AI Models by Cost
Published price for 1M I/O Tokens compared with effective cost after applying the measured or imputed task-usage multiplier.
AI Models by Cost
CostIQ vs Input/Output Token CostEach model's estimated IQ plotted against its published token price. Toggle between input and output price per 1M tokens.Data: AI IQ methodologyOpen chartCostIQ vs Blended Token CostEach model's estimated IQ plotted against the cost of a representative 1M-token blend. Toggle between a coding blend (cache-heavy — 800K cache-read + 100K input + 100K output) and a copywriting blend (output-heavy — 150K input + 850K output).Data: AI IQ methodologyOpen chartCostIQ vs CostEach model's estimated IQ against its effective cost per 1M I/O Tokens (sticker price × measured or imputed usage multiplier).Data: AI IQ methodologyOpen chartCostThe Falling Cost of IntelligenceX = release date. Y = effective cost per 1M I/O Tokens (log). Each step line tracks the cheapest model to date at or above an IQ level; large dots mark the models that set each new price floor, faint dots show every other qualifying model, colored by provider.Data: AI IQ methodologyOpen chartCostEQ vs Effective CostDiagnostic EQ plotted against effective cost per 1M I/O Tokens (sticker price × measured or imputed usage multiplier).Data: Artificial Analysis, ARC Prize, Vals Index v2 Cost per Test +4 moreOpen chartCostInput Price vs Output PriceEach model positioned by its published token prices — input price (Y) against output price (X), both per 1M tokens on a log scale. The dashed line is the best fit across models, showing the typical output-to-input price relationship.Data: AI IQ datasetOpen chart