Kimi K2 Instruct API
Released July 2025 | 256K Tokens context | 1T params (32B active) parameters
Kimi K2 Instruct API enables General-purpose chat and support agents that need fast, fluent responses without exposing chain-of-thought, Coding copilots that draft and review code with tight latency budgets, and Multilingual content generation and summarisation for global product teams. Kimi K2-Instruct is Moonshot AI's reflex-grade 1T-parameter MoE model (32B active) tuned for low-latency instruction following, tool use, and multilingual chat across a 128K context window. Standout strengths include 1T MoE with 32B active parameters provides frontier quality while maintaining fast reflex responses and Trained on 15.5T multilingual and tool-use tokens for strong cross-lingual coverage. 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-Instruct", messages=[ { "role": "user", "content": "Explain quantum computing in simple terms" } ], max_tokens=8192, temperature=0.7, top_p=1, 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-Instruct", messages=[ { "role": "user", "content": "Explain quantum computing in simple terms" } ], max_tokens=8192, temperature=0.7, top_p=1, 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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