ModelSpec is an analysis of alternatives for AI models: it decides which models fit a job from your requirements, sourced benchmarks and real cost, and shows its work.

High volume, fastest

Find a text model for many short requests whose offering streams output fastest and costs little, among models within 1.0 of the strongest response-quality estimate.

Worked example as of 2026-09-23, from the public data image.

Short requests at scale; the fastest of the strong, cost counts.

Requirements

• model.class = text-generator: The workload needs generated text responses.
• chat_preference >= best(1.0): Keep only models whose response-quality estimate is within 1.0 of the strongest eligible model's; speed decides among them. A gap of 1.0 can be measurable but is not decisive.

Preferences

• offering.speed.throughput: Among the models the floor keeps, prefer the offering that produces output tokens fastest.
• -offering.cost_per_task: At volume, cost still counts, in the same proportion to speed as before the floor.

Worked answer

Decision status: no_feasible.

No recommendation: the public image has no answer with enough evidence.

Unknown requirements

Some candidates may qualify. Missing facts:

• chat_preference
• offering.speed.throughput

Missing evidence and possible changes

• no model that meets the requirements has a value for offering.speed.throughput, offering.cost_per_task

Decision dec_92e819199fcb937c469790f2 on public snapshot snap_05a963d2477ac2fb.

What this template teaches

Strength is a floor relative to the strongest eligible model, so speed can never buy it back. Speed is a Prefer, not a gate. An offering without a measured speed may qualify but is not ranked, so this tier answers only once speeds are published.

The live board uses current data. Its answer can change as evidence changes.

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