omniscience, IFBench, Humanity's Last Exam, GPQA Diamond, CritPt. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Price per token, represented as USD per million Tokens. Price is a blend of Input & Output token prices (3:1 ratio).

Context Window

Context Window

Context Window: Tokens Limit; Higher is better

Maximum number of combined input & output tokens. Output tokens commonly have a significantly lower limit (varied by model).

JSON Mode & Function Calling

Function (Tool) Calling & JSON Mode

ModelsFunction callingJSON Mode
Claude Opus 4.6 (max), Azure logoClaude Opus 4.6 (max), Azure
Claude Sonnet 4.6 (max), Azure logoClaude Sonnet 4.6 (max), Azure
GPT-5.2 (xhigh), Azure logoGPT-5.2 (xhigh), Azure
Claude Opus 4.5, Azure logoClaude Opus 4.5, Azure
GPT-5.2 Codex (xhigh), Azure logoGPT-5.2 Codex (xhigh), Azure
GPT-5.1 (high), Azure logoGPT-5.1 (high), Azure
Claude Opus 4.6, Azure logoClaude Opus 4.6, Azure
GPT-5 (high), Azure logoGPT-5 (high), Azure
GPT-5 Codex (high), Azure logoGPT-5 Codex (high), Azure
Claude Sonnet 4.6, Azure logoClaude Sonnet 4.6, Azure
GPT-5.1 Codex (high), Azure logoGPT-5.1 Codex (high), Azure
Claude Opus 4.5, Azure logoClaude Opus 4.5, Azure

Indicates whether the provider supports function calling in their API. Function calling is also known as 'Tool Calling'.

Indicates whether the provider supports JSON mode in their API. When JSON mode is enabled, the models will always return a valid JSON object.

Pricing

Intelligence vs. Price

Artificial Analysis Intelligence Index; Price: USD per 1M Tokens
Most attractive quadrant
Azure

Artificial Analysis Intelligence Index v4.0 includes: GDPval-AA, 𝜏²-Bench Telecom, Terminal-Bench Hard, SciCode, AA-LCR, AA-Omniscience, IFBench, Humanity's Last Exam, GPQA Diamond, CritPt. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Price per token, represented as USD per million Tokens. Price is a blend of Input & Output token prices (3:1 ratio).

Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).

Performance Summary

Output Speed vs. Price

Output Speed: Output Tokens per Second; Price: USD per 1M Tokens; 10,000 Input Tokens
Most attractive quadrant
Azure

Tokens per second received while the model is generating tokens (ie. after first chunk has been received from the API for models which support streaming).

Price per token, represented as USD per million Tokens. Price is a blend of Input & Output token prices (3:1 ratio).

Speed

Measured by Output Speed (tokens per second)

Output Speed

Output Tokens per Second; Higher is better; 10,000 Input Tokens

Tokens per second received while the model is generating tokens (ie. after first chunk has been received from the API for models which support streaming).

Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).

Latency

Measured by Time (seconds) to First Token

Time to First Answer Token

Seconds to First Token Received; Lower is better
Input processing time
'Thinking' time (reasoning models, where applicable)

Time to first answer token received, in seconds, after API request sent. For reasoning models, this includes the 'thinking' time of the model before providing an answer. For models which do not support streaming, this represents time to receive the completion.

Seconds to receive a 500 token response. Key components:

  • Input time: Time to receive the first response token
  • Thinking time (only for reasoning models): Time reasoning models spend outputting tokens to reason prior to providing an answer. Amount of tokens based on the average reasoning tokens across a diverse set of 60 prompts (methodology details).
  • Answer time: Time to generate 500 output tokens, based on output speed

Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).

End-to-End Response Time

Seconds to output 500 Tokens, calculated based on time to first token, 'thinking' time for reasoning models, and output speed

End-to-End Response Time vs. Price

End-to-End Response Time: End-to-End Seconds to Output 500 Tokens; Price: USD per 1M Tokens
Most attractive quadrant
Azure

Seconds to receive a 500 token response considering input processing time, 'thinking' time of reasoning models, and output speed.

Price per token, represented as USD per million Tokens. Price is a blend of Input & Output token prices (3:1 ratio).

Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).

Key definitions

Maximum number of combined input & output tokens. Output tokens commonly have a significantly lower limit (varied by model).

Tokens per second received while the model is generating tokens (ie. after first chunk has been received from the API for models which support streaming).

Time to first token received, in seconds, after API request sent. For reasoning models which share reasoning tokens, this will be the first reasoning token. For models which do not support streaming, this represents time to receive the completion.

Price per token, represented as USD per million Tokens. Price is a blend of Input & Output token prices (3:1 ratio).

Price per token generated by the model (received from the API), represented as USD per million Tokens.

Price per token included in the request/message sent to the API, represented as USD per million Tokens.

Metrics are 'live' and are based on the past 72 hours of measurements, measurements are taken 8 times a day for single requests and 2 times per day for parallel requests.

