AlphaSense Study: Context is the Bottleneck, GPT-5.6 Beats Kimi on Cost Efficiency
rohanpaul_ai · x · 2026-08-15
A new study by AlphaSense reveals that the bottleneck for answer quality in finance and business research has shifted from raw model intelligence to context retrieval. The report notes that while Kimi is cheaper per token than GPT-5.6 Sol, it consumes significantly more tokens to assemble context, making it more expensive per completed query. The study emphasizes that the real unit of cost is tokens-to-completion multiplied by token price. Additionally, pairing frontier models with AlphaSense Search is roughly 3x cheaper and yields answers preferred 2:1 over a vector-RAG baseline.
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