Corograph & OpenAI

Figure 1 Context tokens per run

Context tokens are the tokens the model read to answer a whole run: the uncached input plus every cache read, summed over every API call in the run, all threads included. Each column is the mean over the valid, scored runs of one arm at one model, every arm split by model because token counts are model facts; the dashed lines mark Corograph's and each bare harness's pooled value wherever pooling is licensed. Full figure, definition and disclosures →

Figure 13 Cost per correct answer

Cost per correct answer divides what a run cost by the number of questions it answered correctly: the vendor-reported figure where the harness reported one, otherwise the benchmark's estimate from the published rate tables over the run's four token columns. Each column is the mean over valid, scored runs at one model (or pooled over the arm's roster in the pooled-models view); Corograph and the bare harnesses are the dashed references, and the data table names each row's cost basis. Full figure, definition and disclosures →

Figure 7 Outcome composition per run

Every valid run becomes one thin column showing how its questions divided between correct (the arm's color), incorrect (dark) and unanswered or unjudged (black), so an arm's whole record is visible at once. Columns are ordered by model then repetition; the number above each group is the arm's mean accuracy and the number inside is its difference from Corograph. Full figure, definition and disclosures →

Figure 2 Accuracy per run

Accuracy is the share of a run's questions whose answer the grading pipeline marked correct, so every dot is one complete run of one arm at one model. The solid tick is the arm's pooled mean, the thin line its worst-to-best range, and dots are translucent so overlap reads as density; hover a dot for its model, repetition and run directory. Full figure, definition and disclosures →

Figure 14 Cost per run

Every dot is what one run cost (vendor-reported where available, otherwise estimated from the rate tables), so the spread of an arm's price is visible, not just its mean. The solid tick is the pooled mean and the thin line the range; hover a dot for its model, repetition and run directory. Full figure, definition and disclosures →

Figure 15 Tool result tokens per run

Tool result tokens are the volume of text the arm's tools pushed back into the model's context over a run — file contents, search hits, shell output, or an index query's result — estimated per call from the result size. Every dot is one run, split per model for every arm; the solid tick is the mean and the thin line the range. Full figure, definition and disclosures →

Figure 12 Pass rate by question category and difficulty

Every question carries one or more categories and a difficulty; each cell is the pass rate of an arm on the questions in that category (or at that difficulty), pooled over the selected models — by default the Anthropic models, which every arm ran. The number in each cell is the pass rate; the color is that cell's difference from bare Claude Code in the same column — blue above, orange below, neutral at the baseline — so a row reads as where an arm beats or trails the null hypothesis. Full figure, definition and disclosures →