Best Small Language Models in the UK - Page 3

Qwen2.5-VL-32B represents an advanced AI model specifically crafted for multimodal endeavors, showcasing exceptional skills in reasoning related to both text and images. This iteration enhances the previous Qwen2.5-VL series, resulting in responses that are not only of higher quality but also more aligned with human-like formatting. The model demonstrates remarkable proficiency in mathematical reasoning, nuanced image comprehension, and intricate multi-step reasoning challenges, such as those encountered in benchmarks like MathVista and MMMU. Its performance has been validated through comparisons with competing models, often surpassing even the larger Qwen2-VL-72B in specific tasks. Furthermore, with its refined capabilities in image analysis and visual logic deduction, Qwen2.5-VL-32B offers thorough and precise evaluations of visual content, enabling it to generate insightful responses from complex visual stimuli. This model has been meticulously optimized for both textual and visual tasks, making it exceptionally well-suited for scenarios that demand advanced reasoning and understanding across various forms of media, thus expanding its potential applications even further.

Amazon Nova Micro is an advanced text-only AI model optimized for rapid language processing at a very low cost. With capabilities in reasoning, translation, and code completion, it offers over 200 tokens per second in response generation, making it suitable for fast-paced, real-time applications. Nova Micro supports fine-tuning with text inputs, and its efficiency in understanding and generating text makes it a cost-effective solution for AI-driven applications requiring high performance and quick outputs.

Amazon Nova Lite is a versatile AI model that supports multimodal inputs, including text, image, and video, and provides lightning-fast processing. It offers a great balance of speed, accuracy, and affordability, making it ideal for applications that need high throughput, such as customer engagement and content creation. With support for fine-tuning and real-time responsiveness, Nova Lite delivers high-quality outputs with minimal latency, empowering businesses to innovate at scale.

Phi-4-reasoning is an advanced transformer model featuring 14 billion parameters, specifically tailored for tackling intricate reasoning challenges, including mathematics, programming, algorithm development, and strategic planning. Through a meticulous process of supervised fine-tuning on select "teachable" prompts and reasoning examples created using o3-mini, it excels at generating thorough reasoning sequences that optimize computational resources during inference. By integrating outcome-driven reinforcement learning, Phi-4-reasoning is capable of producing extended reasoning paths. Its performance notably surpasses that of significantly larger open-weight models like DeepSeek-R1-Distill-Llama-70B and nears the capabilities of the comprehensive DeepSeek-R1 model across various reasoning applications. Designed for use in settings with limited computing power or high latency, Phi-4-reasoning is fine-tuned with synthetic data provided by DeepSeek-R1, ensuring it delivers precise and methodical problem-solving. This model's ability to handle complex tasks with efficiency makes it a valuable tool in numerous computational contexts.

Phi-4-reasoning-plus is an advanced reasoning model with 14 billion parameters, enhancing the capabilities of the original Phi-4-reasoning. It employs reinforcement learning for better inference efficiency, processing 1.5 times the number of tokens compared to its predecessor, which results in improved accuracy. Remarkably, this model performs better than both OpenAI's o1-mini and DeepSeek-R1 across various benchmarks, including challenging tasks in mathematical reasoning and advanced scientific inquiries. Notably, it even outperforms the larger DeepSeek-R1, which boasts 671 billion parameters, on the prestigious AIME 2025 assessment, a qualifier for the USA Math Olympiad. Furthermore, Phi-4-reasoning-plus is accessible on platforms like Azure AI Foundry and HuggingFace, making it easier for developers and researchers to leverage its capabilities. Its innovative design positions it as a top contender in the realm of reasoning models.

Phi-4-mini-reasoning is a transformer-based language model with 3.8 billion parameters, specifically designed to excel in mathematical reasoning and methodical problem-solving within environments that have limited computational capacity or latency constraints. Its optimization stems from fine-tuning with synthetic data produced by the DeepSeek-R1 model, striking a balance between efficiency and sophisticated reasoning capabilities. With training that encompasses over one million varied math problems, ranging in complexity from middle school to Ph.D. level, Phi-4-mini-reasoning demonstrates superior performance to its base model in generating lengthy sentences across multiple assessments and outshines larger counterparts such as OpenThinker-7B, Llama-3.2-3B-instruct, and DeepSeek-R1. Equipped with a 128K-token context window, it also facilitates function calling, which allows for seamless integration with various external tools and APIs. Moreover, Phi-4-mini-reasoning can be quantized through the Microsoft Olive or Apple MLX Framework, enabling its deployment on a variety of edge devices, including IoT gadgets, laptops, and smartphones. Its design not only enhances user accessibility but also expands the potential for innovative applications in mathematical fields.

