EQ vs Effective Cost
Diagnostic EQ plotted against effective cost per 1M I/O Tokens (sticker price × measured or imputed usage multiplier).
EQ vs Effective Cost
EQ vs Effective Cost
Diagnostic EQ plotted against effective cost per 1M I/O Tokens (sticker price × measured or imputed usage multiplier).
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How to read this chart
Each point is a public model. The chart compares EQ against Effective Cost (per 1M I/O Tokens), with color showing the model provider.
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 chartCostAI Models by CostPublished price for 1M I/O Tokens compared with effective cost after applying the measured or imputed task-usage multiplier.Data: Artificial Analysis, ARC Prize, Vals.aiOpen 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