Google AI vs Microsoft: Foundation Models Comparison

Google AI develops the Gemini family of multimodal models, capable of processing text, images, audio, and video in a single model. The division traces its roots to Google Brain (founded 2011), which merged with DeepMind (acquired by Google in 2014) in April 2023 to form Google DeepMind under CEO Demis Hassabis.

Gemini models power Google's consumer AI products including the Gemini chatbot, Search AI Overviews, and Workspace integrations. The API is available through Google AI Studio and Vertex AI, offering models from the cost-efficient Flash-Lite ($0.10/$0.40 per MTok) to the frontier Gemini 3.1 Pro ($2/$12 per MTok). Google's generous free tier provides unlimited access to several models, and the 1M+ token context window is the largest among major providers.

Consumer subscriptions range from free to Google AI Ultra at $249.99/month, which includes advanced features like Project Mariner (browser agent) and 30TB cloud storage. Google DeepMind employs approximately 5,600-8,000 people and continues to push boundaries with open-source models like Gemma and foundational research contributions.

Microsoft Phi is a family of small language models designed for resource efficiency without compromising performance. Starting with Phi-2 (2.7B parameters) that surpassed Mistral and Llama-2 models at 7B-13B parameters, the Phi family now includes Phi-4, Phi-4-multimodal (text, audio, vision), and Phi-4-mini. Phi-4 costs USD 0.13 per 1M input tokens and USD 0.50 per 1M output tokens on Azure, with a blended rate of USD 0.22 per 1M tokens. The models excel at math and reasoning tasks, with Phi-4 outperforming comparable and larger models through high-quality synthetic datasets and post-training innovations. Phi models are particularly effective for resource-constrained environments, on-device inference, latency-sensitive scenarios, and cost-constrained use cases. Available through Azure AI Foundry with pay-as-you-go and provisioned throughput options, Phi models provide a 200,000-word vocabulary in 20+ languages. While impressive for their size, limitations include primary English design, reduced factual knowledge capacity, code generation primarily in Python, and tendency for textbook-like verbose responses.