This is the open-weight value derby. Kimi K2 Thinking, from Moonshot AI, is a trillion-parameter MoE (32B active) tuned for deep reasoning, generating extensive chain-of-thought for hard math, coding and science. It posts ~71% SWE-bench Verified and ~83% LiveCodeBench with a 256K context, at prices several times below Western frontier models.
DeepSeek V3.2 is the efficiency master: a 671B MoE with sparse attention, a 128K context and even lower pricing, plus a focus on reasoning-in-tool-use that makes it a strong agent backbone. Kimi's heavy chain-of-thought can edge it on the hardest reasoning; DeepSeek's sparse attention and pricing make it cheaper for long-context and high-volume work. Both are open-weight and easy to integrate, so the choice often comes down to reasoning depth versus cost-per-token.
Which should you choose?
Kimi K2 Thinking
Kimi K2 Thinking · 262K context
Pick it if
you want maximum open-weight reasoning depth.
DeepSeek V3.2
DeepSeek V3.2 · 163K context
Pick it if
you want the lowest cost and efficient long context.
Kimi K2 Thinking edges the hardest reasoning with deep chain-of-thought; DeepSeek V3.2 wins on cost-efficiency and long-context value. Both are top open-weight picks: benchmark on your tasks.
Questions
Which is better for reasoning?
Kimi K2 Thinking's extensive chain-of-thought gives it an edge on the hardest math and coding problems, though DeepSeek V3.2 is very close.
Are both open source?
Yes. Both ship open weights (Kimi under a modified MIT licence, DeepSeek under a permissive licence), so you can self-host either.