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
AI Models by Cost
Published price for 1M I/O Tokens compared with effective cost after applying the measured or imputed task-usage multiplier.
Controls:
How to read this chart
Each model's published sticker price is compared with its effective task cost after the measured or imputed usage multiplier is applied. Rows need positive input and output pricing; zero/free-only rows are not treated as $0 effective cost.
Data sources
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 Effective CostEach model's estimated IQ plotted against effective cost per 1M I/O Tokens (sticker price × measured or imputed usage multiplier).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.ai +3 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 chartCostTask EfficiencyEach dot shows the inverse of the effective-cost usage multiplier. Higher means less price-adjusted task work: 2× is about half the median task effort. Source-backed multipliers are preferred; lineage, peer, and 1× fallbacks are labeled in tooltips.Data: Artificial Analysis, ARC Prize, Vals.aiOpen chart