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Phi-4 Multimodal Instruct logo

Phi-4 Multimodal Instruct API Provider Benchmarking & Analysis

Microsoft Azure

Open weights model

Released February 2025

Analysis of API providers for Phi-4 Multimodal Instruct across performance metrics including latency (time to first token), output speed (output tokens per second), price and others. API providers benchmarked include Microsoft Azure.

Fastest

#1
AzureAzure
16.5 t/s

Output speed

Total 1 providers

Lowest Latency

#1
AzureAzure
0.86 s

Time to first token

Total 1 providers

Lowest Price

Blended price (per 1M tokens)

Total 1 providers

Phi-4 Multimodal is available through Azure.

Speed
Output Tokens per Second; Higher is better
End-to-End Response Time
Seconds; Lower is better
Price
USD per 1M Tokens; Lower is better

Navigation

PricingSpeedLatencyEnd-to-End Response TimeAPI FeaturesSummary Table of Key Comparison Metrics

Update: Default performance benchmarking workload has updated to 10k input tokens to better reflect production use cases. You can still select different workloads above!

Pricing

Pricing: Input and Output Prices

USD per 1M Tokens; Lower is better
Input price
Output price

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

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

Speed vs. Price

Output Speed: Output Tokens per Second; Price: USD per 1M Tokens
Most attractive quadrant

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).

Figures represent median (P50) measurement over the past 72 hours to reflect sustained changes in performance.

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 median (P50) measurement over the past 72 hours to reflect sustained changes in performance.

Latency vs. Output Speed

Latency: Seconds to First Token Received; Output Speed: Output Tokens per Second; 10,000 Input Tokens
Most attractive quadrant
Size represents Price (USD per M Tokens)
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).

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).

Figures represent median (P50) measurement over the past 72 hours to reflect sustained changes in performance.

Latency

Measured by Time (seconds) to First Token

Time to First Token

Seconds to First Token Received; Lower is better; 10,000 Input Tokens

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.

Figures represent median (P50) measurement over the past 72 hours to reflect sustained changes in performance.

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

Seconds to Output 500 Tokens, including reasoning model 'thinking' time; Lower is better; 10,000 Input Tokens
Input processing time
'Thinking' time (reasoning models)
Outputting time

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

For fair comparison, the number of reasoning tokens is standardized across all providers for each model based on the model's representative query token counts.

Figures represent median (P50) measurement over the past 72 hours to reflect sustained changes in performance.

API Features

Function (Tool) Calling & JSON Mode

ModelsFunction callingJSON Mode
Azure logoAzure

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.

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).

While models have their own context window, in cases this is limited by providers.

Summary Table of Key Comparison Metrics

Frequently Asked Questions

Common questions about Phi-4 Multimodal Instruct providers

Phi-4 Multimodal Instruct is available through 1 API provider: Azure. Each provider offers different performance characteristics and pricing.

Phi-4 Multimodal Instruct is currently available through 1 API provider that we benchmark and track.

The fastest provider for Phi-4 Multimodal Instruct by output speed is Azure (16.5 t/s). Output speed measures how quickly tokens are generated after the model starts responding.

The provider with the lowest time to first token for Phi-4 Multimodal Instruct is Azure (0.86s). Lower latency means faster initial response time.

When choosing a provider for Phi-4 Multimodal Instruct, consider: output speed (for throughput-intensive tasks), latency (for interactive applications requiring quick first responses), pricing (for cost-sensitive workloads), and API features like JSON mode or function calling.

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.

For information about Phi-4 Multimodal Instruct's intelligence, capabilities, modalities, and how it compares to other models, see the model overview page. View model overview