AI Model Catalogue

Gemma 4 26B A4B IT is an instruction-tuned Mixture-of-Experts (MoE) model from Google DeepMind. Despite 25.2B total parameters, only 3.8B activate per token during inference — delivering near-31B quality at...

Qwen3-Next-80B-A3B-Instruct is an instruction-tuned chat model in the Qwen3-Next series optimized for fast, stable responses without “thinking” traces. It targets complex tasks across reasoning, code generation, knowledge QA, and multilingual...

This advanced language model excels at complex reasoning, long-form content generation, and nuanced analysis tasks, making it ideal for professionals, researchers, and developers requiring high-quality outputs for demanding applications.

This optical character recognition model excels at extracting and digitizing text from documents and images, making it ideal for researchers, developers, and organizations needing to convert scanned papers, PDFs, and photographs into machine-readable text.

This is a high-performance 70-billion parameter language model optimized for complex reasoning tasks and long-form content generation, ideal for developers and enterprises requiring advanced inference capabilities without the computational overhead of larger models.

This advanced multimodal vision-language model excels at understanding and analyzing images alongside text, making it ideal for developers and enterprises needing sophisticated visual reasoning, document processing, and image-based question-answering capabilities.

This fine-tuned 70-billion parameter language model excels at creative writing, roleplay, and narrative generation tasks, making it ideal for authors, game developers, and creative professionals seeking high-quality, imaginative text outputs with minimal safety restrictions.

This advanced image editing model excels at precise object removal, inpainting, and content-aware modifications, making it ideal for designers, photographers, and content creators who need professional-quality image manipulation without manual editing.

A compact multimodal language model optimized for fast inference and lower computational costs, ideal for developers and organizations seeking efficient text and image understanding capabilities without requiring enterprise-grade hardware.

This instruction-tuned 8 billion parameter language model excels at following user directions for tasks like question answering, summarization, and creative writing, making it ideal for developers and organizations seeking a fast, cost-effective alternative to larger models without sacrificing quality on most general-purpose NLP applications.

This lightweight sentence embedding model excels at converting text into dense vector representations for semantic similarity tasks, making it ideal for developers and researchers needing fast, efficient paraphrase detection, clustering, and semantic search with minimal computational overhead.

This lightweight sentence embedding model excels at converting text into dense vector representations for semantic similarity tasks, making it ideal for developers and researchers needing efficient retrieval, clustering, and matching capabilities with minimal computational overhead.

This lightweight 12 billion parameter vision-language model excels at multimodal tasks combining image and text understanding, making it ideal for developers and organizations seeking efficient inference on resource-constrained hardware without sacrificing capability.