Kimi K2 Thinking API
Released November 2025 | 256K Tokens context | 1T params (32B active) parameters
Kimi K2 Thinking API enables Complex agentic research workflows, Long-horizon coding and debugging, Advanced mathematical reasoning, Multi-step tool orchestration, Autonomous writing and analysis, and Scientific reasoning tasks. Kimi 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. Standout strengths include First open-source model to beat closed frontier models (HLE, BrowseComp, SWE-bench) and 1T MoE with only 32B active per token. It is optimized for production agent and assistant workloads where response quality, latency, and predictable operating cost all matter.
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")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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