Kimi-K2.5

📰   Tech Blog     |     📄   Paper 0. Changelog 2026.1.29: - The default system prompt might cause confusion to users and unexpected behaviours, so we remove it. - The token ` ` is incorrect; it has been replaced with ` ` in the chat template. 1. Model Introduction Kimi K2.5 is an open-source, native multimodal agentic model built through continual pretraining on approximately 15 trillion mixed visual and text tokens atop Kimi-K2-Base. It seamlessly integrates vision and language understanding with advanced agentic capabilities, instant and thinking modes, as well as conversational and agentic paradigms. Key Features **Native Multimodality**: Pre-trained on vision–language tokens, K2.5 excels in visual knowledge, cross-modal reasoning, and agentic tool use grounded in visual inputs. **Coding with Vision**: K2.5 generates code from visual specifications (UI designs, video workflows) and autonomously orchestrates tools for visual data processing. **Agent Swarm**: K2.5 transitions from single-agent scaling to a self-directed, coordinated swarm-like execution scheme. It decomposes complex tasks into parallel sub-tasks executed by dynamically instantiated, domain-specific agents. 2. Model Summary | | | |:---:|:---:| | **Architecture** | Mixture-of-Experts (MoE) | | **Total Parameters** | 1T | | **Activated Parameters** | 32B | | **Number of Layers** (Dense layer included) | 61 | | **Number of Dense Layers** | 1 | | **Attention Hidden Dimension** | 7168 | | **MoE Hidden Dimension** (per Expert) | 2048 | | **Number of Attention Heads** | 64 | | **Number of Experts** | 384 | | **Selected Experts per Token** | 8 | | **Number of Shared Experts** | 1 | | **Vocabulary Size** | 160K | | **Context Length** | 256K | | **Attention Mechanism** | MLA | | **Activation Function** | SwiGLU | | **Vision Encoder** | MoonViT | | **Parameters of Vision Encoder** | 400M | 3. Evaluation Results Benchmark Kimi K2.5 (Thinking) GPT-5.2 (xhigh) Cla