Mistral Embed and Codestral Embed vs OpenAI embeddings

Mistral Embed and Codestral Embed

EU and US regional endpoints and a French legal entity. Embedding API uptime of 94.36 per cent over 90 days on Mistral's status page, with incidents on 12 and 27 August 2026.

OpenAI embeddings

text-embedding-3-small at $0.02 per million tokens, $0.01 through the Batch API. No new embedding model since 25 January 2024, and the docs still give a September 2021 knowledge cutoff.

Mistral Embed and Codestral Embed

1. Use codestral-embed whenever you want smaller or binary vectors. mistral-embed has no output options
2. Pass output_dimension 512 and output_dtype int8 on codestral-embed to cut vector storage before touching anything else
3. Keep chunks under 8k tokens. There's no long-context embedding model on this API
4. Check status.mistral.ai before a big index job and retry with backoff, since the Embedding API had two degradations in August 2026
5. Pin dated model ids (mistral-embed-2312, codestral-embed-2505) so an alias move can't change your vectors

OpenAI embeddings

1. Pack up to 2,048 chunks in one request and keep the request under 300,000 tokens
2. Count tokens before sending. An input over 8,192 tokens is rejected, not truncated
3. Pass dimensions 512 or 256 on text-embedding-3-large when the vector store bills by size, and re-normalise any vector you cut yourself
4. Split a Batch API index job into batches of under 50,000 inputs. It's half price with a 24-hour window
5. Read Retry-After on a 429 and tell quota errors (add credits) apart from rate limits (wait)