Grok Evolution Velocity Benchmarker

// ENGINE CONTROLS LIVE_CALCULATOR

Cadence Multiplier 1.25x

0.5x (Weekly) 1.0x (Daily) 3.0x (Continuous)

Projection Horizon (Days) 90d

Active Agent Modules 4 Active

Reasoning Core

Multi-Modal Vision

Real-Time Search

Code Sandbox

RLHF & Self-Correction Rounds 4 Rounds/Day

// CAPABILITY TRAJECTORY (COMPOUND IMPROVEMENT) DAY_OVER_DAY

// DOMAIN BENCHMARK RADAR COMPOSITE

// ESTIMATED SUBSYSTEMS

MATH / REASONING

95.2%

CODE GENERATION

93.4%

MULTI-MODAL VISION

94.1%

CONVERSATION / QA

96.5%

Based on empirical compound feedback loop model: $A_t = A_{max} - (A_{max} - A_0)e^{-\alpha \cdot \text{cadence} \cdot t}$.

The benchmarker computes assigned metrics from cadence and module inputs

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Read the explanation

The source daily gain starts at point two two times cadence divided by one point two five, multiplied by separate module and training-round factors. At four modules and four rounds those two factors equal one. Cadences one point two five and two point five therefore give point two two and point four four. Bars share eight hundred pixels per assigned gain point. These are source assumptions, not observed improvements of any model. The source module bonus is module count divided by four times five. One module contributes one point two five; four contribute five. Bars share eighty pixels per index contribution point. Other index terms are separate, and the final expression is capped at ninety-nine point four. This is synthetic scoring, not a validated benchmark result. The source latency expression adds training rounds times two point five to a baseline and selected module costs, then subtracts a cadence term. Two rounds add five and four add ten before other terms and rounding. Bars share thirty-five pixels per assigned millisecond. No real request latency is measured by this expression. Complete native JSON, original reset, chart buffers and four-width first-footer preservation remain checked.