We evaluated Kimi K2 Thinking on all benchmarks, finding strong performance on coding benchmarks like SWE-bench Verified. We otherwise found middle-of-the-pack performance on both public and private benchmarks.

In comparison to Kimi K2 Instruct, we found a modest improvement across most industries, with the exception of legal and finance. For instance, the Instruct model outperforms the thinking model by nearly 5% on our TaxEval benchmark.

Overall, we found Kimi K2 Thinking to be a strong successor to Kimi K2 Instruct, but doesn’t live up to the twitter hype of “best model on the market”.

We just evaluated Kimi K2 Thinking on our Vals Index! We found it to improve upon the already solid performance of Kimi K2 Instruct, placing 2nd in our open-weight category.

Kimi K2 Thinking especially shines on agentic use-cases requiring iterative tool calling, like our Finance Agent Benchmark. It performs similarly to Kimi K2 Instruct on other benchmarks.

One meaningful downside of the model is its latency - on average, Kimi K2 Thinking takes almost five times as long as GLM 4.6, the leader on our open-weight Vals Index.

Overall, we see Kimi K2 Thinking as an important contribution to the open-source ecosystem.