Meta Llama 3.3 is a multilingual large language model (LLM) with 70 billion parameters (text input/text output), featuring pre-training and instruction-tuning. The instruction-tuned pure text model of Llama 3.3 is optimized for multilingual conversational use cases and outperforms many available open-source and closed chat models on common industry benchmarks.

Llama 3.2 is designed to handle tasks that combine visual and textual data. It excels in tasks such as image description and visual question answering, bridging the gap between language generation and visual reasoning.

Llama 3.2 is designed to handle tasks that combine visual and textual data. It excels in tasks such as image description and visual question answering, bridging the gap between language generation and visual reasoning.

Llama 3.1 8B is a high-performance model that offers rapid text generation capabilities, making it ideal for applications requiring large-scale efficiency and cost-effectiveness.

Llama 3.1 70B provides enhanced AI reasoning capabilities, suitable for complex applications, supporting extensive computational processing while ensuring efficiency and accuracy.

Llama 3 Groq 8B Tool Use is a model optimized for efficient tool usage, supporting fast parallel computation.

Llama 3 Groq 70B Tool Use offers powerful tool invocation capabilities, supporting efficient processing of complex tasks.

Meta Llama 3 8B delivers high-quality reasoning performance, suitable for diverse application needs.

Meta Llama 3 70B provides unparalleled complexity handling capabilities, tailored for high-demand projects.

Gemma 2 9B is a model optimized for specific tasks and tool integration.

Gemma 7B is suitable for medium to small-scale task processing, offering cost-effectiveness.

Mixtral 8x7B provides high fault-tolerant parallel computing capabilities, suitable for complex tasks.

LLaVA 1.5 7B offers integrated visual processing capabilities, generating complex outputs from visual information inputs.