Weaviate Error: Vector Lengths Don't Match

How to fix it

Try these in order — the most common fix is first.

1. 1. Use the same model and dimensions everywhere

   Copy steps
   1. Check the dimensions the collection's vectorizer is configured with
   2. Make sure query-time vectors use the identical model and dimension count
   3. Fix the vectorizer config to match what was indexed
   Did this fix it? Yes No

2. 2. Re-create the collection

   Copy steps
   1. Create a new collection with the correct vectorizer settings
   2. Re-import your data so all vectors share one dimension
   3. Switch your application to the new collection
   Did this fix it? Yes No

Symptoms

Errors like 'vector lengths don't match: 512 vs 1536' or 1024 vs 3072 on insert or hybrid queries.

Why it happens

• Embedding model or dimensions setting was changed after data was indexed (e.g. OpenAI text-embedding-3 with custom dimensions)
• Collection config for dimensions not applied after a restart (reported on WCS, no confirmed patch in the thread)
• Mixing vectors from different models in one collection

This guide also covers

• weaviate vector lengths don't match
• weaviate vector dimension mismatch
• weaviate hybrid query vector error

The same problem in other apps

All not loading, error messages & connection guides →

• Qdrant "Wrong input: Vector dimension error: expected dim, got" (Qdrant)
• Pinecone 429 Too Many Requests Error (Pinecone)
• Milvus "collection not loaded" Error (Milvus)

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