Tasks and inputs
Mistral Embed is listed for Embeddings. It accepts text and produces text. Use the publisher’s input format and preprocessing for your chosen deployment.
Mistral Embed from Mistral AI: Embedding model for semantic search, retrieval, clustering, and ranking pipelines.
Recorded modality support shows what this model can accept and produce. An undocumented modality is shown as unknown rather than assumed to be unsupported.
Accepts
Produces
Embedding model for semantic search, retrieval, clustering, and ranking pipelines. The registry places it in the mistral-embed family with a release date of 2023-12-11. This profile describes its documented interface and how to evaluate it for the intended task.
The recorded inputs are text, with text output. The registry lists a 8,000-token context window and up to 3,072 output tokens. Additional capability flags are not established by this record. These are specifications, not a measured quality or speed ranking. A host can impose its own request limits and support a different subset of features.
Mistral Embed is recorded as a hosted model without downloadable weights. Confirm the current model ID and access conditions on the chosen API. A registry listing alone does not establish that an account or region has access.
No independently checked token rate is added from a hosting alias. Use the route’s current price and billing unit when comparing cost, including output, reasoning, cache behavior and retries where applicable.
Evaluate retrieval with representative queries and known relevant documents. Keep chunking, distance metric and index configuration consistent. Changing an embedding model usually requires rebuilding the index; vector dimensions and similarity behavior need to match the stored corpus.
This is a registry-sourced profile. A model-specific publisher document has not yet been confirmed; access and specifications should be checked before an integration decision. SyncDev has not independently benchmarked Mistral Embed.
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Mistral Embed is listed for Embeddings. It accepts text and produces text. Use the publisher’s input format and preprocessing for your chosen deployment.
Documented limits: 8K context and 3.07K maximum output. Measure a completed task using the prompts, documents and tools your application will use. Include reasoning and retries in the result.
Use the model identifier and account requirements for the selected API route. Confirm its request limits and output format with one complete task before connecting a larger workflow.
These records share a directory category. Check the source card, output type and deployment requirements before treating a model as a replacement.
Gemini Embedding 2 from Google: Multimodal embedding model mapping text, images, video, audio, and PDFs into a unified embedding space.
NVIDIA
Llama Nemotron Embed VL 1B v2 from NVIDIA: Embedding model for semantic search, retrieval, clustering, and ranking pipelines.
Gemini Embedding 001 from Google: Embedding model for semantic search, retrieval, clustering, and ranking pipelines.