Compare the Top Foundation Models that integrate with SambaNova as of September 2026

This a list of Foundation Models that integrate with SambaNova. Use the filters on the left to add additional filters for products that have integrations with SambaNova. View the products that work with SambaNova in the table below.

What are Foundation Models for SambaNova?

Foundation models are large-scale artificial intelligence models trained on vast and diverse datasets that serve as the underlying technology for a wide range of AI applications. These models learn general-purpose capabilities such as language understanding, reasoning, image recognition, code generation, speech processing, and multimodal comprehension, allowing them to be adapted or fine-tuned for specific tasks across industries. Foundation models power applications including chatbots, AI agents, search, content generation, software development, scientific research, and business automation. Many are available through cloud APIs, open-source distributions, and enterprise AI platforms, supporting custom model development, retrieval-augmented generation (RAG), and domain-specific optimization. By providing reusable, general-purpose intelligence, foundation models enable organizations to accelerate AI development, reduce implementation costs, and build sophisticated AI-powered applications. Compare and read user reviews of the best Foundation Models for SambaNova currently available using the table below. This list is updated regularly.

  • 1
    Qwen

    Qwen

    Alibaba

    Qwen is a powerful, free AI assistant built on the advanced Qwen model series, designed to help anyone with creativity, research, problem-solving, and everyday tasks. While Qwen Chat is the main interface for most users, Qwen itself powers a broad range of intelligent capabilities including image generation, deep research, website creation, advanced reasoning, and context-aware search. Its multimodal intelligence enables Qwen to understand and process text, images, audio, and video simultaneously for richer insights. Qwen is available on web, desktop, and mobile, ensuring seamless access across all devices. For developers, the Qwen API provides OpenAI-compatible endpoints, making integration simple and allowing Qwen’s intelligence to power apps, services, and automation. Whether you're chatting through Qwen Chat or building with the Qwen API, Qwen delivers fast, flexible, and highly capable AI support.
    Starting Price: Free
  • 2
    DeepSeek R1

    DeepSeek R1

    DeepSeek

    DeepSeek-R1 is an advanced open-source reasoning model developed by DeepSeek, designed to rival OpenAI's Model o1. Accessible via web, app, and API, it excels in complex tasks such as mathematics and coding, demonstrating superior performance on benchmarks like the American Invitational Mathematics Examination (AIME) and MATH. DeepSeek-R1 employs a mixture of experts (MoE) architecture with 671 billion total parameters, activating 37 billion parameters per token, enabling efficient and accurate reasoning capabilities. This model is part of DeepSeek's commitment to advancing artificial general intelligence (AGI) through open-source innovation.
    Starting Price: Free
  • 3
    Llama 4 Behemoth
    Llama 4 Behemoth is Meta's most powerful AI model to date, featuring a massive 288 billion active parameters. It excels in multimodal tasks, outperforming previous models like GPT-4.5 and Gemini 2.0 Pro across multiple STEM-focused benchmarks such as MATH-500 and GPQA Diamond. As the teacher model for the Llama 4 series, Behemoth sets the foundation for models like Llama 4 Maverick and Llama 4 Scout. While still in training, Llama 4 Behemoth demonstrates unmatched intelligence, pushing the boundaries of AI in fields like math, multilinguality, and image understanding.
    Starting Price: Free
  • 4
    Llama 4 Maverick
    Llama 4 Maverick is one of the most advanced multimodal AI models from Meta, featuring 17 billion active parameters and 128 experts. It surpasses its competitors like GPT-4o and Gemini 2.0 Flash in a broad range of benchmarks, especially in tasks related to coding, reasoning, and multilingual capabilities. Llama 4 Maverick combines image and text understanding, enabling it to deliver industry-leading results in image-grounding tasks and precise, high-quality output. With its efficient performance at a reduced parameter size, Maverick offers exceptional value, especially in general assistant and chat applications.
    Starting Price: Free
  • 5
    Llama 4 Scout
    Llama 4 Scout is a powerful 17 billion active parameter multimodal AI model that excels in both text and image processing. With an industry-leading context length of 10 million tokens, it outperforms its predecessors, including Llama 3, in tasks such as multi-document summarization and parsing large codebases. Llama 4 Scout is designed to handle complex reasoning tasks while maintaining high efficiency, making it perfect for use cases requiring long-context comprehension and image grounding. It offers cutting-edge performance in image-related tasks and is particularly well-suited for applications requiring both text and visual understanding.
    Starting Price: Free
  • 6
    Llama

    Llama

    Meta

    Llama (Large Language Model Meta AI) is a state-of-the-art foundational large language model designed to help researchers advance their work in this subfield of AI. Smaller, more performant models such as Llama enable others in the research community who don’t have access to large amounts of infrastructure to study these models, further democratizing access in this important, fast-changing field. Training smaller foundation models like Llama is desirable in the large language model space because it requires far less computing power and resources to test new approaches, validate others’ work, and explore new use cases. Foundation models train on a large set of unlabeled data, which makes them ideal for fine-tuning for a variety of tasks. We are making Llama available at several sizes (7B, 13B, 33B, and 65B parameters) and also sharing a Llama model card that details how we built the model in keeping with our approach to Responsible AI practices.
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