Kimi K2 Thinking
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Kimi K2 Thinking

kimi-k2-thinking
Moonshot AI
Kimi K2 Thinking is the latest generation open-source reasoning model, designed for advanced multi-step thinking and dynamic tool invocation. It achieves state-of-the-art performance on benchmarks such as HLE and BrowseComp, demonstrating exceptional reasoning depth and stability. Built with native INT4 quantization and a 256k context window, Kimi K2 delivers high precision with significantly reduced inference latency and memory usage, supporting 200–300 consecutive tool calls for long-context reasoning, complex task planning, and intelligent agent applications.

Pricing

  • Input Tokens: $0.548 /M tokens
  • Output Tokens: $2.192 /M tokens
  • Cache Read: $0 /M tokens

Input Modalities

  • Text

Context length

  • 262K tokens

Max output

  • 262K tokens

Capabilities

  • Thinking
  • Streaming
  • Tool calling
  • Web search
  • URL context
  • Code interpreter
  • Computer use
  • File search
  • Memory tool
  • Structured outputs
  • Citations
  • Prompt caching
  • Background mode
  • Server-side sessions

Providers

Sophnet s-kimi-k2-thinking
Pricing$0.548$2.192
Context262K
Max output262K
Latency2.4S
Throughput13.3TPS
Uptime
0.00% uptime 2 days ago
0.00% uptime yesterday
0.00% uptime today
Siliconflow sf-kimi-k2-thinking
Pricing$0.548$2.192
Context256K
Max output256K
Latency1.6S
Throughput42.4TPS
Uptime
0.00% uptime 2 days ago
0.00% uptime yesterday
0.00% uptime today
Moonshot kimi-k2-thinking
Pricing$0.548$2.192
Cache$0
Context262K
Max output262K
Latency2.4S
Throughput17.6TPS
Uptime
0.00% uptime 2 days ago
0.00% uptime yesterday
0.00% uptime today

Performance for kimi-k2-thinking

Uptime is the percentage of requests that succeeded over the past 72 hours. AIHubMix continuously monitors every provider and automatically retries with the next-best provider when one returns an error or responds too slowly; Latency is total round-trip time (lower is better); Throughput is how fast the model writes (tokens per second, higher is better).

Uptime
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Latency
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Throughput
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Try this model

Python
import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["AIHUBMIX_API_KEY"],
    base_url="https://shkq.org/v1",
)

response = client.chat.completions.create(
    model="kimi-k2-thinking",
    messages=[
      {
        "role": "user",
        "content": "Hello, how are you?"
      }
    ],
    max_tokens=1024,
    stream=False,
)

print(response.choices[0].message.content)

Frequently asked questions

What is Kimi K2 Thinking?

Kimi K2 Thinking is the latest generation open-source reasoning model, designed for advanced multi-step thinking and dynamic tool invocation. It achieves state-of-the-art performance on benchmarks such as HLE and BrowseComp, demonstrating exceptional reasoning depth and stability. Built with native INT4 quantization and a 256k context window, Kimi K2 delivers high precision with significantly reduced inference latency and memory usage, supporting 200–300 consecutive tool calls for long-context reasoning, complex task planning, and intelligent agent applications.