Kimi K2 Thinking
latestKimi K2 Thinking is the first open-weights model to achieve SOTA performance against leading closed-source models (GPT-5, Claude 4.5 Sonnet) across major benchmarks including HLE (44.9%), BrowseComp (60.2%), and SWE-Bench Verified (71.3%). Built on a 1T parameter MoE architecture with 32B active per token and native INT4 quantization via QAT, it maintains stable tool-use across 200–300 sequential calls within a 256K context window.
input tokens
output tokens
Cached $0.14$0.12/1M cached tokens
from openai import OpenAI # Initialize the OpenAI client with Qubrid base URL client = OpenAI( base_url="https://platform.qubrid.com/v1", api_key="QUBRID_API_KEY", ) stream = client.chat.completions.create( model="moonshotai/Kimi-K2-Thinking", messages=[ { "role": "user", "content": "Explain quantum computing in simple terms" } ], max_tokens=16384, temperature=1, top_p=0.95, stream=True ) for chunk in stream: if chunk.choices and chunk.choices[0].delta.content: print(chunk.choices[0].delta.content, end="", flush=True) print("\n") Enterprise
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