OpenKLO/kokolok-1.0-flash

TEXT GENERATIONPricing:Input $0.32 / Cached $0.064 / Output $1.6Concurrent Unit Cost:1Model Size:3.1BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Aug 11, 2026Architecture:Transformer0.0K Featherless Exclusive Cold

OpenKLO/kokolok-1.0-flash is a 3.1 billion parameter language model developed by OpenKLO, featuring a 32768-token context length. This model is designed for general language understanding and generation tasks, providing a balance between performance and computational efficiency. Its architecture is optimized for rapid inference, making it suitable for applications requiring quick responses.

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Model Overview

OpenKLO/kokolok-1.0-flash is a 3.1 billion parameter language model developed by OpenKLO. It is characterized by its substantial 32768-token context window, which allows it to process and generate longer sequences of text while maintaining coherence and understanding. The model is built for efficient performance, aiming to provide a capable solution for various natural language processing tasks.

Key Capabilities

  • General Language Understanding: Processes and interprets diverse textual inputs.
  • Text Generation: Capable of generating coherent and contextually relevant text.
  • Extended Context Handling: Benefits from a 32768-token context length for complex, multi-turn interactions or long document analysis.
  • Optimized for Speed: Designed with a "flash" designation, indicating a focus on rapid inference and deployment.

Use Cases

This model is suitable for applications where a balance of performance, context handling, and inference speed is crucial. Potential use cases include:

  • Chatbots and Conversational AI: Leveraging the extended context for more natural and informed dialogues.
  • Content Creation: Generating articles, summaries, or creative text based on prompts.
  • Code Assistance: Potentially aiding in code completion or explanation, given its general language capabilities.
  • Research and Development: Serving as a foundational model for further fine-tuning on specific domain tasks.