Gemini Embedding vs Mistral Embed and Codestral Embed

Gemini Embedding

Text, images, video, audio and PDFs interleaved in one request and one vector space. $0.20 per million text tokens, against $0.02 for OpenAI's small model.

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.

Gemini Embedding

1. Don't send task_type to gemini-embedding-2. Prefix the text instead, task: search result | query: ... for queries and title: ... | text: ... for documents
2. Ask for output_dimensionality 768 unless you need 3072. Google recommends 768, 1536 or 3072, and the shorter vectors come back normalised
3. Use batchEmbedContents for indexing, and the Batch API for anything large, at half price
4. Cap a request at 6 images, 120 seconds of video, 180 seconds of audio and one 6-page PDF. Split longer media first
5. Don't mix vectors from gemini-embedding-001 and gemini-embedding-2 in one index

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