Tasks and inputs
Llama Nemotron Embed VL 1B v2 is listed for Embeddings. It accepts text, image and produces text. Use the publisher’s input format and preprocessing for your chosen deployment.
SyncDev model directory · Profile updated Oct 13, 2026
Llama Nemotron Embed VL 1B v2 from NVIDIA: Embedding model for semantic search, retrieval, clustering, and ranking pipelines.
The specification score summarizes recorded features and limits. It is not a benchmark result.
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 nemotron family with a release date of 2026-02-10. This profile describes its documented interface and how to evaluate it for the intended task.
The recorded inputs are text and image, with text output. The registry lists a 32,768-token context window and up to 2,048 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.
The registry marks Llama Nemotron Embed VL 1B v2 as an open-weight model. Check the linked checkpoint’s licence and runtime requirements before downloading or deploying it. Open weights describe availability, while licence terms determine permitted use. Local inference still requires hardware; a hosted route can charge separately.
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 Llama Nemotron Embed VL 1B v2.
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Llama Nemotron Embed VL 1B v2 is listed for Embeddings. It accepts text, image and produces text. Use the publisher’s input format and preprocessing for your chosen deployment.
Documented limits: 32.77K context and 2.05K maximum output. The listed route charges $0.00 input and $0.00 output per million tokens. Measure a completed task using the prompts, documents and tools your application will use. Include reasoning and retries in the result.
The weights are downloadable under the publisher’s model licence. Check runtime support and size the complete model and working context for your expected concurrency.
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.
Gemini Embedding 001 from Google: Embedding model for semantic search, retrieval, clustering, and ranking pipelines.
Mistral AI
Mistral Embed from Mistral AI: Embedding model for semantic search, retrieval, clustering, and ranking pipelines.