Kimi K2 vs DeepSeek V3

The two most-discussed open-weight models from China in 2026: Kimi K2 (Moonshot, 1M context, ~1T MoE) and DeepSeek V3.2 (671B MoE, top-tier reasoning + coding). Different strengths, different fits.

The headline differences

• Context: Kimi K2 = 1M tokens. DeepSeek V3.2 = 128k.
• Best at: Kimi = long-doc work. DeepSeek = coding + reasoning.
• Pricing: Kimi $0.60/$2.50. DeepSeek V3.2 $0.27/$1.10. R1 $0.55/$2.19.
• Open weights: both, with custom licenses (read before commercial use).

Pick Kimi K2 for

• Long-document reasoning (1M context).
• Whole-codebase analysis without sharding.
• Long-running agents that accumulate context.
• Open-weight long-context use cases.

Pick DeepSeek V3.2 / R1 for

• Code generation + agentic SWE.
• High-volume API loops (cheapest frontier-tier).
• Reasoning chains where R1’s thinking-token economics make it cheap to over-think.
• OpenAI SDK drop-in replacement.

Self-hosting

Both need serious GPUs. K2 is even larger than V3.2 (~1T vs 671B). For commodity hardware, prefer DeepSeek-Distill-Qwen-32B or Qwen 3.5 32B — competitive on smaller budgets.

Track all open-weight options at open-source LLM tracker.