Kimi K2.5 Thinking Mode Complete Tutorial: 3 Steps to Enable Deep Reasoning

Author's Note: A detailed guide on how to invoke kimi-k2.5 via the APIYI platform, enable the enable_thinking parameter, and enjoy stable pricing at less than 80% of the official rate. Includes complete sample code for curl, Python, and JavaScript.

The thinking mode of Kimi K2.5 is currently one of the most powerful reasoning capabilities among open-weights models, boasting an AIME 2025 math benchmark score of 96.1%. However, many developers run into the same issue when integrating it: the model doesn't output its thought process after the API call.

This happens because you need to manually pass the "enable_thinking": true parameter to the APIYI platform to activate the thinking mode. In this article, we'll walk you through the complete configuration for integrating Kimi K2.5's thinking mode from scratch.

🎯 Key Takeaway: By the end of this guide, you'll know how to fully invoke the kimi-k2.5 thinking mode and how to use this capability reliably via APIYI at a price point lower than 80% of the official rate.

Core Highlights of Kimi K2.5 Thinking Mode

💡 Quick Start: Register an account at APIYI (apiyi.com), top up your balance, and you're ready to invoke kimi-k2.5. It supports OpenAI-compatible interfaces, so there's no need to change your existing code framework.

What is Kimi K2.5: The 1 Trillion Parameter Open-Source Inference Flagship

Released by Moonshot AI on January 27, 2026, Kimi K2.5 stands as one of the most powerful multimodal Large Language Models in the open-source community today.

Kimi K2.5 Core Architecture Specifications

Kimi K2.5 utilizes Multi-Head Latent Attention (MLA) and a 384-expert MoE structure. By maintaining a total of 1 trillion parameters while only activating 32 billion during inference, it achieves an optimal balance between performance and cost.

Four Operating Modes of Kimi K2.5

K2.5 Instant      → Rapid response, no thinking process, suitable for simple tasks
K2.5 Thinking     → Deep reasoning, outputs reasoning_content, suitable for complex problems
K2.5 Agent        → Autonomous task execution, tool calling capability
K2.5 Agent Swarm  → Multi-agent collaboration, up to 100 sub-agents in parallel

The APIYI platform currently supports K2.5 Thinking mode, which can be activated via the enable_thinking: true parameter to output the complete reasoning chain.

💡 Usage Tip: We recommend accessing kimi-k2.5 through APIYI (apiyi.com). It provides a stable, official Alibaba Cloud proxy link, so you don't have to worry about service interruptions.

Kimi K2.5 Performance Benchmarks: Thinking Mode Real-World Data

Once you enable the thinking mode, the reasoning performance of Kimi K2.5 sees a massive boost. Here are the key benchmark results:

Key Benchmark Scores

Note: The data above is sourced from the Artificial Analysis Intelligence Index, January 2026 evaluation results.

Compared to the standard mode, the thinking mode delivers a 30-50% significant improvement in complex math, multi-step reasoning, and code generation tasks. The trade-off is that token consumption is about 2-4 times higher than the standard mode, so keeping a close eye on max_tokens is key to controlling costs.

3 Steps to Enable Kimi K2.5 Thinking Mode on APIYI

Step 1: Register and Get Your API Key

Visit the APIYI official website at apiyi.com to register your account and complete the following:

1. Register and verify your email address.
2. Go to "Console" → "API Key Management".
3. Create a new API Key and save it securely.

🎯 Pricing Advantage: Get a $10 bonus when you top up $100. With group pricing at 0.88 (input tokens), your actual usage cost is significantly lower than 80% of the official Kimi pricing. APIYI provides stable routes comparable to Alibaba Cloud's official transit, ensuring enterprise-grade reliability.

Step 2: Configure Request Parameters

To activate the Kimi K2.5 thinking mode, you need to configure these three parameters:

{
  "model": "kimi-k2.5",
  "enable_thinking": true,
  "temperature": 1.0,
  "max_tokens": 16000
}

⚠️ Important Note: The parameter logic on the APIYI platform differs from the official Kimi API:

• Official Kimi: Thinking is enabled by default; you must pass a parameter to disable it.
• APIYI Platform: You must manually pass "enable_thinking": true to activate it.

