Search Results for "structured text" - Page 14

...It supports dozens of languages, adheres reliably to system prompts, and provides native function calling and structured JSON output—key capabilities for agentic and automation workflows. The model also includes a 256k context window, allowing it to handle long documents and extended reasoning chains.

...Its multilingual support spans dozens of major languages, making it suitable for global, multilingual, and localized AI applications. The model’s architecture provides native function calling, structured JSON outputs, and reliable tool-use behavior essential for agentic automation. Overall, it delivers a powerful blend of

Kimi K2.6 is an open-source native multimodal agentic model built for advanced autonomous execution, long-horizon coding, and large-scale task orchestration. It is designed to handle complex end-to-end software workflows across multiple languages and domains, including front-end development, DevOps, performance optimization, and coding-driven design. Beyond coding, it can transform prompts and visual inputs into production-ready interfaces and lightweight full-stack outputs with structured...

Ministral 3 8B Instruct 2512 is a balanced, efficient model in the Ministral 3 family, offering strong multimodal capabilities within a compact footprint. It combines an 8.4B-parameter language model with a 0.4B vision encoder, enabling both text reasoning and image understanding. This FP8 instruct-fine-tuned variant is optimized for chat, instruction following, and structured outputs, making it ideal for daily assistant tasks and lightweight agentic workflows. Designed for edge deployment, the model can run on a wide range of hardware and fits locally on a single 12GB GPU, with the option for even smaller quantized configurations. ...

Ministral 3 14B Reasoning 2512 is the largest model in the Ministral 3 series, delivering frontier-level performance with capabilities comparable to the Mistral Small 3.2 24B model. It pairs a 13.5B-parameter language model with a 0.4B vision encoder, enabling strong multimodal reasoning across both text and images. This version is specifically post-trained for reasoning tasks, making it highly effective for math, coding, STEM workloads, and complex multi-step problem-solving. Despite its...

Ministral 3 3B Instruct 2512 is the smallest model in the Ministral 3 family, offering a lightweight yet capable multimodal architecture designed for edge and low-resource deployments. It includes a 3.4B-parameter language model paired with a 0.4B vision encoder, enabling it to understand both text and visual inputs. As an FP8 instruct-fine-tuned model, it is optimized for chat, instruction following, and compact agentic tasks while maintaining strong adherence to system prompts. Despite its...

...As the instruct-tuned FP8 variant, it is optimized for reliable instruction following, agentic workflows, production-grade assistants, and long-context enterprise tasks. It incorporates a massive 673B-parameter language MoE backbone and a 2.5B-parameter vision encoder, enabling rich multimodal understanding across text and images. The model supports dozens of languages and maintains strong system-prompt adherence, making it suitable for global and structured enterprise use. Designed for high performance, it runs on a single node of B200 or H200 GPUs in FP8, and can also operate in NVFP4 mode on H100 or A100 hardware. With a 256k context window, it excels at long-document comprehension, deep retrieval workflows, and complex knowledge-intensive tasks.