List of the Top 8 Small Language Models for Microsoft Foundry Models in 2026

Mistral AI is recognized as a pioneering startup in the field of artificial intelligence, with a particular emphasis on open-source generative technologies. The company offers a wide range of customizable, enterprise-grade AI solutions that can be deployed across multiple environments, including on-premises, cloud, edge, and individual devices. Notable among their offerings are "Le Chat," a multilingual AI assistant designed to enhance productivity in both personal and business contexts, and "La Plateforme," a resource for developers that streamlines the creation and implementation of AI-powered applications. Mistral AI's unwavering dedication to transparency and innovative practices has enabled it to carve out a significant niche as an independent AI laboratory, where it plays an active role in the evolution of open-source AI while also influencing relevant policy conversations. By championing the development of an open AI ecosystem, Mistral AI not only contributes to technological advancements but also positions itself as a leading voice within the industry, shaping the future of artificial intelligence. This commitment to fostering collaboration and openness within the AI community further solidifies its reputation as a forward-thinking organization.

We are excited to unveil Mistral NeMo, our latest and most sophisticated small model, boasting an impressive 12 billion parameters and a vast context length of 128,000 tokens, all available under the Apache 2.0 license. In collaboration with NVIDIA, Mistral NeMo stands out in its category for its exceptional reasoning capabilities, extensive world knowledge, and coding skills. Its architecture adheres to established industry standards, ensuring it is user-friendly and serves as a smooth transition for those currently using Mistral 7B. To encourage adoption by researchers and businesses alike, we are providing both pre-trained base models and instruction-tuned checkpoints, all under the Apache license. A remarkable feature of Mistral NeMo is its quantization awareness, which enables FP8 inference while maintaining high performance levels. Additionally, the model is well-suited for a range of global applications, showcasing its ability in function calling and offering a significant context window. When benchmarked against Mistral 7B, Mistral NeMo demonstrates a marked improvement in comprehending and executing intricate instructions, highlighting its advanced reasoning abilities and capacity to handle complex multi-turn dialogues. Furthermore, its design not only enhances its performance but also positions it as a formidable option for multi-lingual tasks, ensuring it meets the diverse needs of various use cases while paving the way for future innovations.

GPT-5 mini is a faster, more affordable variant of OpenAI’s advanced GPT-5 language model, specifically tailored for well-defined and precise tasks that benefit from high reasoning ability. It accepts both text and image inputs (image input only), and generates high-quality text outputs, supported by a large 400,000-token context window and a maximum of 128,000 tokens in output, enabling complex multi-step reasoning and detailed responses. The model excels in providing rapid response times, making it ideal for use cases where speed and efficiency are critical, such as chatbots, customer service, or real-time analytics. GPT-5 mini’s pricing structure significantly reduces costs, with input tokens priced at $0.25 per million and output tokens at $2 per million, offering a more economical option compared to the flagship GPT-5. While it supports advanced features like streaming, function calling, structured output generation, and fine-tuning, it does not currently support audio input or image generation capabilities. GPT-5 mini integrates seamlessly with multiple API endpoints including chat completions, responses, embeddings, and batch processing, providing versatility for a wide array of applications. Rate limits are tier-based, scaling from 500 requests per minute up to 30,000 per minute for higher tiers, accommodating small to large scale deployments. The model also supports snapshots to lock in performance and behavior, ensuring consistency across applications. GPT-5 mini is ideal for developers and businesses seeking a cost-effective solution with high reasoning power and fast throughput. It balances cutting-edge AI capabilities with efficiency, making it a practical choice for applications demanding speed, precision, and scalability.

We are excited to unveil the latest version of our open-source large language model, which includes model weights and initial code for the pretrained and fine-tuned Llama language models, ranging from 7 billion to 70 billion parameters. The Llama 2 pretrained models have been crafted using a remarkable 2 trillion tokens and boast double the context length compared to the first iteration, Llama 1. Additionally, the fine-tuned models have been refined through the insights gained from over 1 million human annotations. Llama 2 showcases outstanding performance compared to various other open-source language models across a wide array of external benchmarks, particularly excelling in reasoning, coding abilities, proficiency, and knowledge assessments. For its training, Llama 2 leveraged publicly available online data sources, while the fine-tuned variant, Llama-2-chat, integrates publicly accessible instruction datasets alongside the extensive human annotations mentioned earlier. Our project is backed by a robust coalition of global stakeholders who are passionate about our open approach to AI, including companies that have offered valuable early feedback and are eager to collaborate with us on Llama 2. The enthusiasm surrounding Llama 2 not only highlights its advancements but also marks a significant transformation in the collaborative development and application of AI technologies. This collective effort underscores the potential for innovation that can emerge when the community comes together to share resources and insights.