Step 3: Send Requests and Parse Thinking Content

Below is a complete example of how to trigger the thinking mode and parse the response.

curl Example (Fastest way to verify)

curl --location 'https://api.apiyi.com/v1/chat/completions' \
--header "Authorization: Bearer sk-YOUR_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "model": "kimi-k2.5",
    "messages": [
        {
            "role": "system",
            "content": "You are a helpful assistant."
        },
        {
            "role": "user",
            "content": "Explain step-by-step: Why does 0.1 + 0.2 not equal 0.3 in computing?"
        }
    ],
    "enable_thinking": true,
    "temperature": 1.0,
    "max_tokens": 16000
}'

Python Example (Recommended for production)

from openai import OpenAI

client = OpenAI(
    api_key="sk-YOUR_API_KEY",
    base_url="https://api.apiyi.com/v1"
)

response = client.chat.completions.create(
    model="kimi-k2.5",
    messages=[
        {
            "role": "system",
            "content": "You are a helpful assistant."
        },
        {
            "role": "user",
            "content": "Analyze the time complexity of this code and provide optimization suggestions:\n\ndef find_duplicates(arr):\n    result = []\n    for i in range(len(arr)):\n        for j in range(i+1, len(arr)):\n            if arr[i] == arr[j] and arr[i] not in result:\n                result.append(arr[i])\n    return result"
        }
    ],
    extra_body={
        "enable_thinking": True
    },
    temperature=1.0,
    max_tokens=16000
)

# Parse thinking content (if present)
message = response.choices[0].message

# Output the reasoning process (reasoning_content field)
if hasattr(message, 'reasoning_content') and message.reasoning_content:
    print("=== Thinking Process ===")
    print(message.reasoning_content)
    print()

# Output the final answer
print("=== Final Answer ===")
print(message.content)

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: 'sk-YOUR_API_KEY',
  baseURL: 'https://api.apiyi.com/v1',
});

async function callKimiThinking(userMessage) {
  const response = await client.chat.completions.create({
    model: 'kimi-k2.5',
    messages: [
      {
        role: 'system',
        content: 'You are a helpful assistant.',
      },
      {
        role: 'user',
        content: userMessage,
      },
    ],
    // Pass enable_thinking via extra_body
    // @ts-ignore
    enable_thinking: true,
    temperature: 1.0,
    max_tokens: 16000,
  });

  const message = response.choices[0].message;
  
  // Extract reasoning process
  const reasoningContent = message.reasoning_content;
  if (reasoningContent) {
    console.log('=== Thinking Process ===');
    console.log(reasoningContent);
    console.log();
  }
  
  // Extract final answer
  console.log('=== Final Answer ===');
  console.log(message.content);
  
  return {
    thinking: reasoningContent,
    answer: message.content,
  };
}

// Usage example
callKimiThinking('Prove step-by-step: There are infinitely many prime numbers (Euclid\'s proof)');

💡 Integration Tip: Simply replace the base_url with https://api.apiyi.com/v1 in the code above. The rest of the parameters are fully compatible with the OpenAI SDK, so there's no extra learning curve. APIYI (apiyi.com) supports calling all mainstream models with a single Key.

Key Parameters Explained: Configure Correctly to Avoid Pitfalls

Parameter Configuration Reference Table

API Response Structure Explained

{
  "choices": [
    {
      "message": {
        "role": "assistant",
        "content": "Final answer content...",
        "reasoning_content": "The model's thought process, including step-by-step reasoning..."
      }
    }
  ],
  "usage": {
    "prompt_tokens": 150,
    "completion_tokens": 3200,
    "total_tokens": 3350
  }
}

The reasoning_content field contains the complete chain-of-thought, which is typically 3-5 times longer than the content field and is the core data for understanding the model's decision-making process.

🎯 Cost Control Tip: Token consumption in thinking mode is about 2-4 times higher than in standard mode. We recommend accessing it via APIYI (apiyi.com), where group pricing of 0.88 can significantly reduce reasoning costs. Plus, you get a $10 bonus for every $100 topped up.

APIYI vs. Official: Price and Stability Comparison

Platform Comparison Overview

APIYI Price Advantage Calculation Example

Taking 1,000 monthly calls to the kimi-k2.5 thinking mode (averaging 3,000 input tokens + 5,000 output tokens per call) as an example:

Input token cost:
  Official price approx. $0.60/1M → 1000 calls × 3000 tokens = 3M tokens → $1.80
  APIYI group price 0.88 discount → approx. $1.58

Output token cost (including reasoning):
  Official price approx. $2.50/1M → 1000 calls × 5000 tokens = 5M tokens → $12.50
  APIYI group price 0.88 discount → approx. $11.00

Monthly savings: approx. $1.72 + top-up bonus covers an additional ~10% of costs

💡 Actual Savings: The "under 20% off" at APIYI comes from two combined factors: group price discounts (0.88) + top-up bonuses (deposit $100, get $10, which is an extra 10% budget). The actual total cost is roughly 79-80% of the official price.

Best Use Cases for Kimi K2.5 Thinking Mode

Scenarios Recommended for Thinking Mode

1. Complex Mathematical Reasoning

# Suitable for thinking mode
prompt = "Please prove Fermat's Last Theorem for n=3 and provide detailed steps."

2. Code Debugging and Optimization

# Suitable for thinking mode
prompt = """
The following code has a hidden concurrency bug. Please identify and fix it:
[Paste complex multi-threaded code here]
"""

3. Multi-step Logical Analysis

# Suitable for thinking mode
prompt = "Analyze the logical loopholes in this business plan and rank them by priority."

4. Scientific Derivation

# Suitable for thinking mode
prompt = "Derive the energy level formula for the hydrogen atom from the basic principles of quantum mechanics."

