Comprehensive side-by-side LLM comparison
Phi-4-multimodal-instruct leads with 4.4% higher average benchmark score. DeepSeek VL2 offers 2.6K more tokens in context window than Phi-4-multimodal-instruct. Phi-4-multimodal-instruct is $4809.35 cheaper per million tokens. Both models have their strengths depending on your specific coding needs.
DeepSeek
DeepSeek VL2 is a multimodal language model developed by DeepSeek. It achieves strong performance with an average score of 70.9% across 14 benchmarks. It excels particularly in DocVQA (93.3%), ChartQA (86.0%), TextVQA (84.2%). It supports a 259K 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. Released in 2024, it represents DeepSeek's latest advancement in AI technology.
Microsoft
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.
1 month newer
DeepSeek VL2
DeepSeek
2024-12-13
Phi-4-multimodal-instruct
Microsoft
2025-02-01
Cost per million tokens (USD)
DeepSeek VL2
Phi-4-multimodal-instruct
Context window and performance specifications
Average performance across 20 common benchmarks
DeepSeek VL2
Phi-4-multimodal-instruct
Phi-4-multimodal-instruct
2024-06-01
Available providers and their performance metrics
DeepSeek VL2
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