Microsoft Azure: Models Intelligence, Performance & Price

Microsoft Azure
Microsoft Azure

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.

Most Intelligent

#1
Claude Opus 4.7 (max)
Claude Opus 4.7 (max)
57
#2
GPT-5.4 (xhigh)
GPT-5.4 (xhigh)
57
#3
Kimi K2.6
Kimi K2.6
54
#4
Grok 4.3 (high)
Grok 4.3 (high)
53
#5
Claude Opus 4.6 (max)
Claude Opus 4.6 (max)
53

Intelligence index

Total 79 models

Fastest

#1
gpt-oss-120b (high)
gpt-oss-120b (high)
353 t/s
#2
gpt-oss-120b (low)
gpt-oss-120b (low)
344 t/s
#3
DeepSeek V3.2
DeepSeek V3.2
236 t/s
#4
Grok 4.20 0309 v2
Grok 4.20 0309 v2
231 t/s
#5
GPT-4.1 nano
GPT-4.1 nano
217 t/s

Output speed

Total 79 models

Lowest Price

#1
GPT-5 nano (high)
GPT-5 nano (high)
$0.06
#2
GPT-5 nano (medium)
GPT-5 nano (medium)
$0.06
#3
GPT-4.1 nano
GPT-4.1 nano
$0.08
#4
GPT-4o mini
GPT-4o mini
$0.14
#5
Phi-4
Phi-4
$0.16

Blended price (per 1M tokens)

Total 79 models

Indicates a reasoning model

Azure offers 79 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 4.7 (max) (57), GPT-5.4 (xhigh) (57), Kimi K2.6 (54).
  • For output speed, the fastest models are gpt-oss-120b (high) (353 t/s), gpt-oss-120b (low) (344 t/s), DeepSeek V3.2 (236 t/s). Speed varies significantly across models, with a 63% difference between the fastest and slowest.
  • For latency, Llama 3.1 8B (0.62s), gpt-oss-120b (low) (0.67s), gpt-oss-120b (high) (0.69s) offer the lowest time to first token.
  • For pricing, GPT-5 nano (high) ($0.06), GPT-5 nano (medium) ($0.06), GPT-4.1 nano ($0.08) offer the lowest blended prices per 1M tokens. Prices vary up to 2.9x across models.
  • For context window size, GPT-5.4 (xhigh) (1m), Claude Opus 4.7 (max) (1m), Claude Opus 4.6 (max) (1m) support the largest context windows on Azure.

Highlights

Intelligence

Artificial Analysis Intelligence Index · Higher is better

Speed

Output tokens per second · Higher is better
Updated

Price

USD per 1M tokens (blended) · Lower is better

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

APEX-Agents-AA

Long-horizon agentic tasks

ITBench-AA

Kubernetes incident root-cause analysis

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

Blended at 7:2:1 (cache-input-output) · Artificial Analysis Intelligence Index · USD per 1M tokens (blended)
Most attractive quadrant
Anthropic
DeepSeek
Kimi
OpenAI
xAI

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, shown in USD per million tokens. Price is a blend of cache hit, input, and output token prices using the selected ratio (default 7:2:1 cache-input-output).

The blended cache price shown here uses cache hit price only. Other caching costs differ by provider:

  • Anthropic: charges a separate cache write fee, with different rates for 5-minute and 1-hour TTLs (1-hour TTL is more expensive). Blended price charts use Anthropic cache write price for the input leg.
  • Google (Vertex/Gemini): charges a per-hour cache storage fee in addition to cache hit pricing. Some providers also use tiered pricing for prompts above 200K tokens.
  • OpenAI, DeepSeek, others: typically charge only cache hit pricing with no write or storage fee.

See Prompt Caching for the full breakdown.

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).

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 4.7 (max), Azure logoClaude Opus 4.7 (max), Azure
GPT-5.4 (xhigh), Azure logoGPT-5.4 (xhigh), Azure
Kimi K2.6, Azure logoKimi K2.6, Azure
Grok 4.3 (high), Azure logoGrok 4.3 (high), Azure
Claude Opus 4.6 (max), Azure logoClaude Opus 4.6 (max), Azure
Claude Sonnet 4.6 (max), Azure logoClaude Sonnet 4.6 (max), Azure
DeepSeek V4 Pro (Max), Azure logoDeepSeek V4 Pro (Max), Azure
GPT-5.2 (xhigh), Azure logoGPT-5.2 (xhigh), Azure
DeepSeek V4 Pro (High), Azure logoDeepSeek V4 Pro (High), Azure
Claude Opus 4.5, Azure logoClaude Opus 4.5, Azure
Grok 4.20 0309 v2, Azure logoGrok 4.20 0309 v2, Azure
GPT-5.2 Codex (xhigh), Azure logoGPT-5.2 Codex (xhigh), 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.