Scenarios Where Thinking Mode Is Unnecessary

# Use standard mode (without enable_thinking) for these scenarios to save 50-70% on token costs

# Simple Q&A
"What's the weather like today?"  # No reasoning required

# Text Translation
"Please translate the following content into English: ..."  # No reasoning required

# Formatted Output
"Format the following JSON data for display"  # No reasoning required

# Creative Writing
"Write a poem about spring"  # No deep reasoning required

🎯 Usage Tip: We recommend dynamically switching modes based on task complexity. By connecting via APIYI (apiyi.com), you can use a single API key to flexibly call kimi-k2.5 (thinking mode) alongside other lightweight models, mixing and matching as needed.

Streaming Output: Handling Real-time Responses in Thinking Mode

When using streaming with the thinking mode, you need to handle the incremental chunks of reasoning_content specifically:

from openai import OpenAI

client = OpenAI(
    api_key="sk-YOUR_API_KEY",
    base_url="https://api.apiyi.com/v1"
)

# Streaming invocation example
stream = client.chat.completions.create(
    model="kimi-k2.5",
    messages=[
        {"role": "user", "content": "Please analyze the worst-case time complexity of the quicksort algorithm."}
    ],
    extra_body={"enable_thinking": True},
    temperature=1.0,
    max_tokens=16000,
    stream=True
)

thinking_buffer = []
answer_buffer = []
is_thinking = True

for chunk in stream:
    delta = chunk.choices[0].delta
    
    # Process reasoning content stream
    if hasattr(delta, 'reasoning_content') and delta.reasoning_content:
        thinking_buffer.append(delta.reasoning_content)
        print(delta.reasoning_content, end='', flush=True)
    
    # Process final answer stream
    elif delta.content:
        if is_thinking:
            print("\n\n=== Final Answer ===\n")
            is_thinking = False
        answer_buffer.append(delta.content)
        print(delta.content, end='', flush=True)

print()  # Newline

💡 Streaming Key Points: reasoning_content and content are independent fields during streaming. Typically, the reasoning_content is output in full before the content begins. You need to listen for incremental data from both fields separately.

FAQ

Q1: I don't see the reasoning_content field after calling the API. Is the thinking mode not working?

A: Please check the following three points:

1. Have you correctly passed the "enable_thinking": true parameter?
2. Is max_tokens set to 16000 or higher?
3. When using the Python SDK, are you passing the parameter via extra_body={"enable_thinking": True}?

We recommend testing directly with curl first to confirm the parameter format is correct before integrating it into your code. APIYI customer support at apiyi.com is available for technical assistance.

Q2: Token consumption is too high in thinking mode. How can I control costs?

A: You can optimize from these perspectives:

1. Disable thinking mode for simple tasks (do not pass the enable_thinking parameter).
2. Appropriately lower max_tokens (minimum 8000, though this may truncate complex reasoning).
3. Route tasks: use kimi-k2.5 thinking for complex reasoning and lightweight models like gpt-4o-mini for simple tasks.
4. Reduce base costs through APIYI (apiyi.com) group pricing (0.88).

Q3: Must temperature be set to 1.0?

A: It is highly recommended by the official documentation to set it to 1.0, as this is the optimal temperature parameter for the kimi-k2.5 thinking mode. Setting it too low (e.g., 0.7) can make the model too conservative during reasoning, leading to lower quality; setting it too high (e.g., 1.5) may result in incoherent reasoning chains. Using 1.0 is the safest choice.

Q4: Is the kimi-k2.5 on APIYI exactly the same as the official one?

A: Yes. APIYI uses the official Alibaba Cloud transit link; the model weights and capabilities are identical to the official Kimi. The only difference lies in how parameters are passed: the official version enables thinking by default, while APIYI requires you to manually pass enable_thinking: true. This is a standard difference for an API proxy service and does not affect the model's output quality.

Summary: Key Takeaways from Kimi K2.5 Thinking Mode

Kimi K2.5 excels in core benchmarks such as AIME mathematical reasoning (96.1%) and code generation (SWE-Bench 76.8%). Its thinking mode is particularly well-suited for complex tasks that require multi-step reasoning.

🎯 Get Started Now: Visit the APIYI website at apiyi.com, register for an account to get your API key, and you can integrate the Kimi K2.5 thinking mode in under 5 minutes. Top up $100 to receive a $10 bonus, and when combined with our group discounts, your total cost will be more than 20% lower than the official Kimi rates.

Article written by the APIYI technical team | Data sources: Moonshot AI official documentation and Artificial Analysis evaluation report (January 2026)

For technical support, please visit the APIYI Help Center: help.apiyi.com