Best open source LLMs for Enterprise

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Summary

Open-source LLMs: Balancing cost, privacy, and performance for the enterprise. What are Open-Source LLMs? Open-source large language models (LLMs) put you in control. Imagine you’re using a language model for customer support. You need the model to handle sensitive information securely and to be customizable to fit your needs. With open-source LLMs, you get full access to a model’s inner workings—both its code and training data. This openness allows you to fine-tune the model for accuracy and data privacy, which is especially important if you’re in a field with strict compliance standards. Unlike proprietary models like GPT-4o, whose algorithms are kept hidden, open-source LLMs are fully transparent. Proprietary models like GPT-4o and Claude 3.5 are at the cutting edge of performance, but open-source LLMs are catching up. As traditional benchmarks often focus on skills like math or multimodal abilities, they don't accurately reflect a model's suitability for enterprise-specific tasks like customer support, where response accuracy, contextuality, and compliance matter more. So, which open-source LLM should you use to build your enterprise in-house solution? The answer depends on your requirements: For more on the best proprietary alternatives, check out our article on ChatGPT alternatives. How We Evaluated Open-Source LLMs for Enterprise Use Cases We evaluated a selection of open-source LLMs using the BASIC benchmark, a practical framework designed to assess models' suitability for the specific needs of enterprise applications. Instead of relying on general NLP benchmarks, our assessment focuses on five enterprise-relevant metrics: Boundedness: Ability to generate on-topic responses. Accuracy: Reliability in providing correct answers. Speed: Response time, which impacts user experience. Inexpensiveness: Cost-efficiency, focusing on token usage and scalability. Completeness: Providing enough context without excessive verbosity. We tested the models using a single data...

First seen: 2025-05-17 17:47

Last seen: 2025-05-17 17:47