Kimi K2 Thinking: Agentic Tool Loops in Python

Moonshot's Kimi K2.6 shipped as open weights in late April and did something open models had not done before: it tied or beat the closed frontier on real coding work. On SWE-Bench Pro it scored 58.6, ahead of GPT-5.4 (57.7) and Claude Opus 4.6 (53.4), at a list price of $0.60 / $2.50 per million input/output tokens. That price-to-capability ratio is why it spread across developer communities within hours.

But the screenshots people keep sharing are not the benchmark bars. They are the worklogs: a single K2.6 agent that ran for twelve hours straight, made over four thousand tool calls, and optimized a model's inference loop from 15 to 193 tokens per second. That kind of endurance comes from one design decision you can use directly: K2.6 was trained to interleave its private chain-of-thought with tool calls, and the API hands that reasoning back to you in a field called reasoning_content. Feed it back in, and the model keeps its train of thought across dozens of steps instead of starting cold each turn.