The gap between open and closed AI models is narrowing fast, and China’s Moonshot AI is leading the charge with Kimi K2 Thinking. This new trillion-parameter MoE model, featuring 32B active parameters and a 256K context window, is setting new benchmarks in multi-step reasoning and agentic tool use[1]. With native INT4 quantization, Kimi K2 Thinking delivers lossless reductions in inference latency and GPU memory, making it both faster and cheaper to deploy than its predecessors[4].
Recent evaluations show Kimi K2 Thinking outperforming GPT-5 and Claude Sonnet 4.5 on key benchmarks: it scored 44.9% on Humanity’s Last Exam (HLE) with tools, 60.2% on BrowseComp, and 71.3% on SWE-Bench Verified[5]. These results highlight its strength in extended reasoning and stable tool invocation across hundreds of sequential steps, positioning it as one of the most capable open-weight reasoning agents available today[2].
What sets Kimi K2 Thinking apart is its blend of scale, efficiency, and agentic capability. Moonshot AI’s focus on dynamic tool use and deep reasoning makes it a compelling choice for developers and researchers pushing the boundaries of what AI agents can achieve[3].
- Trillion-parameter MoE architecture with 32B active parameters
- 256K context window and native INT4 quantization
- State-of-the-art scores on HLE, BrowseComp, and SWE-Bench
- Optimized for multi-step reasoning and agentic workflows
The rise of models like Kimi K2 Thinking signals a new era where open-weight AI can rival—and sometimes surpass—the performance of closed frontier models, accelerating innovation for everyone.
