South Korean Tech Firm Kakao Releases Open-Source Decision Model Kanana-2-v-decide: Outputs Probabilities Instead of Text for AI Agents

South Korean technology company Kakao has released Kanana-2-v-decide, an open-source multimodal decision model designed specifically for AI agents. Unlike standard Large Language Models (LLMs) that generate text responses requiring further parsing, Kanana-2-v-decide outputs structured data such as probabilities, choices, or scores directly. This allows AI systems to bypass the step of extracting conclusions from natural language, enabling faster execution of subsequent actions.

The model is built upon Kakao's multimodal language model Kanana-2-v-3b and utilizes an embedding-based architecture. It converts questions and options into vectors to determine similarity, which reduces computational load and response time for repeated requests. Kakao states that the model responds within an average of 0.04 seconds on a single H200 GPU, approximately 43 times faster than OpenAI's GPT-6 Luna.

In terms of accuracy, Kakao evaluated the model using its proprietary 'Kanana Decision Bench' (15,909 Korean items) and the public 'Decision Index'. The company reports that Kanana-2-v-decide achieved the highest scores among compared models, including Microsoft's Decision-1, TypeSafe AI's Jev, Perplexity's pplx-decider-v1.1-27B, and Upstage's Solar Decide. These performance metrics are self-reported by Kakao and have not yet been independently verified.

The model is available under the 'Kakao Open License', permitting both research and commercial use. Kakao plans to integrate it into internal projects such as web search and product category classification before broader deployment. The release addresses a growing need in the AI industry for specialized 'decision-only' models that can handle the rapid, structured judgments required by autonomous agents, distinct from the generative capabilities of general-purpose LLMs.