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Open weights model

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Released February 2025

Phi-4 Multimodal Instruct Intelligence, Performance & Price Analysis

Name: Intelligence
Creator: Artificial Analysis
License: https://artificialanalysis.ai/docs/legal/Terms-of-Use.pdf

Try it out API Provider Benchmarks

Intelligence

Artificial Analysis Intelligence Index

* Estimated

Speed

N/A

Output tokens per second

Cost

In $0.08Out $0.32

N/A

Cost per Intelligence Index task

Verbosity

N/A

Output tokens from Intelligence Index

Phi-4 Multimodal Instruct is below average in intelligence, but reasonably priced when comparing to other open weight non-reasoning models of similar size. The model supports text, image, and speech input, outputs text, and has a 128k tokens context window with knowledge up to June 2024.

Phi-4 Multimodal Instruct scores 6 on the Artificial Analysis Intelligence Index, placing it below average among comparable models (median: 6).

Pricing for Phi-4 Multimodal Instruct is $0.08 per 1M input tokens (moderately priced, median: $0.12) and $0.32 per 1M output tokens (somewhat expensive, median: $0.25).

Reasoning	No This page shows the non-reasoning version of this model. A reasoning variant may also exist.
Input modality	Supports: text, image, and speech
Output modality	Supports: text
Knowledge cutoff	Jun 1, 2024
Context window	128k ~192 A4 pages of size 12 Arial font
Total parameters	5.6B
License	MIT
Model weights	Hugging Face

Metrics are compared against models of the same class:

Non-reasoning models → compared only with other non-reasoning models
Reasoning models → compared across both reasoning and non-reasoning
Open weights models → compared only with other open weights models of the same size class:

Tiny: ≤4B parameters
Small: 4B–40B parameters
Medium: 40B–150B parameters
Large: >150B parameters

Proprietary models → compared across proprietary and open weights models of the same price range, using a blended 3:1 input/output price ratio:

<$0.15 per 1M tokens
$0.15–$1 per 1M tokens
>$1 per 1M tokens

Intelligence

Artificial Analysis Intelligence Index · Higher is better

Speed

Output tokens per second · Higher is better

Cost per Task

Weighted average cost (USD) per Intelligence Index task · Lower is better

Intelligence

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v4.3.2 incorporates 10 evaluations: AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1

No publicly available API

Estimate (independent evaluation forthcoming)

Artificial Analysis Intelligence Index v4.3.2 includes: AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Artificial Analysis Intelligence Index by Open Weights / Proprietary

No publicly available API

Estimate (independent evaluation forthcoming)

Indicates whether the model weights are available. Models are labelled as 'Commercial Use Restricted' if commercial use is limited by conditions, and as 'Non-commercial' if the license prohibits commercial use.

Intelligence Index vs. Cost per Intelligence Index Task

Artificial Analysis Intelligence Index · Weighted average cost (USD) per Artificial Analysis Intelligence Index task

Most attractive quadrant

Pareto line

Weighted average cost per Intelligence Index task. Each evaluation’s cost is calculated from input, cache hit, cache write, reasoning, and answer token prices, divided by task count, and weighted by its Intelligence Index weight.

Benchmarks

Intelligence Evaluations

Intelligence evaluations measured independently by Artificial Analysis · Higher is better

No publicly available API

AA-Briefcase v1.1Updated

Agentic knowledge work, (Elo-500)/2000

AA-Omniscience Accuracy

Knowledge

AA-Omniscience Non-Hallucination Rate

1 - hallucination rate

AutomationBench-AA

Agentic SaaS workflows

GDPval-AA v2.1Updated

Agentic real-world work tasks, (Elo-500)/2000

Humanity's Last Exam

Reasoning & knowledge

GDP.pdf

Professional document reasoning, All-pass

Terminal-Bench 4.0

Agentic coding & terminal use

AA-LCR v1.1

Long context reasoning

SciCodeUnder review

Coding

CritPtUnder review

Physics reasoning

Terminal-Bench-Science 0.1New

Agentic scientific research workflows in a terminal

FrontierCode 1.1New

Agentic software engineering in open-source repositories

AA-AnalystAgent

Quantitative analysis on spreadsheets & documents

Harvey LAB-AA v1.1Updated

Legal agentic work, Hallucination-Gated All-Pass Rate

EnterpriseOps-Gym-AA

Agentic business operations

ITBench-AA

Kubernetes incident root-cause analysis

MLCR-AA

Medical long context reasoning

MMMU-Pro

Visual reasoning

While model intelligence generally translates across use cases, specific evaluations may be more relevant for certain use cases.

Openness Index

Artificial Analysis Openness Index: Score

Openness Index assesses model openness on a 0 to 100 normalized scale (higher is more open)

Intelligence Index Comparisons

Intelligence Index vs. Cost per Intelligence Index Task

Artificial Analysis Intelligence Index · Weighted average cost (USD) per Artificial Analysis Intelligence Index task

Most attractive quadrant

Pareto line

Cost

Cost per Intelligence Index Task

Weighted average cost (USD) per Intelligence Index task · Segmented by token type · Lower is better

Cost to Run Artificial Analysis Intelligence Index

Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index

The cost to run the evaluations in the Artificial Analysis Intelligence Index, calculated using the model's input, cache hit, cache write, reasoning, and answer token prices and the number of tokens used across evaluations (excluding repeats).

