Kimi K2 Thinking Outperforms GPT-5 and Claude 4.5, Redefines Open-Source AI

• Moonshot AI’s Kimi K2 Thinking outperformed GPT-5 and Claude 4.5 across key reasoning and coding benchmarks.
• The one-trillion-parameter MoE model activates 32B per inference and supports 256k-token context windows.
• Fully open-source under a Modified MIT License, K2 Thinking costs up to 10x less than GPT-5 while matching or exceeding its performance.

Moonshot AI, the Chinese startup backed by Alibaba, has unleashed a shockwave across the global AI industry with the release of its Kimi K2 Thinking model, a trillion-parameter behemoth that has outperformed OpenAI’s GPT-5 and Anthropic’s Claude 4.5 Sonnet across key reasoning and coding benchmarks.

Launched on November 6, Kimi K2 Thinking achieved state-of-the-art results, scoring 44.9% on Humanity’s Last Exam, 60.2% on the BrowseComp web reasoning benchmark, and 71.3% on SWE-Bench Verified, cementing its lead over both U.S. and Chinese peers. Built as a Mixture-of-Experts (MoE) model, it activates 32 billion parameters per inference from a trillion-parameter total and supports a 256,000-token context window, enabling deep reasoning over extended conversations.

Unlike proprietary systems, K2 Thinking is fully open-source under a Modified MIT License, allowing unrestricted commercial use with a light attribution clause for massive-scale deployments. Its transparent reasoning design exposes each logical step in outputs, helping users verify multi-stage problem-solving in real time, a rarity among closed models.

Despite its massive scale, the model’s runtime efficiency is exceptional: it costs just $0.15 per million tokens (cache hits) and $2.50 for output, nearly ten times cheaper than GPT-5’s reported pricing. Moonshot AI claims it can perform 200–300 sequential tool calls autonomously, marking a new high for “agentic” reasoning systems that can plan, execute, and self-correct tasks without human guidance.

Benchmarks reveal K2 Thinking not only outpaces GPT-5 (54.9%) and Claude 4.5 (24.1%) in BrowseComp, but also matches or exceeds GPT-5 in technical reasoning and coding domains. This milestone effectively collapses the performance gap between open-weight models and frontier proprietary systems.

K2 Thinking’s rise comes as major U.S. AI firms face growing scrutiny over $1.4 trillion in cumulative AI infrastructure spending. With open-weight systems like Moonshot’s K2 and MiniMax-M2 achieving near or superior results at a fraction of the cost, the economics of “bigger is better” AI are now being fundamentally questioned.

For enterprises, this marks a paradigm shift. High-end reasoning and coding performance no longer requires billion-dollar data centers; it’s now available, freely and openly, from China’s rapidly evolving AI ecosystem.

Editorial Note: This news article has been written with assistance from AI. Edited & fact-checked by the Editorial Team.

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