Moonshot AI open-sourced Kimi K2.6 in late April. This is not a routine model iteration—it approaches the level of top closed-source models on multiple benchmarks while remaining fully open-source.
Core Specs
- Architecture: 1T parameter MoE (Mixture of Experts)
- Context Window: 256K tokens (training platform supports 265K)
- License: Open weights, available through Nous Portal, Cline, Fireworks AI
- GitHub: MoonshotAI/Kimi-K2 — 10,700 stars
Benchmark Performance
| Benchmark | Kimi K2.6 | Reference |
|---|---|---|
| LiveBench | Surpasses Opus 4.7 | Highest among open models |
| Terminal-Bench | Near GPT-5.4, Opus 4.7 | ~1/6 the cost |
| Document Arena | #8, +14 pts over K2.5-Thinking | #1 open model |
| Vision Arena | #15, +9 pts over K2.5-Thinking | #1 open vision model |
Agent Capabilities
- Supports 300 parallel sub-agents from a single prompt
- Can execute 4,000 coordinated steps
- Supports 12 hours of autonomous operation
- Covers coding, research, slides, spreadsheets, dataset generation, document writing
Available Channels
- Nous Portal: Free trial (powered by Vercel AI Gateway)
- Cline: Limited-time free access
- Fireworks AI: SFT, DPO, RL fine-tuning support, 265K context
- Cloudflare Workers: Direct deployment
- Hugging Face: Open weights download
Quick Start
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("MoonshotAI/Kimi-K2.6", trust_remote_code=True)