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Documentation Index

Fetch the complete documentation index at: https://docs.kymaapi.com/llms.txt

Use this file to discover all available pages before exploring further.

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Available models

Model IDParametersContextSpeedBest For
minimax-m2.5—196KMediumAgentic coding, SWE-bench
minimax-m2.7—205KMediumAgentic coding, productivity, debugging

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Why MiniMax?

MiniMax models are strong for real-world engineering and productivity workflows. They excel at:
  • Multi-file code editing — understands project context across files
  • Bug fixing — diagnoses and fixes real GitHub issues
  • Agentic workflows — works well with tool use and multi-step coding tasks
  • Long context — 196K tokens covers large codebases

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Recommendation

  • Use minimax-m2.5 when you want a proven SWE-bench-focused agentic coding model.
  • Use minimax-m2.7 when you want the newer MiniMax generation for agentic coding, debugging, and productivity workflows.
from openai import OpenAI

client = OpenAI(base_url="https://kymaapi.com/v1", api_key="ky-...")

response = client.chat.completions.create(
    model="minimax-m2.7",
    messages=[{"role": "user", "content": "Fix the race condition in this code: ..."}]
)