Kimi K2.5

Overview

Kimi K2.5 is MoonshotAI's flagship multimodal large language model, featuring a 1 trillion parameter Mixture-of-Experts (MoE) architecture with 32 billion parameters activated per token. Released in January 2025, it represents a significant advancement in multimodal reasoning, code generation, and agentic capabilities.

The model supports a 256K token context window with up to 33K tokens of output per completion, making it well-suited for complex document analysis, large codebase understanding, and multi-step reasoning tasks. Its MoE architecture enables efficient inference by activating only the most relevant expert modules for each token, delivering strong performance while maintaining reasonable computational costs.

Kimi K2.5 is trained on approximately 15 trillion tokens of mixed visual and text data, enabling native multimodal understanding across text, images, and experimental video input. The model excels at cross-modal reasoning tasks and can generate code from visual inputs such as UI screenshots.

Capabilities

• 1T parameter MoE architecture with 32B activated parameters per token across 61 layers
• 256K token context window with up to 33K output tokens for long-form generation
• Native multimodal support for text and image inputs, with experimental video support
• Multi-head Latent Attention (MLA) for efficient long-context processing
• Agent Swarm capability for autonomous task decomposition and parallel sub-task execution
• Dual modes including instant (fast) and thinking (step-by-step reasoning) modes

Strengths

• Strong performance on complex coding and reasoning benchmarks
• Native vision-language understanding trained on mixed modality data
• Efficient MoE architecture balances performance and inference cost
• Supports both single-agent and multi-agent workflow configurations
• OpenAI-compatible API format for easy integration
• Competitive pricing at $0.50/M input and $2.80/M output tokens

Limitations and Considerations

• Large model requires significant compute resources for self-hosting
• MoE architecture complexity may affect deployment in constrained environments
• Video input support is experimental and may have limitations
• For simple tasks, smaller models may offer better cost efficiency

Best Use Cases

Kimi K2.5 is ideal for:

• Complex agentic workflows with autonomous task decomposition
• Multimodal reasoning across text and images
• Code generation from visual inputs (UI screenshots, diagrams)
• Large document analysis and synthesis
• Multi-step research and analytical tasks
• Enterprise applications requiring long-context understanding