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Key Technical Specifications

Model Architecture

Built on a Mixture-of-Experts (MoE) architecture with 1 trillion total parameters and 32 billion activated parameters. Features 384 experts with 8 experts selected per token, optimized for efficient inference while maintaining high performance. Trained with the innovative Muon optimizer to achieve zero training instability.

Performance Metrics

The Kimi-K2-Instruct-0905 model demonstrates exceptional performance across coding, math, and reasoning benchmarks:
  • LiveCodeBench: 53.7% Pass@1 (top-tier coding performance)
  • SWE-bench Verified: 65.8% single-attempt accuracy
  • MMLU (Massive Multitask Language Understanding): 89.5% exact match
  • Tau2 retail tasks: 70.6% Avg@4

Use Cases

Enhanced Frontend Development
Leverage superior frontend coding capabilities for modern web development, including React, Vue, Angular, and responsive UI/UX design with best practices.
Advanced Agent Scaffolds
Build sophisticated AI agents with improved integration capabilities across popular agent frameworks and scaffolds, enabling seamless tool calling and autonomous workflows.
Tool Calling Excellence
Experience enhanced tool calling performance with better accuracy, reliability, and support for complex multi-step tool interactions and API integrations.
Full-Stack Development
Handle end-to-end software development from frontend interfaces to backend logic, database design, and API development with improved coding proficiency.

Best Practices

  • For frontend development, specify the framework (React, Vue, Angular) and provide context about existing codebase structure for consistent code generation.
  • When building agents, leverage the improved scaffold integration by clearly defining agent roles, tools, and interaction patterns upfront.
  • Utilize enhanced tool calling capabilities by providing comprehensive tool schemas with examples and error handling patterns.
  • Structure complex coding tasks into modular components to take advantage of the model's improved full-stack development proficiency.
  • Use the full 256K context window for maintaining codebase context across multiple files and maintaining development workflow continuity.

Get Started with Kimi K2 0905

Experience moonshotai/kimi-k2-instruct-0905 on Groq:

shell
pip install groq
Python
from groq import Groq
client = Groq()
completion = client.chat.completions.create(
    model="moonshotai/kimi-k2-instruct-0905",
    messages=[
        {
            "role": "user",
            "content": "Explain why fast inference is critical for reasoning models"
        }
    ]
)
print(completion.choices[0].message.content)

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