Ethical Tech CoLab / AI Models Research
An interactive companion to a July 2026 comparative review of frontier, open-weight, and efficient AI model families. Every figure below carries its source, its date, and how far it has been independently verified.
There is no universally best AI model. Selection is a constrained optimisation across verified task quality, factual reliability, latency, token consumption, energy per accepted output, privacy, and total deployment cost.
The four instruments below let you interrogate that claim rather than take it on faith. The first is the one that matters most: advertised price per million tokens is not cost, because a model that has to be retried is not cheap.
Set the shape of your workload. The calculator applies the published input and output rates, adds reasoning tokens at the output rate, and then divides by the acceptance rate to give cost per accepted task. Defaults reproduce the document-analysis example in the review.
Read the spread, not the ranking. At the default settings the cheapest and most expensive options differ by roughly 26 times for the same request. Quality is not held constant across that spread, so the chart is a cost measurement and not a value judgement. Move the acceptance rate below 100% to see why a cheaper model can be the more expensive choice.
Output is priced three to six times above input across every published schedule in the review, and it is generated sequentially. Verbosity is therefore a cost driver, a latency driver, and an energy driver at once.
Gemini 3.1 Pro is absent because the review did not extract a per-token rate for it; the provider publishes pricing only as a live schedule.
A mixture-of-experts model stores many parameters and activates a small subset per token. That lowers compute per token relative to a dense model of the same stored size. It does not remove the memory, routing, or communication cost, so active share is a compute proxy and not a serving-cost measure.
Interactive agent benchmarks are where the remaining gap is clearest. Production systems fail through sequences of small errors rather than single wrong answers, which is why a few percentage points on a long-horizon task matter more than the same gap on a single-answer test.
Vertical marks show human baselines where the AI Index reports one. Human scores are often measured under different time, tool, and incentive conditions than model scores, so the gap is indicative rather than exact.
No single energy number describes a model. Prompt length, output length, batch size, precision, hardware generation, serving stack, and utilisation each change the result by large factors. The two scenarios below are the only two the source states, so the toggle switches between them rather than interpolating.
Optimisation matters more than model choice. A peer-reviewed study of inference across hardware, serving frameworks, batching, and decoding strategies found that appropriate optimisations cut energy by up to 73% against an unoptimised baseline. A peer-reviewed
Do not divide sector totals by a query count. Data-centre electricity is projected to reach about 945 TWh in 2030 against roughly 415 TWh in 2024, but that covers every data-centre workload, not AI queries alone. B institutional
The grade tells you how much independent scrutiny a figure has survived. It does not tell you whether the figure is true. A Grade A result can be wrong and a Grade C claim can be accurate.
A independent
Peer-reviewed studies, standardised academic benchmarks, and reproducible third-party evaluations. The only class admissible on its own for a comparative claim between providers.
B institutional
Technical reports, model cards, benchmark methodology pages, and official specifications. A provider is the definitive authority on its own commercial terms, so prices and context limits sit here.
C provider-reported
Launch benchmark tables and vendor performance claims. Recorded because excluding them would leave most commercial models undocumented. Never used alone to rank one provider above another.
! conditions unstated
Every provider benchmark figure in the review omits its harness, sampling policy, and tool permissions. Results whose conditions are unknown are recorded but are never placed in a ranked column beside results whose conditions are known.
Prices and rankings change without notice. Every value here is a record of what a named source stated on 2026-07-22, not a statement about the present. Verify against current provider documentation before any procurement, budgeting, or deployment decision. Nothing on this page is legal, financial, or procurement advice.
The full source register, the extraction protocol, and the validation tooling live in the research repository. Figures reproduced here are drawn from that review and inherit its limitations, which are stated in full alongside it.