AI Model Catalogue

This 17-billion parameter mixture-of-experts model with 128 experts, optimized for instruction-following tasks with an expansive 1 million token context window, serves developers and organizations needing cost-effective long-document processing and complex reasoning while maintaining high performance across diverse applications.

A 17-billion parameter model with 128 experts and 262K token context window designed for developers and researchers who need efficient long-document processing, complex reasoning, and cost-effective inference across diverse language tasks.

A large language model with 164K token context designed for researchers, developers, and enterprises requiring advanced reasoning, long-document analysis, and complex problem-solving across technical, creative, and analytical domains.

This advanced large language model excels at complex reasoning, multi-turn conversations, and processing extensive documents up to 131K tokens, making it ideal for researchers, developers, and enterprises requiring sophisticated natural language understanding for tasks like code generation, analysis, and knowledge-intensive applications.

A compact yet capable language model optimized for efficient inference and real-time applications, designed for developers and organizations seeking fast, cost-effective AI solutions without sacrificing reasoning quality across a 131K token context window.

This instruction-following model with 262K token context excels at handling long documents, complex reasoning tasks, and detailed conversations, making it ideal for developers and enterprises needing efficient processing of extended content without sacrificing performance.

This lightweight 1.5 billion parameter language model excels at efficient text generation, summarization, and question-answering tasks with a 131K token context window, making it ideal for developers and organizations seeking fast inference on resource-constrained devices while maintaining reasonable quality across general-purpose NLP applications.

A compact yet capable language model with 197K token context window designed for developers and businesses seeking efficient performance across coding, analysis, and content generation tasks without requiring substantial computational resources.

A lightweight 3-billion-parameter language model optimized for fast inference and low resource consumption, ideal for developers and organizations seeking to deploy efficient AI applications on edge devices, mobile platforms, or cost-constrained environments while maintaining reasonable performance across general-purpose NLP tasks.

A lightweight 1-billion parameter language model optimized for efficient inference on resource-constrained devices and edge deployment, ideal for developers and organizations seeking to run conversational AI locally without requiring expensive hardware while maintaining a 131K token context window for handling longer documents and conversations.

Qwen3-235B-A22B-Thinking-2507 is a high-performance, open-weight Mixture-of-Experts (MoE) language model optimized for complex reasoning tasks. It activates 22B of its 235B parameters per forward pass and natively supports up to 262,144...

Qwen3-Coder-30B-A3B-Instruct is a 30.5B parameter Mixture-of-Experts (MoE) model with 128 experts (8 active per forward pass), designed for advanced code generation, repository-scale understanding, and agentic tool use. Built on the...

Qwen3-Next-80B-A3B-Thinking is a reasoning-first chat model in the Qwen3-Next line that outputs structured “thinking” traces by default. It’s designed for hard multi-step problems; math proofs, code synthesis/debugging, logic, and agentic...

Qwen3-VL-8B-Instruct is a multimodal vision-language model from the Qwen3-VL series, built for high-fidelity understanding and reasoning across text, images, and video. It features improved multimodal fusion with Interleaved-MRoPE for long-horizon...

A 70-billion parameter language model with a 131K token context window, optimized for enterprise applications requiring extended document analysis, complex reasoning, and nuanced understanding across legal, financial, and technical domains.

Llama Guard 4 is a Llama 4 Scout-derived multimodal pretrained model, fine-tuned for content safety classification. Similar to previous versions, it can be used to classify content in both LLM...

Qwen3-VL-32B-Instruct is a large-scale multimodal vision-language model designed for high-precision understanding and reasoning across text, images, and video. With 32 billion parameters, it combines deep visual perception with advanced text...