Kimi K2 Thinking Model: Complete Developer Guide for Reasoning Workflows

from openai import OpenAI

# Moonshot uses OpenAI-compatible API format
client = OpenAI(
    api_key="your-moonshot-api-key",
    base_url="https://api.moonshot.cn/v1"
)

response = client.chat.completions.create(
    model="kimi-k2-thinking",
    messages=[
        {
            "role": "system",
            "content": "You are a senior software architect. Think step by step."
        },
        {
            "role": "user",
            "content": """Design a distributed rate limiter that:
1. Handles 100K requests/second across 50 nodes
2. Supports sliding window algorithm
3. Has <5ms p99 latency
4. Gracefully degrades if Redis is unavailable

Provide the architecture, data structures, and Go implementation."""
        }
    ],
    temperature=0.1,  # Low temp for reasoning tasks
    max_tokens=8192
)

print(response.choices[0].message.content)
# Includes detailed reasoning + implementation

from openai import OpenAI

client = OpenAI(
    api_key="your-crazyrouter-key",
    base_url="https://crazyrouter.com/v1"
)

# Same model, lower price, automatic fallback
response = client.chat.completions.create(
    model="kimi-k2-thinking",
    messages=[{
        "role": "user",
        "content": "Prove that there are infinitely many primes of the form 4k+3."
    }],
    temperature=0.0,
    max_tokens=4096
)

# Stream the response including reasoning process
stream = client.chat.completions.create(
    model="kimi-k2-thinking",
    messages=[{
        "role": "user",
        "content": "Find all bugs in this code and explain your reasoning:\n\n"
                   "```python\n"
                   "def merge_sorted(a, b):\n"
                   "    result = []\n"
                   "    i = j = 0\n"
                   "    while i < len(a) and j < len(b):\n"
                   "        if a[i] <= b[j]:\n"
                   "            result.append(a[i])\n"
                   "            i += 1\n"
                   "        else:\n"
                   "            result.append(b[j])\n"
                   "            j += 1\n"
                   "    return result\n"
                   "```"
    }],
    stream=True,
    stream_options={"include_usage": True}
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: 'your-crazyrouter-key',
  baseURL: 'https://crazyrouter.com/v1',
});

async function solveWithReasoning(problem) {
  const response = await client.chat.completions.create({
    model: 'kimi-k2-thinking',
    messages: [
      {
        role: 'system',
        content: 'Solve problems step by step. Show your reasoning clearly.'
      },
      { role: 'user', content: problem }
    ],
    temperature: 0.1,
    max_tokens: 8192,
  });

  return {
    answer: response.choices[0].message.content,
    tokens: response.usage,
  };
}

// Example: Complex algorithm design
const result = await solveWithReasoning(
  'Design an algorithm to find the longest increasing subsequence ' +
  'in O(n log n) time. Prove its correctness and analyze space complexity.'
);

def smart_route(query, complexity_score):
    """Route to appropriate model based on task complexity."""
    if complexity_score < 0.3:
        # Simple tasks: use fast, cheap model
        return "gpt-4o-mini"
    elif complexity_score < 0.7:
        # Medium tasks: K2 standard (non-thinking)
        return "kimi-k2"
    else:
        # Complex reasoning: K2 Thinking
        return "kimi-k2-thinking"

# Estimate complexity from query characteristics
def estimate_complexity(query):
    indicators = [
        "prove" in query.lower(),
        "design" in query.lower() and "system" in query.lower(),
        "optimize" in query.lower(),
        len(query) > 500,
        "step by step" in query.lower(),
        any(word in query.lower() for word in ["algorithm", "architecture", "debug"])
    ]
    return sum(indicators) / len(indicators)

# Control reasoning depth with max_tokens
# Shorter max_tokens = less thinking = cheaper

# Quick reasoning (budget mode)
response = client.chat.completions.create(
    model="kimi-k2-thinking",
    messages=[{"role": "user", "content": problem}],
    max_tokens=2048  # Limits thinking depth
)

# Deep reasoning (quality mode)
response = client.chat.completions.create(
    model="kimi-k2-thinking",
    messages=[{"role": "user", "content": problem}],
    max_tokens=16384  # Allows extensive reasoning
)

import hashlib
import json
import redis

r = redis.Redis()

def cached_reasoning(prompt, model="kimi-k2-thinking"):
    # Hash the prompt for cache key
    cache_key = f"reasoning:{hashlib.sha256(prompt.encode()).hexdigest()}"

    # Check cache
    cached = r.get(cache_key)
    if cached:
        return json.loads(cached)

    # Generate fresh reasoning
    response = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": prompt}],
        temperature=0.0  # Deterministic for caching
    )

    result = {
        "content": response.choices[0].message.content,
        "tokens": response.usage.model_dump()
    }

    # Cache for 24 hours
    r.setex(cache_key, 86400, json.dumps(result))
    return result