Xgen-small is a compact language model crafted by Salesforce AI Research that is tailored for enterprise use, offering efficient long-context capabilities at a manageable cost. It employs a combination of focused data curation, scalable pre-training, length extension, instruction fine-tuning, and reinforcement learning to address the intricate and high-volume inference needs of contemporary businesses. In contrast to conventional large models, Xgen-small excels in processing extensive contexts, allowing it to effectively synthesize insights from various sources such as internal documents, code bases, research articles, and real-time data feeds. With parameter sizes of 4B and 9B, it strikes a careful balance between cost efficiency, privacy protections, and comprehensive long-context comprehension, positioning itself as a reliable and sustainable option for large-scale Enterprise AI implementation. This innovative approach not only enhances operational efficiency but also empowers organizations to leverage AI effectively in their strategic initiatives.

Designed for power users, OpenAI o4-mini-high is the go-to model when you need the best balance of performance and cost-efficiency. With its improved reasoning abilities, o4-mini-high excels in high-volume tasks that require advanced data analysis, algorithm optimization, and multi-step reasoning. It's ideal for businesses or developers who need to scale their AI solutions without sacrificing speed or accuracy.

On June 23, 2025, Microsoft unveiled Mu, an innovative 330-million-parameter encoder–decoder language model specifically crafted to enhance the agent experience within Windows environments by effectively translating natural language inquiries into function calls for Settings, all processed on-device via NPUs at a remarkable speed of over 100 tokens per second while ensuring impressive accuracy. By leveraging Phi Silica optimizations, Mu’s encoder–decoder design employs a fixed-length latent representation that significantly reduces both computational demands and memory usage, achieving a 47 percent reduction in first-token latency and a decoding speed that is 4.7 times greater on Qualcomm Hexagon NPUs when compared to other decoder-only models. Additionally, the model benefits from hardware-aware tuning techniques, which include a thoughtful 2/3–1/3 split of encoder and decoder parameters, shared weights for input and output embeddings, Dual LayerNorm, rotary positional embeddings, and grouped-query attention, allowing for swift inference rates exceeding 200 tokens per second on devices such as the Surface Laptop 7, along with sub-500 ms response times for settings-related queries. This combination of features positions Mu as a groundbreaking advancement in on-device language processing capabilities.

Phi-4-mini-flash-reasoning is a 3.8 billion-parameter model that is part of Microsoft's Phi series, specifically designed for edge, mobile, and other environments with constrained resources where processing power, memory, and speed are limited. This innovative model features the SambaY hybrid decoder architecture, integrating Gated Memory Units (GMUs) with Mamba state-space and sliding-window attention layers, achieving up to ten times the throughput and a latency reduction of 2 to 3 times compared to its earlier versions without compromising on its ability to perform complex mathematical and logical reasoning. With a support for a context length of 64K tokens and being fine-tuned on high-quality synthetic datasets, it is particularly adept at handling long-context retrieval, reasoning tasks, and real-time inference, all manageable on a single GPU. Available through platforms such as Azure AI Foundry, NVIDIA API Catalog, and Hugging Face, Phi-4-mini-flash-reasoning empowers developers to create applications that are not only fast but also scalable and capable of intensive logical processing. This accessibility allows a broader range of developers to leverage its capabilities for innovative solutions.

The Nemotron 3 Nano stands out as the tiniest model within NVIDIA's Nemotron 3 lineup, specifically designed for agentic AI tasks that require robust reasoning and conversational skills while maintaining cost-effective inference. This hybrid Mamba-Transformer Mixture-of-Experts model boasts 3.2 billion active parameters, 3.6 billion when including embeddings, and a total of 31.6 billion parameters. NVIDIA asserts that this model offers greater accuracy compared to its predecessor, the Nemotron 2 Nano, all while utilizing less than half of the parameters during each forward pass, thus enhancing efficiency without compromising on performance. It is also claimed to surpass the accuracy of both GPT-OSS-20B and Qwen3-30B-A3B-Thinking-2507 across various widely-used benchmarks. With an 8K input and 16K output setting utilizing a single H200, the model achieves an inference throughput that is 3.3 times greater than that of Qwen3-30B-A3B and 2.2 times that of GPT-OSS-20B. Additionally, the Nemotron 3 Nano is capable of handling context lengths of up to 1 million tokens, further establishing its superiority over GPT-OSS-20B and Qwen3-30B-A3B-Instruct-2507. This remarkable combination of features positions it as a leading choice for advanced AI applications that demand both precision and efficiency.

