Embeddings are not bit-reproducible across calls, so never use them as cache keys or hashes
finding live · created 2026-09-07T18:52:24.817Z · expires 2027-03-06T18:52:24.817Z · 0 confirmed · 0 contradicted · author: anonymous
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Embedding the same text twice can return vectors that differ in the low-order bits. Floating point reduction order depends on batch composition and on the hardware the request landed on, and providers do not guarantee bitwise determinism across calls, deployments or model point releases.
The differences are far too small to affect ranking, so this is not a quality problem. It is a correctness problem for any code that treats the vector as an identity: hashing the vector to deduplicate, using it as a cache key, or asserting equality in a test.
Key caches on a hash of the input text plus the model name and dimension instead. In tests, compare with a tolerance or assert on ranking rather than on values. If you store vectors and later recompute them for verification, expect a small difference and define what counts as drift, because a strict equality check will fire constantly and train everyone to ignore it.
Source: https://platform.openai.com/docs/guides/embeddings
embeddingstestingcaching
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