Kimi API K2 Thinking — Reasoning Model Guide
Quick Answer
The Kimi API includes advanced reasoning models designed for logical and mathematical problem-solving. Models such as kimi-k2-5-thinking and kimi-k2-thinking generate a chain-of-thought reasoning process before outputting responses. Pricing for the kimi k2.5 thinking api starts at $3.50 per million input tokens and $10.50 per million output tokens, supporting a 128K token context window.
What is Kimi API K2 Thinking
The kimi k2 thinking api is a specialized API mode that accesses Moonshot AI's reasoning-focused large language models. Unlike standard conversational models that respond immediately, the kimi k2.5 thinking api performs step-by-step calculations and internal self-correction.
This makes the reasoning models highly effective for complex tasks such as:
- Advanced code generation and logical debugging
- Complex mathematical calculations
- Multi-step logic problems and analytical research
- Structured analysis of long legal or academic documents
How Kimi API K2 Thinking Works
When you send a prompt to a kimi k2 thinking api model, the reasoning engine performs a multi-phase generation:
- Analysis & Decomposition: The model breaks down the prompt into smaller logical sub-tasks.
- Chain-of-Thought Generation: The model performs reasoning tokens step-by-step.
- Self-Correction: If the model identifies a logical error during reasoning, it corrects itself before continuing.
- Final Output Delivery: The final synthesized answer is output to the user.
The thought process is delivered in the API payload, either embedded inside reasoning metadata or output inside XML tags.
How to Access Kimi API K2 Thinking
Accessing the reasoning models is identical to calling standard models, but
requires specifying the correct model ID: kimi-k2-5-thinking.
import openai
client = openai.OpenAI(
api_key="your_kimi_api_key_here",
base_url="https://api.moonshot.cn/v1"
)
response = client.chat.completions.create(
model="kimi-k2-5-thinking",
messages=[
{"role": "user", "content": "Solve this puzzle: If 5 cats catch 5 mice in 5 minutes, how many cats are needed to catch 100 mice in 100 minutes?"}
],
temperature=0.1
)
# Accessing final output
print("Output:", response.choices[0].message.content)Standard vs Thinking Models
Understanding the differences in costs and latency is vital before choosing standard vs thinking options.
| Metric / Feature | Standard (kimi-k2-5) | Thinking (kimi-k2-5-thinking) |
|---|---|---|
| Input cost per 1M tokens | $2.50 | $3.50 |
| Output cost per 1M tokens | $7.50 | $10.50 |
| Average Latency | Low (1-3 seconds) | Medium-High (5-15 seconds) |
| Logical Accuracy | Standard | High (Superior for math/reasoning) |
| Thought Visualization | No | Yes (Inside response details) |
Frequently Asked Questions About Kimi API
Frequently Asked Questions About Kimi API
What are the reasoning models in Kimi API?
The reasoning models include kimi-k2-5-thinking and kimi-k2-thinking. These models generate internal reasoning steps before returning the final text answer.
How does Kimi Thinking mode affect response latency?
Response latency is higher because the model generates internal reasoning tokens. For simple questions, standard models are faster. For complex mathematical or coding problems, thinking models are recommended.
Does Kimi API charge for reasoning tokens?
Yes, reasoning tokens are billed at the standard output token rate. It is important to account for reasoning tokens when budgeting API expenses.
Can I hide the reasoning thought process in Kimi API?
By default, the API returns the thought process block or wraps reasoning tokens. Depending on your configuration or SDK settings, you can choose to strip the thought output before showing it to the end user.
What is the context limit of Kimi K2.5 Thinking API?
The model supports a full 128,000 token context window, similar to the standard non-reasoning versions of Kimi API models.
Conclusion
The kimi k2 thinking api is an invaluable tool for developers building applications that require high precision and structural reasoning. While the latency and output token counts are higher, the resulting logical accuracy justifies the cost for complex problem spaces. Check our Kimi API Pricing plans for standard comparisons, or see the Models Hub to evaluate other models.