ZTE-AIM/EmbodiedBrain-32B
VISIONPricing:Input $1.06 / Cached $0.053 / Output $2.6Concurrent Unit Cost:2Model Size:32BQuant:FP8Context Size:32kPublished:Oct 30, 2025License:apache-2.0Architecture:Transformer0.0K Open Weights Featherless Exclusive Cold
EmbodiedBrain-32B by ZTE-AIM is a 32 billion parameter language model with a 32768 token context window. This model is designed for general language understanding and generation tasks, offering a substantial capacity for complex reasoning and detailed responses. Its large parameter count and extensive context length make it suitable for applications requiring deep comprehension and coherent, long-form output.
Loading preview...
EmbodiedBrain-32B Overview
EmbodiedBrain-32B is a large language model developed by ZTE-AIM, featuring 32 billion parameters and a substantial context window of 32768 tokens. This model is engineered to handle a wide array of natural language processing tasks, leveraging its significant scale to process and generate intricate text.
Key Capabilities
- Extensive Context Understanding: With a 32768-token context length, the model can process and retain information from very long inputs, enabling more coherent and contextually relevant responses over extended conversations or documents.
- General Language Proficiency: Designed for broad applicability, it excels in tasks such as text generation, summarization, question answering, and translation.
- High Parameter Count: The 32 billion parameters contribute to enhanced reasoning abilities and a deeper understanding of linguistic nuances, leading to more sophisticated and accurate outputs.
Good for
- Complex Content Generation: Ideal for generating detailed articles, creative writing, or comprehensive reports where maintaining context and coherence over long passages is crucial.
- Advanced Conversational AI: Suitable for chatbots and virtual assistants that require deep contextual memory and the ability to engage in extended, nuanced dialogues.
- Research and Development: Provides a robust foundation for further fine-tuning on specialized datasets for domain-specific applications requiring high linguistic fidelity.