Methodology

The AI Model Estimator is a structured decision support tool. It replaces gut feel and raw price comparisons with a consistent framework for evaluating AI models against specific tasks — but it is not a substitute for running your own evaluations on your actual workload.

Every number this tool produces is an estimate derived from proxy benchmarks. The tool is designed to move a conversation forward, not to end it. Use it to narrow your options, identify where the local-vs-API tradeoff is worth investigating, and understand which dimensions of performance matter most for your task. Validate the recommendation that matters most with a real test before committing.

Model data is sourced from Artificial Analysis, an independent benchmarking platform that tracks performance, pricing, and speed across frontier and open-weight models. Data is cached and refreshed weekly.

The composite Intelligence Index is built from up to eight component benchmarks: GPQA Diamond (graduate-level scientific reasoning), LiveCodeBench (real-world coding tasks), MMLU-Pro (professional knowledge), SciCode (scientific problem solving), HLE (extremely hard knowledge questions), and three proprietary Artificial Analysis evaluations covering instruction following, coding, and general reasoning.

Not every model has scores across all benchmarks. Where a benchmark score is missing and that benchmark carries weight for a given task, the weight is redistributed proportionally across the benchmarks the model does have. A warning is logged when this happens. Models missing all pricing data are excluded from scoring entirely.

Scoring runs in four steps for every model-task pair.

Step 1 — Weighted intelligence score

Rather than using the composite Intelligence Index, we compute a task-specific score by taking a weighted combination of the benchmarks most predictive of performance on that task. A coding task weights LiveCodeBench and the coding index heavily. A legal reasoning task weights GPQA Diamond. This produces a weighted intelligence value that better reflects what the task actually requires.

Step 2 — Sigmoid success curve

The weighted intelligence score is passed through a sigmoid function to produce an estimated task success rate. The sigmoid has two parameters: a midpoint (the intelligence score at which a model succeeds 50% of the time) and a steepness value (how sharply success falls off below that midpoint). Both are task-specific. The midpoint is computed at runtime by finding the intelligence score that corresponds to the task's anchor benchmark threshold across the current model dataset — so it self-calibrates as new models are added. The steepness is derived from the task's success curve shape: flat tasks (sentiment analysis) use a low steepness, steep tasks (agentic workflows) use a high steepness.

Step 3 — Effective cost per success

The listed blended price understates the real cost of a successful output when the model fails some percentage of calls. Effective cost adjusts for this: cost per call divided by success rate. A model succeeding 50% of the time effectively costs twice its listed price per usable output.

Step 4 — Value index

Success rate divided by effective cost per success. Higher is better. This is the primary ranking metric.

Each task is calibrated against a specific Artificial Analysis benchmark at a defined threshold. The threshold represents the benchmark score at which a model transitions from mostly failing to mostly succeeding at that task — the 50% success point on the sigmoid curve.

These thresholds are estimated, not measured. They represent informed judgment about which benchmark best proxies for the cognitive demands of each task, and at what performance level a model becomes competent enough to reliably handle it. Tasks are flagged with an estimation confidence level — high, medium, or low — that reflects how grounded that judgment is. Low confidence tasks should be treated as especially directional.

The midpoint recalibrates automatically as new models enter the dataset. Because midpoints are computed from the live distribution of model scores rather than fixed at a static value, adding a wave of stronger models shifts the midpoints for hard tasks upward. This means the same model may score differently over time as the benchmark landscape changes — which reflects reality, not a bug.

The optimization output surfaces two heroes: the model with the highest success rate at your threshold, and the model with the lowest cost per successful output at your threshold. When both cards point to the same model, that is a strong signal. When they differ, you have a real tradeoff between reliability and cost efficiency that only your use case can resolve.

Runner-up models are grouped by proximity to the winner's value index rather than ranked strictly. Models within 15% of the winner are statistically similar — the difference between them is smaller than the uncertainty in the underlying estimates. Treat them as peers and choose based on provider preference, latency, or availability.

The local vs. API recommendation is the highest-stakes output this tool produces and the one most sensitive to your specific inputs. Hardware cost assumptions, actual call volume, and your team's capacity to operate local infrastructure all affect the breakeven point significantly. The estimator gives you a structured starting point — validate it with your real numbers before making infrastructure decisions.