Kimi K2 Thinking, released by MoonshotAI on November 6, 2025, is an advanced reasoning model designed for complex tasks. It features a massive context window of 256k tokens, allowing it to handle extensive input and output in text format. This model excels in persistent, step-by-step reasoning, making it ideal for tasks that require dynamic tool invocation and complex workflows. With its ability to maintain stable multi-agent behavior through numerous tool calls, Kimi K2 Thinking is optimized for autonomous research, coding, and writing, supporting a wide range of applications in analytical tasks.
Use Cases
Here are a few ways teams apply MoonshotAI: Kimi K2 Thinking in practice—from fast drafting to multimodal understanding. Adapt these ideas to your workflow.
Autonomous research across diverse subjects
Dynamic coding assistance for developers
Writing support for long-form content creation
Complex analytical task management
Real-time tool invocation for enhanced productivity
Key Features
A quick look at the capabilities that make this model useful in real projects.
Trillion-parameter Mixture-of-Experts architecture
256k-token context window for extensive tasks
Supports step-by-step reasoning and tool use
Optimized for complex workflows and multi-agent behavior
High inference efficiency for demanding tasks
Specs
Overview
Vendor
moonshotai
Model ID
moonshotai/kimi-k2-thinking
Release
2025-11-06
Modalities & context
Input
text
Output
text
Context
262,144 tokens
Parameters & defaults
Supported parameters: frequency_penalty, include_reasoning, max_tokens, presence_penalty, reasoning, response_format, stop, structured_outputs, temperature, tool_choice, tools, top_p
Defaults: temperature 0.2, top_p 0.95
Benchmark tests: MoonshotAI: Kimi K2 Thinking
We ran this model against a few representative prompts to show its range. Review the outputs below and be the judge.
Text
Prompt:
Write 150 words on how AI might positively upend work, leisure and creativity
The MoonshotAI Kimi K2 Thinking model is designed for advanced natural language processing tasks, leveraging deep learning techniques to understand and generate human-like text. It is capable of performing a variety of functions, including text summarization, sentiment analysis, and conversational AI, making it suitable for applications in customer support, content creation, and data analysis. The model can process large volumes of text data efficiently, enabling users to extract insights and automate responses in real-time.
However, users should be aware of certain constraints, such as potential biases in generated content based on training data and limitations in understanding context in complex conversations. Additionally, the model may require fine-tuning for specific industry applications to optimize performance. Overall, the Kimi K2 Thinking model offers a versatile solution for organizations seeking to enhance their text-based workflows.
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