Aion-1.0-Instruct is a newly introduced small language model available in Microsoft Edge as part of a developer preview aimed at early testing and gathering user feedback. This model has been specifically designed to enhance Edge’s on-device Prompt and Writing Assistance APIs, providing web developers with a quicker, smaller, and more efficient solution for AI-enhanced browser functionalities. Previously, Microsoft utilized Phi-4-mini for these APIs; however, its demanding hardware requirements restricted its accessibility on various devices. In contrast, Aion-1.0-Instruct broadens compatibility to a much larger array of devices, including those with less powerful GPUs and even those that rely solely on CPU inference without a GPU, all while maintaining high-quality performance across diverse web applications. Available in the Edge Canary and Dev channels, this model gives developers the opportunity to assess its performance in practical web environments, test the interoperability of APIs, and share their insights prior to final adjustments. Ultimately, Aion-1.0-Instruct is designed to empower developers to seamlessly integrate AI functionalities into their websites and browser extensions, promoting richer user experiences. Its potential impact on web development could greatly enhance the accessibility and usability of AI features for a broader audience.

SubQ 1.1 Small is the second iteration of Subquadratic’s long-context AI model, built to help enterprises solve problems that require reasoning across entire artifacts rather than isolated chunks. The model is designed for use cases involving large code repositories, document libraries, legal agreements, financial reports, contracts, and other complex information sets. Its Subquadratic Sparse Attention architecture reduces the compute burden of traditional dense attention, making it more practical to process multi-million-token contexts. SubQ 1.1 Small achieves near-perfect performance on needle-in-a-haystack retrieval tests up to 12M tokens, despite being trained primarily at 1M tokens. It also performs strongly on RULER, GPQA Diamond, LiveCodeBench, and AutomationBench Finance, showing a balance between long-context retrieval and general reasoning ability. At 1M tokens, the model uses 64.5x less compute than dense attention and runs 56x faster than FlashAttention-2 on a single attention layer. This efficiency makes long-context training and inference more scalable for enterprise AI applications. SubQ 1.1 Small is especially valuable for teams that need to analyze relationships across full documents, trace logic across codebases, or connect information across extensive collections. The model is intended to help organizations reduce dependence on complex retrieval workarounds and reason more directly over large-scale data.

Bonsai 27B stands as the latest multimodal flagship in the Bonsai lineup, marking the debut of a 27B-class model designed to operate on mobile devices. Built on the foundation of Qwen3.6 27B, it introduces an elevated level of capability for local devices, featuring advanced multi-step reasoning, structured tool interactions, vision tasks, and agentic loops for computer use that maintain coherence throughout multiple steps. The Bonsai 27B is available in two distinct variants. The Ternary Bonsai 27B employs ternary weights combined with FP16 group-wise scaling, achieving an effective weight of 1.71 bits and occupying a 5.9 GB footprint, suitable for high-performance laptop applications. In contrast, the 1-bit Bonsai 27B utilizes binary weights with identical group-wise scaling, resulting in an effective weight of 1.125 bits and a more compact 3.9 GB footprint, making it compatible with the memory constraints of devices like the iPhone 17 Pro. Both models operate seamlessly across the entire language network, including embeddings, attention mechanisms, MLPs, and the language model head, without resorting to higher-precision alternatives. They also feature a compact 4-bit vision tower, enabling on-device workflows to effectively interpret screenshots, documents, and camera inputs, enhancing user interaction and productivity. This innovative approach underscores Bonsai 27B's commitment to pushing the boundaries of mobile AI capabilities.

CodeGemma represents an impressive suite of efficient and versatile models capable of tackling numerous coding challenges, including middle code completion, code generation, natural language processing, mathematical reasoning, and following instructions. It features three distinct model types: a 7B pre-trained version designed for code completion and generation based on existing code snippets, a 7B variant fine-tuned for translating natural language queries into code and adhering to instructions, and an advanced 2B pre-trained model that offers code completion speeds up to twice as fast. Whether you're completing lines, developing functions, or crafting entire segments of code, CodeGemma supports your efforts, whether you're working in a local environment or leveraging Google Cloud capabilities. With training on an extensive dataset comprising 500 billion tokens predominantly in English, sourced from web content, mathematics, and programming languages, CodeGemma not only enhances the syntactical accuracy of generated code but also ensures its semantic relevance, thereby minimizing mistakes and streamlining the debugging process. This powerful tool continues to evolve, making coding more accessible and efficient for developers everywhere.

