CHARTS

Assumptions

This chart compares Epoch AI's ECI scores and input and output token prices from the November 9, 2026 snapshot.^{[^1]} It uses 195 distinct model versions with an ECI score, release date, and both token prices. Duplicate model/date/score/price rows are counted once. Different dated or priced versions remain eligible.

Prices are held at the snapshot value for every release date, including retired endpoints. This is a release-date comparison using current prices, not a history of prices paid at the time. The basket contains 750,000 input tokens and 250,000 output tokens; it standardizes prices rather than estimating the tokens a task needs.

Formulas and methods

For a model, basket cost is 0.75 times the input price plus 0.25 times the output price, in dollars per million tokens. At a given date and budget, the frontier is the highest ECI among models released by then whose basket costs no more than that budget.

For each release date, compare the frontiers before and after adding every model released that day. The lower budget L is the higher of the cheapest available basket costs before and after the release. The upper budget U is $93.75, the largest eligible basket price in the snapshot. Integrate the change across that common affordable range with equal weight for equal proportional changes in price, then divide by the width of the range in log dollars:

basket_cost = 0.75 × input_price + 0.25 × output_price
frontier(date, budget) = max ECI among eligible released models
release_gain = integral[L,U] (frontier_after(B) - frontier_before(B)) d ln(B)
               / ln(U/L)
chained_gain = sum of release_gain since March 14, 2023

If L equals U, use the frontier difference at that budget. The lower boundary can fall as cheaper models arrive; a newly affordable budget enters only when a previous frontier exists there.

Walkthrough

Save Epoch's model table and calculate each eligible basket. GPT-4's March 2023 version costs 0.75 × $30 + 0.25 × $60 = $37.50. Set the index to zero at its release. For each later release date, calculate the before and after price frontiers, integrate their difference over their shared budget range, and add the resulting gain to the running total. The chart shows that cumulative ECI gain, not an absolute ECI level.

Notes

The budget ceiling, changing lower boundary, and log-price weighting affect the result. The chain combines comparisons over different budget ranges; it is not the gain at a single fixed dollar budget. Missing scores or prices, pricing changes, provider differences, caching, batch discounts, tool fees, and variation in token use can change the comparison. The chart does not convert the gain to task time or show uncertainty intervals.

The downloadable package contains the frozen source, derived priced rows, and script. Run python3 script.py to reproduce the chart data with Python 3.11 or newer; the upstream ECI fitting process itself is not reproduced.

1. Epoch AI: ECI scores and token prices ↗

Model & inputs (.zip) ↓ · Priced models (.csv) ↓