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Kimi K2 Thinking

Kimi K2 Thinking via Amazon Bedrock

Specifications

Context Window

262,143 tokens

Release Date

2025-12-02

Capabilities

ReasoningTool callingStructured outputTemperature

Availability

Open Weights

Model Overview

Amazon Bedrock is AWS's fully managed service for building generative AI applications. It provides access to foundation models from Amazon (Nova, Titan), Anthropic, Meta, Mistral, and others through a single API.

Kimi K2 Thinking is a kimi-thinking-family model by Amazon Bedrock with a 262k token context window and up to 16k output tokens. It is priced at $0.6000/1M input tokens and $2.50/1M output tokens.

Key capabilities include: reasoning, tool calling, structured output, temperature. It supports advanced reasoning for complex multi-step tasks. It can call external tools and functions for agentic workflows.

Details

ProviderAmazon Bedrock
Model IDmoonshot.kimi-k2-thinking
Familykimi-thinking
Release Date2025-12-02
Last Updated2025-12-02
Knowledge CutoffN/A
Context Window262,143 tokens
Max Output16,000 tokens
Input Cost / 1M$0.6000
Output Cost / 1M$2.50

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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 262,143 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 Amazon Bedrock 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.

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