PricingUpdated

Intelligence vs. Price

Blended at 7:2:1 (cache-input-output) · Artificial Analysis Intelligence Index · USD per 1M tokens (blended)
Most attractive quadrant
Anthropic
DeepSeek
Kimi
OpenAI
xAI

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, shown in USD per million tokens. Price is a blend of cache hit, input, and output token prices using the selected ratio (default 7:2:1 cache-input-output).

The blended cache price shown here uses cache hit price only. Other caching costs differ by provider:

  • Anthropic: charges a separate cache write fee, with different rates for 5-minute and 1-hour TTLs (1-hour TTL is more expensive). Blended price charts use Anthropic cache write price for the input leg.
  • Google (Vertex/Gemini): charges a per-hour cache storage fee in addition to cache hit pricing. Some providers also use tiered pricing for prompts above 200K tokens.
  • OpenAI, DeepSeek, others: typically charge only cache hit pricing with no write or storage fee.

See Prompt Caching for the full breakdown.

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 · USD per 1M tokens (blended) · 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, shown in USD per million tokens. Price is a blend of cache hit, input, and output token prices using the selected ratio (default 7:2:1 cache-input-output).

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

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 · USD per 1M tokens (blended)
Most attractive quadrant
Azure

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

Price per token, shown in USD per million tokens. Price is a blend of cache hit, input, and output token prices using the selected ratio (default 7:2:1 cache-input-output).

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, shown in USD per million tokens. Price is a blend of cache hit, input, and output token prices using the selected ratio (default 7:2:1 cache-input-output).

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 79 models that we track: Claude Opus 4.7 (max), GPT-5.4 (xhigh), Kimi K2.6, Grok 4.3 (high), Claude Opus 4.6 (max), Claude Sonnet 4.6 (max), DeepSeek V4 Pro (Max), GPT-5.2 (xhigh), DeepSeek V4 Pro (High), Claude Opus 4.5, Grok 4.20 0309 v2, GPT-5.2 Codex (xhigh), GPT-5.4 mini (xhigh), Grok 4.3 (medium), GPT-5.1 (high), Kimi K2.5, GPT-5.2 (medium), Claude Opus 4.6 (high), GPT-5 Codex (high), GPT-5 (high), Claude Sonnet 4.6 (Non-reasoning), Grok 4.3 (low), GPT-5.1 Codex (high), Claude Opus 4.5, Claude 4.5 Sonnet, Kimi K2.6, GPT-5 (medium), Grok 4, GPT-5 mini (high), Kimi K2 Thinking, DeepSeek V4 Pro, GPT-5 (low), GPT-5 mini (medium), GPT-5.1 Codex mini (high), o3, GPT-5.4 mini (medium), Kimi K2.5, Claude 4.5 Sonnet, Claude 4.5 Haiku, GPT-5.2, gpt-oss-120b (high), o4-mini (high), DeepSeek V3.2, 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), gpt-oss-120b (low), GPT-5 (minimal), GPT-5.4 mini, 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-mini, o3-pro, Claude 4.1 Opus, and Claude 4.1 Opus.

The most intelligent model available on Microsoft Azure is Claude Opus 4.7 (max) with an Intelligence Index score of 57.

The fastest model on Microsoft Azure by output speed is gpt-oss-120b (high) at 352.7 tokens per second.

The model with the lowest time to first token on Microsoft Azure is Llama 3.1 8B at 0.62s. Lower latency means faster initial response time.

The most affordable model on Microsoft Azure by blended price is GPT-5 nano (high) at $0.06 per 1M tokens (7:2:1 cache hit/input/output ratio).

Prices on Microsoft Azure vary up to 456x across models, from $0.06 per 1M tokens for GPT-5 nano (high) to $26.00 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.

66 of 79 models on Microsoft Azure support JSON mode for structured output.

72 of 79 models on Microsoft Azure support function calling (tool use).

Yes, Microsoft Azure offers 45 reasoning models: Claude Opus 4.7 (max), GPT-5.4 (xhigh), Kimi K2.6, Grok 4.3 (high), Claude Opus 4.6 (max), Claude Sonnet 4.6 (max), DeepSeek V4 Pro (Max), GPT-5.2 (xhigh), DeepSeek V4 Pro (High), Claude Opus 4.5, Grok 4.20 0309 v2, GPT-5.2 Codex (xhigh), GPT-5.4 mini (xhigh), Grok 4.3 (medium), GPT-5.1 (high), Kimi K2.5, GPT-5.2 (medium), GPT-5 Codex (high), GPT-5 (high), Grok 4.3 (low), 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, GPT-5.4 mini (medium), Claude 4.5 Haiku, gpt-oss-120b (high), o4-mini (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-mini, o3-pro, and Claude 4.1 Opus. Reasoning models use extended thinking to work through complex problems before providing an answer.

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.