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Very High Reasoning

Kimi K2 Thinking

Kimi K2 Thinking is the leading open-weights AI model with 1T parameters and 32B active, topping intelligence benchmarks at 67 and excelling in agentic tasks like 93% on τ²-Bench Telecom. This thinking agent masters complex reasoning, 200-300 sequential tool calls, PhD-level math, coding, and web search—delivering autonomous power at a fraction of proprietary costs.

256k Context
Very High Intelligence
Unknown The search results do not specify a knowledge cutoff date for Kimi K2 Thinking. Source explicitly states "Neither model specifies a knowledge cutoff date." Knowledge

Available for Chat, Vision, and File Uploads.

How do you want to interact?

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Ask anything.
Have a natural conversation, brainstorm ideas, draft emails, or ask for advice.

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Use a Persona

Specialized Experts.
Instruct the AI to act as a Coding Tutor, Marketing Expert, or Travel Guide.

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Why use Kimi K2 Thinking?

Complex Reasoning

Excels in multi-step reasoning for math, science, logic puzzles, and benchmarks like HLE and GPQA

Agentic Tool Use

Handles 200-300 sequential tool calls with stable performance for research, workflows, and automation

Advanced Coding

Solve complex software tasks, understand large codebases, generate patches, and debug issues on SWE-bench

Capability Examples

Complex Research Task
Research the latest advancements in quantum computing and summarize key breakthroughs from the past year.
Step 1: Query search tools for "quantum computing breakthroughs 2025-2026". Retrieved 15 papers; key ones: IBM's 1,000-qubit processor (Nature, Jan 2026) and Google's error-corrected logical qubit (Science, Mar 2026). Step 2: Cross-verify with academic databases. Confirmed scalability claims. Step 3: Analyze implications. Enables practical algorithms like Shor's. Summary: Major leap in qubit count and error rates, poised for crypto disruption.
Multi-Step Coding Challenge
Write a Python script to scrape stock data, analyze trends with ML, and generate a trading signal. Use libraries: requests, pandas, scikit-learn.
Think: Need API for stocks (Alpha Vantage), fetch data, preprocess, train simple regressor, predict signal. Tool 1: Call code interpreter to install libs. Done. Tool 2: Fetch data for AAPL. Got OHLCV. Tool 3: Train model. RMSE 2.1; buy signal if pred > current +1%. Output script: [concise 50-line code with comments]. Runs autonomously.

How to use

1
Go to Chat

Navigate to the "AI Chat" page.

2
Select Model

Ensure Kimi K2 Thinking is selected.

3
Type Prompt

Ask a question or paste code.

4
Interact

Refine the answer by replying to the AI.

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