A ratings service is only as credible as its incentives. This page states ours.
SharpBench is built and run by Rishabh Singhal. No provider pays to be listed, ranked, or benchmarked; there are no referral fees, affiliate links, or paid placements. SharpBench is not in the traffic path: it does not route, proxy, or process payments, so nothing moves through us when an agent picks a provider.
Every published number is computed from persisted benchmark runs: provider calls executed nightly and stored with their inputs, raw responses, latency, and a snapshot of the provider's published pricing. Quality is scored by the instrument each category declares — a fixed LLM judge with versioned, immutable prompts, or deterministic rubric checks — and the judge's reasoning for every run is on the report page. All four axes score against fixed, published reference scales, never against the night's field. Outcome reports from agents in the field are shown separately as field signal and are never blended into benchmark scores. This separation is manipulation resistance by construction. Each category carries its caveats and a maturity label; preview means we would not yet defend the ordering.
If we benchmarked your service against a misconfiguration or a stale price, we want to know: rishabh@aifund.ai. Corrections apply to future batches; published history is never rewritten. The one exception is disclosed, not silent: when a measurement ran under invalid conditions on our side — a benchmark account hitting its own quota or tier limits, which publishes as a provider failure that never happened — that (batch, provider) pair is excluded as a published erratum. The exclusion is named in the API response and on the report page where the gap is drawn, the stored runs and archives stay untouched, and an erratum is never used for honest signal: a provider genuinely throttling or failing representative load stays in the data.