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In our 21-criterion evaluation of AI foundation models for language providers, we identified the most significant ones and researched, analyzed, and scored them. This report shows how each provider measures up and helps enterprise business and technology professionals select the right one for their needs.

Benchmarks Are Only One Chapter In The AI Foundation Model Story

• Can configure and govern models to reduce hallucinations and align with your brand. AI-FMLs are powerful in a generalized way, but in order to provide value, they need to be aligned to business data, company values, and policies to diminish hallucinations and stay on brand. It’s simple for an enterprise developer to pass a prompt to a model API and get the output back.
• Respect IP rights and the IP rights of others. Enterprises are rightly concerned about protecting their data, including IP, customer data, and operational data. Enterprises expect that when they send prompts with company data that the AI-FML vendor won’t use that data to improve the model or for any other use.
• Can scale with low latency and never go down. AI-FMLs are not immune to the laws of physics. The bigger the model, the more cycles it needs to return the output. Users of AI-FML chatbots are all too familiar with jittery output often taking several seconds to return a result
• Google’s Gemini combines a world-class model with enterprise cloud services. Google’s flagship model, Gemini, came out of its Google DeepMind research division, focused on cutting-edge AI models and research. Gemini is uniquely differentiated in the market, especially in multimodality and context length, while also ensuring interconnectivity with the broader ecosystem of complementary cloud services.

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