Comprehensive side-by-side LLM comparison
Phi-3.5-mini-instruct leads with 18.4% higher average benchmark score. DeepSeek VL2 offers 2.6K more tokens in context window than Phi-3.5-mini-instruct. Phi-3.5-mini-instruct is $4809.30 cheaper per million tokens. DeepSeek VL2 supports multimodal inputs. Overall, Phi-3.5-mini-instruct is the stronger choice for coding tasks.
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-3.5-mini-instruct is a language model developed by Microsoft. The model shows competitive results across 31 benchmarks. It excels particularly in GSM8k (86.2%), ARC-C (84.6%), RULER (84.1%). It supports a 256K token context window for handling large documents. The model is available through 1 API provider. It's licensed for commercial use, making it suitable for enterprise applications. Released in 2024, it represents Microsoft's latest advancement in AI technology.
3 months newer
Phi-3.5-mini-instruct
Microsoft
2024-08-23
DeepSeek VL2
DeepSeek
2024-12-13
Cost per million tokens (USD)
DeepSeek VL2
Phi-3.5-mini-instruct
Context window and performance specifications
Average performance across 45 common benchmarks
DeepSeek VL2
Phi-3.5-mini-instruct
Available providers and their performance metrics
DeepSeek VL2
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Phi-3.5-mini-instruct
DeepSeek VL2
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DeepSeek VL2
Phi-3.5-mini-instruct
Azure