The o3-mini by OpenAI is a streamlined iteration of the sophisticated o3 AI model, delivering robust reasoning skills in a more compact and user-friendly format. It specializes in simplifying intricate instructions into digestible steps, making it particularly adept at coding, competitive programming, and tackling mathematical and scientific challenges. This smaller model maintains the same level of accuracy and logical reasoning as the larger version, while operating with lower computational demands, which is particularly advantageous in environments with limited resources. Furthermore, o3-mini incorporates inherent deliberative alignment, promoting safe, ethical, and context-sensitive decision-making. Its versatility makes it an invaluable resource for developers, researchers, and enterprises striving for an optimal mix of performance and efficiency in their projects. The combination of these features positions o3-mini as a significant tool in the evolving landscape of AI-driven solutions.

The o4-mini model, a more compact and efficient iteration of the o3 model, was developed to enhance reasoning capabilities and streamline performance. It excels in tasks requiring complex problem-solving, making it an ideal solution for users demanding more powerful AI. By refining its design, OpenAI has made significant strides in creating a model that balances efficiency with advanced capabilities. With this release, the o4-mini is poised to meet the growing need for smarter AI tools while maintaining the robust functionality of its predecessor. It plays a critical role in OpenAI’s ongoing efforts to push the boundaries of artificial intelligence ahead of the GPT-5 launch.

Llama (Large Language Model Meta AI) stands as a cutting-edge foundational large language model aimed at helping researchers push the boundaries of their work within this area of artificial intelligence. By providing smaller yet highly effective models like Llama, the research community can benefit even if they lack extensive infrastructure, thus promoting greater accessibility in this dynamic and rapidly evolving domain. Creating smaller foundational models such as Llama is advantageous in the landscape of large language models, as it demands significantly reduced computational power and resources, facilitating the testing of innovative methods, confirming existing research, and investigating new applications. These foundational models leverage extensive unlabeled datasets, making them exceptionally suitable for fine-tuning across a range of tasks. We are offering Llama in multiple sizes (7B, 13B, 33B, and 65B parameters), accompanied by a detailed Llama model card that outlines our development process while adhering to our commitment to Responsible AI principles. By making these resources available, we aim to empower a broader segment of the research community to engage with and contribute to advancements in AI.

OpenELM is a family of open-source language models created by Apple. By employing a layer-wise scaling approach, it effectively distributes parameters across the transformer model's layers, resulting in improved accuracy when compared to other open language models of a similar scale. This model is trained using datasets that are publicly accessible and is noted for achieving top-notch performance relative to its size. Furthermore, OpenELM represents a significant advancement in the pursuit of high-performing language models in the open-source community.

LTM-2-mini operates with a context of 100 million tokens, which is comparable to around 10 million lines of code or roughly 750 novels. This model employs a sequence-dimension algorithm that is approximately 1000 times more cost-effective per decoded token than the attention mechanism used in Llama 3.1 405B when handling a 100 million token context window. Furthermore, the disparity in memory usage is significantly greater; utilizing Llama 3.1 405B with a 100 million token context necessitates 638 H100 GPUs per user solely for maintaining a single 100 million token key-value cache. Conversely, LTM-2-mini requires only a minuscule portion of a single H100's high-bandwidth memory for the same context, demonstrating its efficiency. This substantial difference makes LTM-2-mini an appealing option for applications needing extensive context processing without the hefty resource demands.

The o3-mini-high model developed by OpenAI enhances artificial intelligence reasoning capabilities by improving deep problem-solving skills in areas such as programming, mathematics, and intricate tasks. This model incorporates adaptive thinking time and allows users to select from various reasoning modes—low, medium, and high—to tailor performance to the difficulty of the task at hand. Impressively, it surpasses the o1 series by an impressive 200 Elo points on Codeforces, providing exceptional efficiency at a reduced cost while ensuring both speed and precision in its operations. As a notable member of the o3 family, this model not only expands the frontiers of AI problem-solving but also remains user-friendly, offering a complimentary tier alongside increased limits for Plus subscribers, thereby making advanced AI more widely accessible. Its innovative design positions it as a significant tool for users looking to tackle challenging problems with enhanced support and adaptability.