Kimi K2.5
Last updated: February 17, 2026
What is Kimi K2.5?
Kimi K2.5 is a trillion-parameter AI model from Beijing-based Moonshot AI. It uses a Mixture of Experts architecture — 384 expert modules, only 32 billion active per query — giving you frontier-level intelligence at efficient compute costs. Its standout feature is Agent Swarm: native multi-agent task decomposition for complex research.
Who is it for?
- Best for: Researchers, analysts, and builders who need deep multi-source research and competitive intelligence
- Not for: Casual chatbot use or everyday questions — stick with ChatGPT for that
- Solo builder score: ⭐⭐⭐⭐☆ (4/5)
What does it cost?
| Plan | Price | What You Get |
|---|---|---|
| API | Pay-per-token | Full trillion-parameter model |
| Open Weights | Free | 171B parameter version on HuggingFace |
Hidden costs: The open weights version requires serious hardware to self-host (171B parameters).
How we’d actually use it
You’re a solo founder researching a new market. Instead of spending hours in 20 browser tabs:
- Give K2.5 a complex research query via API
- Agent Swarm decomposes it into parallel sub-tasks
- Multiple specialized agents research simultaneously
- Results synthesize into a comprehensive, multi-angle report
Time saved vs doing it manually: 4-6 hours of research → 10 minutes
What’s good
- Agent Swarm is genuinely unique — no other model does this natively
- 256K context window fits entire codebases
- Open weights available for self-hosting
- Competitive with GPT-5.2 and Claude 4.5 on benchmarks
What’s not
- API access outside China still maturing
- Data privacy considerations (Chinese company)
- Open weights version needs serious hardware
- Not the best for pure coding tasks (Claude wins there)
FAQ
Q: Is Kimi K2.5 safe to use for business research? A: For the API, be aware data routes through Moonshot’s servers. For maximum privacy, use the open weights on your own infrastructure.
Q: What’s the best alternative to Kimi K2.5 for research? A: Perplexity Pro is the closest for research, but lacks the native multi-agent approach. For general use, Claude 4.5 Opus.