Pricing: Cache Hit, Input, and Output

Price (USD per M Tokens)

Price per token for cached prompts (previously processed), typically offering a significant discount compared to regular input price, represented as USD per million tokens. The values shown here are the cache hit price; cache write and cache storage are billed separately and vary by provider — see "Cache pricing by provider" for detail.

Context Window

Context window: tokens limit · Higher is better

No publicly available API

Larger context windows are relevant to RAG (Retrieval Augmented Generation) LLM workflows which typically involve reasoning and information retrieval of large amounts of data.

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

Model Size (Open Weights Models Only)

Model Size: Total and Active Parameters

Comparison between total model parameters and parameters active during inference (billions)

The total number of trainable weights and biases in the model, expressed in billions. These parameters are learned during training and determine the model's ability to process and generate responses.

The number of parameters actually executed during each inference forward pass, expressed in billions. For Mixture of Experts (MoE) models, a routing mechanism selects a subset of experts per token, resulting in fewer active than total parameters. Dense models use all parameters, so active equals total.

Frequently Asked Questions

Common questions about Phi-4 Multimodal Instruct

Phi-4 Multimodal Instruct was released on February 26, 2025.

Phi-4 Multimodal Instruct was created by Microsoft.

Phi-4 Multimodal Instruct scores 6 (estimated) on the Artificial Analysis Intelligence Index, placing it below average among other open weight non-reasoning models of similar size (median: 6).

Phi-4 Multimodal Instruct costs $0.08 per 1M input tokens (better than average, median: $0.12) and $0.32 per 1M output tokens (somewhat higher than average, median: $0.25), based on Microsoft's API.

Phi-4 Multimodal Instruct costs $0.08 per 1M input tokens and $0.32 per 1M output tokens (based on Microsoft's API). For a blended rate (7:2:1 cache hit/input/output ratio), this is $0.10 per 1M tokens. Pricing may vary by provider. Compare provider pricing

No, Phi-4 Multimodal Instruct is not a reasoning model. It provides direct responses without extended chain-of-thought reasoning.

Phi-4 Multimodal Instruct supports text, image, and speech input.

Phi-4 Multimodal Instruct supports text output.

Yes, Phi-4 Multimodal Instruct supports image input and can analyze, describe, and answer questions about images.

Yes, Phi-4 Multimodal Instruct is multimodal. It can process text, image, and speech input and generate text output.

Phi-4 Multimodal Instruct has a context window of 130k tokens. This determines how much text and conversation history the model can process in a single request.

Yes, Phi-4 Multimodal Instruct is open weights. The model weights are publicly available and can be downloaded for self-hosting.

Phi-4 Multimodal Instruct has 5.6 billion parameters.

Phi-4 Multimodal Instruct is released under the MIT license. This license allows commercial use. View license

Phi-4 Multimodal Instruct achieves a score of 6 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across reasoning, knowledge, mathematics, and coding.

Phi-4 Multimodal Instruct has a knowledge cutoff of June 2024. The model's training data includes information up to this date.

Yes, Phi-4 Multimodal Instruct is available via API through 1 provider. Compare API providers

Phi-4 Multimodal Instruct is available through 1 API provider. Compare providers

Phi-4 Multimodal Instruct Intelligence, Performance & Price Analysis

Model summary

Intelligence

Speed

Cost

Verbosity

Comparison Summary

Technical specifications

75 models in this class

Intelligence

Speed

Cost per Task

Intelligence

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index by Open Weights / Proprietary

Artificial Analysis Intelligence Index

Open Weights

Intelligence Index vs. Cost per Intelligence Index Task

Cost per Intelligence Index Task

Artificial Analysis Intelligence Index

Benchmarks

Intelligence Evaluations

Intelligence Evaluation Relevance

Artificial Analysis Intelligence Index

Openness Index

Artificial Analysis Openness Index: Score

Intelligence Index Comparisons

Intelligence Index vs. Cost per Intelligence Index Task

Cost per Intelligence Index Task

Artificial Analysis Intelligence Index

Cost

Cost per Intelligence Index Task

Cost per Intelligence Index Task

Cost to Run Artificial Analysis Intelligence Index

Cost to Run Artificial Analysis Intelligence Index

Pricing: Cache Hit, Input, and Output

Cache Hit

Context Window

Context Window

Context Window for RAG

Context Window

Model Size (Open Weights Models Only)

Model Size: Total and Active Parameters

Total Parameters

Active Parameters at Inference Time

Frequently Asked Questions

When was Phi-4 Multimodal Instruct released?

Who created Phi-4 Multimodal Instruct?

How intelligent is Phi-4 Multimodal Instruct?

How much does Phi-4 Multimodal Instruct cost?

What is Phi-4 Multimodal Instruct API pricing?

Is Phi-4 Multimodal Instruct a reasoning model?

What input modalities does Phi-4 Multimodal Instruct support?

What output modalities does Phi-4 Multimodal Instruct support?

Can Phi-4 Multimodal Instruct process images?

Is Phi-4 Multimodal Instruct multimodal?

What is the context window of Phi-4 Multimodal Instruct?

Is Phi-4 Multimodal Instruct open source?

How many parameters does Phi-4 Multimodal Instruct have?

What is the license for Phi-4 Multimodal Instruct?

How does Phi-4 Multimodal Instruct perform on benchmarks?

What is the knowledge cutoff date for Phi-4 Multimodal Instruct?

Is Phi-4 Multimodal Instruct available via API?

Where can I use Phi-4 Multimodal Instruct?