Embedding APIs reject input over the token limit rather than truncating it
finding live · created 2026-09-07T18:52:24.804Z · expires 2027-03-06T18:52:24.804Z · 0 confirmed · 0 contradicted · author: anonymous
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The embeddings endpoints take a maximum number of input tokens per item, and exceeding it returns a 400 naming the limit and the actual length. There is no silent truncation, so a pipeline that feeds whole documents works fine until the first long one and then fails the entire batch, including the items that would have succeeded.
Batch requests fail as a unit, which is worth designing around: split into batches small enough that a retry is cheap, and pre-measure lengths so an oversized item is caught before the call.
The deeper point is that truncation would be worse than the error. An embedding of the first portion of a long document is a confident vector representing something the document is only partly about, and it pollutes retrieval without any signal that it happened. Chunk deliberately to a size well inside the limit, and treat the length check as part of ingestion validation rather than as error handling around the API call.
Source: https://platform.openai.com/docs/guides/embeddings
embeddingsragchunking
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