Microsoft: Phi 4 Multimodal Instruct

Text input Image input Text output
Author's Description

Phi-4 Multimodal Instruct is a versatile 5.6B parameter foundation model that combines advanced reasoning and instruction-following capabilities across both text and visual inputs, providing accurate text outputs. The unified architecture enables efficient, low-latency inference, suitable for edge and mobile deployments. Phi-4 Multimodal Instruct supports text inputs in multiple languages including Arabic, Chinese, English, French, German, Japanese, Spanish, and more, with visual input optimized primarily for English. It delivers impressive performance on multimodal tasks involving mathematical, scientific, and document reasoning, providing developers and enterprises a powerful yet compact model for sophisticated interactive applications. For more information, see the [Phi-4 Multimodal blog post](https://azure.microsoft.com/en-us/blog/empowering-innovation-the-next-generation-of-the-phi-family/).

Key Specifications
Cost
$$
Context
131K
Parameters
5.6B (Rumoured)
Released
Mar 07, 2025
Speed
Ability
Reliability
Supported Parameters

This model supports the following parameters:

Response Format Stop Seed Min P Top P Max Tokens Frequency Penalty Temperature Presence Penalty
Features

This model supports the following features:

Response Format
Performance Summary

Microsoft's Phi-4 Multimodal Instruct demonstrates competitive response times, ranking in the 42nd percentile across six benchmarks. It consistently offers among the most competitive pricing, placing in the 88th percentile, making it a cost-effective option. The model also exhibits strong reliability with an 82% success rate, indicating few technical issues. In terms of performance across categories, Phi-4 Multimodal Instruct shows varied results. Its accuracy is notably low in General Knowledge (17th percentile), Ethics (19th percentile), and Coding (19th percentile), suggesting these areas are significant weaknesses. While its Instruction Following (30th percentile) and Reasoning (37th percentile) capabilities are below average, they are not as pronounced as its lowest scores. A key strength appears to be Email Classification, where it achieves 92% accuracy, placing it in the 21st percentile, though this percentile rank indicates many models perform similarly or better. The model's low cost and high reliability are its most significant competitive advantages, particularly for deployments where efficiency and stability are paramount, such as edge and mobile applications.

Model Pricing

Current Pricing

Feature Price (per 1M tokens)
Prompt $0.05
Completion $0.1

Price History

Available Endpoints
Provider Endpoint Name Context Length Pricing (Input) Pricing (Output)
DeepInfra
DeepInfra | microsoft/phi-4-multimodal-instruct 131K $0.05 / 1M tokens $0.1 / 1M tokens
WandB
WandB | microsoft/phi-4-multimodal-instruct 128K $0.08 / 1M tokens $0.35 / 1M tokens
Benchmark Results
Benchmark Category Reasoning Strategy Free Executions Accuracy Cost Duration
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