Moonshot's newly released Kimi K3 is a 2.8 trillion parameter model that marks their most capable release to date. This article explores its performance benchmarks, pricing structures compared to Anthropic’s Sonnet series, and how it handles complex tasks like SVG generation. Through the lens of the pelican benchmark, the author examines the relationship between reasoning token consumption, cost, and a model's spatial awareness.
Main points:
- K3 is described as an open 3T-class model with high performance on long-horizon knowledge work.
- The pricing structure for K3 represents a significant increase over previous Moonshot models.
- Testing the pelican prompt reveals high reasoning token usage and substantial costs per task.
- While useful for checking spatial awareness, simple benchmarks fail to test agentic tool calling.
Main points:
- K3 is described as an open 3T-class model with high performance on long-horizon knowledge work.
- The pricing structure for K3 represents a significant increase over previous Moonshot models.
- Testing the pelican prompt reveals high reasoning token usage and substantial costs per task.
- While useful for checking spatial awareness, simple benchmarks fail to test agentic tool calling.
