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Most embeddings issues are request-shape or model-capability mismatches. See Embeddings for the full reference.

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400 model_capability_not_supported

The model you named isn’t an embedding model. Pick one where GET /v1/models shows supports_embeddings: true.

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400 unprocessable_entity with model “auto”

Embeddings doesn’t support Auto Routing. model: "auto" returns 400 unprocessable_entity; name a specific embedding model.

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422 model missing or invalid field

  • model is required unless your key has a default model configured.
  • dimensions only works on models that support truncation — sending it to a model that doesn’t will be rejected.
  • input_type, instructions, and sparse_embedding are provider-specific (e.g. asymmetric models, BytePlus), and a model that doesn’t support them may reject the request.
  • sparse_embedding is an object ({"type": "enabled"}), not a boolean. true fails validation.

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Results seem mismatched to my inputs

When you pass an array of strings, the returned data array is in the same order as your input. Match by index, and don’t assume the API reorders or dedupes — it doesn’t.

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Input too long

Each model has a context_length. Inputs longer than that are rejected — check the model’s context_length via GET /v1/models and chunk long text before embedding. (Behavior inferred from the documented context_length field.)

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Garbled vectors / wrong format

encoding_format controls the output: "float" returns a numeric array, "base64" returns a packed string you must decode. If your vectors look like gibberish, you’re probably reading base64 output as floats.

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Still stuck?

See the Mesh API error reference or email contact@meshapi.ai.