Frequently Asked Questions

Common questions about Microsoft Azure

Microsoft Azure offers 66 models that we track: Claude Opus 4.6 (max), Claude Sonnet 4.6 (max), GPT-5.2 (xhigh), Claude Opus 4.5, GPT-5.2 Codex (xhigh), GPT-5.1 (high), Claude Opus 4.6, GPT-5 (high), GPT-5 Codex (high), Claude Sonnet 4.6, GPT-5.1 Codex (high), Claude Opus 4.5, Claude 4.5 Sonnet, GPT-5 (medium), Grok 4, GPT-5 mini (high), Kimi K2 Thinking, GPT-5 (low), GPT-5 mini (medium), GPT-5.1 Codex mini (high), o3, Claude 4.5 Sonnet, Claude 4.5 Haiku, Grok 4 Fast, GPT-5.2, gpt-oss-120B (high), o4-mini (high), DeepSeek V3.2, Grok 3 mini Reasoning (high), Claude 4.5 Haiku, o1, GPT-5.1, DeepSeek R1 0528, GPT-5 nano (high), GPT-4.1, GPT-5 nano (medium), o3-mini (high), Grok 3, gpt-oss-120B (low), GPT-5 (minimal), Grok 4 Fast, GPT-4.1 mini, Mistral Large 3, DeepSeek V3 0324, DeepSeek R1 (Jan), Mistral Medium 3, GPT-4o (Aug), Llama 4 Maverick, Llama 3.1 405B, GPT-4o (Nov), Llama 3.3 70B, Llama 4 Scout, Command A, GPT-4.1 nano, Llama 3.1 8B, Phi-4, Phi-4 Mini, Phi-4 Multimodal, o1-preview, GPT-4o (May), GPT-4 Turbo, GPT-4o mini, o3-pro, o3-mini, Claude 4.1 Opus, and Claude 4.1 Opus.

The most intelligent model available on Microsoft Azure is Claude Opus 4.6 (max) with an Intelligence Index score of 53.

The fastest model on Microsoft Azure by output speed is gpt-oss-120B (low) at 296.3 tokens per second.

The model with the lowest time to first token on Microsoft Azure is Llama 3.1 8B at 0.56s. Lower latency means faster initial response time.

The most affordable model on Microsoft Azure by blended price is GPT-5 nano (high) at $0.14 per 1M tokens (3:1 input to output ratio).

Prices on Microsoft Azure vary up to 255x across models, from $0.14 per 1M tokens for GPT-5 nano (high) to $35.00 per 1M tokens for o3-pro.

Yes, Microsoft Azure offers an OpenAI-compatible API, making it easy to switch from OpenAI or use existing OpenAI SDK integrations.

37 of 66 models on Microsoft Azure support JSON mode for structured output.

55 of 66 models on Microsoft Azure support function calling (tool use).

Yes, Microsoft Azure offers 34 reasoning models: Claude Opus 4.6 (max), Claude Sonnet 4.6 (max), GPT-5.2 (xhigh), Claude Opus 4.5, GPT-5.2 Codex (xhigh), GPT-5.1 (high), GPT-5 (high), GPT-5 Codex (high), GPT-5.1 Codex (high), Claude 4.5 Sonnet, GPT-5 (medium), Grok 4, GPT-5 mini (high), Kimi K2 Thinking, GPT-5 (low), GPT-5 mini (medium), GPT-5.1 Codex mini (high), o3, Claude 4.5 Haiku, Grok 4 Fast, gpt-oss-120B (high), o4-mini (high), Grok 3 mini Reasoning (high), o1, DeepSeek R1 0528, GPT-5 nano (high), GPT-5 nano (medium), o3-mini (high), gpt-oss-120B (low), DeepSeek R1 (Jan), o1-preview, o3-pro, o3-mini, and Claude 4.1 Opus. Reasoning models use extended thinking to work through complex problems before providing an answer.

Yes, 17 of 66 models on Microsoft Azure are open weight models: Kimi K2 Thinking, gpt-oss-120B (high), DeepSeek V3.2, DeepSeek R1 0528, gpt-oss-120B (low), Mistral Large 3, DeepSeek V3 0324, DeepSeek R1 (Jan), Llama 4 Maverick, Llama 3.1 405B, Llama 3.3 70B, Llama 4 Scout, Command A, Llama 3.1 8B, Phi-4, Phi-4 Mini, and Phi-4 Multimodal.

Yes, provider performance can vary over time due to infrastructure changes, load balancing, and updates. We continuously benchmark all providers and display historical performance trends in the "Over Time" charts.

When choosing a model on Microsoft Azure, consider: intelligence (for quality-sensitive tasks), output speed (for throughput-intensive tasks), latency (for interactive applications requiring quick first responses), pricing (for cost-sensitive workloads), and features like context window size, JSON mode, or function calling support.