The Grok-3 Mini, a creation of xAI, functions as a swift and astute AI companion tailored for those in search of quick yet thorough answers to their questions. While maintaining the essential features of the Grok series, this smaller model presents a playful yet profound perspective on diverse aspects of human life, all while emphasizing efficiency. It is particularly beneficial for individuals who are frequently in motion or have limited access to resources, guaranteeing that an equivalent level of curiosity and support is available in a more compact format. Furthermore, Grok-3 Mini is adept at tackling a variety of inquiries, providing succinct insights that do not compromise on depth or precision, positioning it as a valuable tool for managing the complexities of modern existence. In addition to its practicality, Grok-3 Mini also fosters a sense of engagement, encouraging users to explore their questions further in a user-friendly manner. Ultimately, it represents a harmonious blend of intelligence and usability that addresses the evolving needs of today's users.

Phi-4 is an innovative small language model (SLM) with 14 billion parameters, demonstrating remarkable proficiency in complex reasoning tasks, especially in the realm of mathematics, in addition to standard language processing capabilities. Being the latest member of the Phi series of small language models, Phi-4 exemplifies the strides we can make as we push the horizons of SLM technology. Currently, it is available on Azure AI Foundry under a Microsoft Research License Agreement (MSRLA) and will soon be launched on Hugging Face. With significant enhancements in methodologies, including the use of high-quality synthetic datasets and meticulous curation of organic data, Phi-4 outperforms both similar and larger models in mathematical reasoning challenges. This model not only showcases the continuous development of language models but also underscores the important relationship between the size of a model and the quality of its outputs. As we forge ahead in innovation, Phi-4 serves as a powerful example of our dedication to advancing the capabilities of small language models, revealing both the opportunities and challenges that lie ahead in this field. Moreover, the potential applications of Phi-4 could significantly impact various domains requiring sophisticated reasoning and language comprehension.

Phi-4-reasoning is a sophisticated transformer model that boasts 14 billion parameters, crafted specifically to address complex reasoning tasks such as mathematics, programming, algorithm design, and strategic decision-making. It achieves this through an extensive supervised fine-tuning process, utilizing curated "teachable" prompts and reasoning examples generated via o3-mini, which allows it to produce detailed reasoning sequences while optimizing computational efficiency during inference. By employing outcome-driven reinforcement learning techniques, Phi-4-reasoning is adept at generating longer reasoning pathways. Its performance is remarkable, exceeding that of much larger open-weight models like DeepSeek-R1-Distill-Llama-70B, and it closely rivals the more comprehensive DeepSeek-R1 model across a range of reasoning tasks. Engineered for environments with constrained computing resources or high latency, this model is refined with synthetic data sourced from DeepSeek-R1, ensuring it provides accurate and methodical solutions to problems. The efficiency with which this model processes intricate tasks makes it an indispensable asset in various computational applications, further enhancing its significance in the field. Its innovative design reflects an ongoing commitment to pushing the boundaries of artificial intelligence capabilities.

Phi-4-mini-reasoning is an advanced transformer-based language model that boasts 3.8 billion parameters, tailored specifically for superior performance in mathematical reasoning and systematic problem-solving, especially in scenarios with limited computational resources and low latency. The model's optimization is achieved through fine-tuning with synthetic data generated by the DeepSeek-R1 model, which effectively balances performance and intricate reasoning skills. Having been trained on a diverse set of over one million math problems that vary from middle school level to Ph.D. complexity, Phi-4-mini-reasoning outperforms its foundational model by generating extensive sentences across numerous evaluations and surpasses larger models like OpenThinker-7B, Llama-3.2-3B-instruct, and DeepSeek-R1 in various tasks. Additionally, it features a 128K-token context window and supports function calling, which ensures smooth integration with different external tools and APIs. This model can also be quantized using the Microsoft Olive or Apple MLX Framework, making it deployable on a wide range of edge devices such as IoT devices, laptops, and smartphones. Furthermore, its design not only enhances accessibility for users but also opens up new avenues for innovative applications in the realm of mathematics, potentially revolutionizing how such problems are approached and solved.