Phi-4-multimodal-instruct
Multimodal
Zero-eval
#1ScienceQA Visual
#1BLINK
#1InterGPS
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by Microsoft
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About
Phi-4-multimodal-instruct is a multimodal language model developed by Microsoft. It achieves strong performance with an average score of 72.0% across 15 benchmarks. It excels particularly in ScienceQA Visual (97.5%), DocVQA (93.2%), MMBench (86.7%). It supports a 256K token context window for handling large documents. The model is available through 1 API provider. As a multimodal model, it can process and understand text, images, and other input formats seamlessly. It's licensed for commercial use, making it suitable for enterprise applications. Released in 2025, it represents Microsoft's latest advancement in AI technology.
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Pricing Range
Input (per 1M)$0.05 -$0.05
Output (per 1M)$0.10 -$0.10
Providers1
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Timeline
AnnouncedFeb 1, 2025
ReleasedFeb 1, 2025
Knowledge CutoffJun 1, 2024
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Specifications
Training Tokens5.0T
Capabilities
Multimodal
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License & Family
License
MIT
Performance Overview
Performance metrics and category breakdown
Overall Performance
15 benchmarks
Average Score
72.0%
Best Score
97.5%
High Performers (80%+)
7Performance Metrics
Max Context Window
256.0KAvg Throughput
25.0 tok/sAvg Latency
1ms+
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All Benchmark Results for Phi-4-multimodal-instruct
Complete list of benchmark scores with detailed information
| ScienceQA Visual | multimodal | 0.97 | 97.5% | Self-reported | |
| DocVQA | multimodal | 0.93 | 93.2% | Self-reported | |
| MMBench | multimodal | 0.87 | 86.7% | Self-reported | |
| POPE | multimodal | 0.86 | 85.6% | Self-reported | |
| OCRBench | multimodal | 0.84 | 84.4% | Self-reported | |
| AI2D | multimodal | 0.82 | 82.3% | Self-reported | |
| ChartQA | multimodal | 0.81 | 81.4% | Self-reported | |
| TextVQA | multimodal | 0.76 | 75.6% | Self-reported | |
| InfoVQA | multimodal | 0.73 | 72.7% | Self-reported | |
| MathVista | multimodal | 0.62 | 62.4% | Self-reported |
Showing 1 to 10 of 15 benchmarks