Kimi-K2-Thinking via Hugging Face
Specifications
Context Window
262,144 tokens
Release Date
2025-11-06
Capabilities
ReasoningTool callingTemperature
Availability
Open Weights
Model Overview
Hugging Face is the leading open-source AI community and platform. Their Inference API provides easy access to thousands of models, and they maintain the most popular repository of pre-trained AI models.
Kimi-K2-Thinking is a kimi-thinking-family model by Hugging Face with a 262k token context window and up to 262k output tokens. It is priced at $0.6000/1M input tokens and $2.50/1M output tokens.
Key capabilities include: reasoning, tool calling, temperature. It supports advanced reasoning for complex multi-step tasks. It can call external tools and functions for agentic workflows.
Details
ProviderHugging Face
Model IDmoonshotai/Kimi-K2-Thinking
Familykimi-thinking
Release Date2025-11-06
Last Updated2025-11-06
Knowledge Cutoff2024-08
Context Window262,144 tokens
Max Output262,144 tokens
Input Cost / 1M$0.6000
Output Cost / 1M$2.50
Cache Read / 1M$0.1500
More models from Hugging Face
Frequently Asked Questions
How much does Kimi-K2-Thinking cost to use?
Kimi-K2-Thinking is priced at $0.6000/1M input tokens and $2.50/1M output tokens. Use the cost estimator on this page to calculate your expected spend based on your usage pattern.
What is a token and how does it relate to pricing?
A token is a chunk of text — roughly ¾ of a word in English. For example, "chatbot" is two tokens. LLM API pricing is based on the number of tokens you send (input) and receive (output). Input tokens include your prompts, uploaded documents, and images, while output tokens are the model's generated responses.
Why are input and output tokens priced differently?
LLM providers charge separately for input and output tokens. Output tokens are typically more expensive because generating each token requires more compute — the model must run a full forward pass for every token it produces, while input tokens are processed in parallel.
What is the context window of Kimi-K2-Thinking?
Kimi-K2-Thinking supports a context window of 262,144 tokens. This is the maximum number of tokens (input + output combined) the model can process in a single request. Larger context windows let you send longer documents or maintain longer conversation histories.
How accurate is this cost estimation?
This tool provides a ballpark estimate based on per-token pricing. Actual costs may differ due to prompt caching, batched API calls, volume discounts, reasoning token overhead, and provider-specific billing rules. Use it for budgeting and comparison, not as an invoice prediction.
How does Kimi-K2-Thinking pricing compare to other models?
You can compare Kimi-K2-Thinking with other models on our LLM API pricing calculator. Use the cost estimator to see side-by-side cost breakdowns across different providers and models to find the best fit for your budget and requirements.
What factors affect my total API cost?
Your total cost depends on several factors: the number of API calls you make, the length of your prompts (input tokens), the length of generated responses (output tokens), whether you use features like image or document uploads (which add input tokens), and any provider-specific charges for caching or batch processing.
How can my team use Kimi-K2-Thinking via API?
You can connect your own Hugging Face API key and give your entire team access to Kimi-K2-Thinking through TypingMind Teams. It lets you build a unified AI workspace where team members can use Kimi-K2-Thinking and other models — without needing their own API keys. You stay in control of usage limits, costs, and permissions, all from a single dashboard.
