🧠 Food for thought

Implications, context, and why it matters.

The models’ strengths reveal divergent AI strategies

• Kimi 2.5 and DeepSeek R1 take different paths in the AI race, beyond broad capability gains.
• Kimi K2 Thinking, a precursor to 2.5, excels at agentic tasks (it can plan and carry out multi-step work using external tools). It can run 200 to 300 sequential tool calls and beats models like GPT-5 on some browsing benchmarks 1.
• DeepSeek R1 leads in reasoning and mathematics, scoring 97.4% on the MATH-500 benchmark (a widely used math problem test set for evaluating AI reasoning) 1.
• DeepSeek sparked a price war in May 2024, cutting API costs by 5 to 10x. Average market prices then fell 92% by early 2025, shifting the industry’s economics 2.

Efficient Chinese AI models threaten the capital-intensive Western approach

• Models like Kimi and DeepSeek challenge the Western claim that AI leadership depends on ever larger spending and compute.
• Moonshot AI’s Kimi K2 Thinking model reportedly cost $4.6 million to train, while DeepSeek’s V3 model reportedly cost $6 million to train 2.
• Part of the cost edge comes from native INT4 quantization (a technique that uses 4-bit numbers to represent model weights). It shrinks model size and speeds up inference without major quality loss 3.
• State-of-the-art results at lower cost put pressure on Western rivals whose API prices lean on large infrastructure outlays.