Reranking model for improving retrieval quality in search and recommendation systems
https://openrouter.ai/api/v1 nvidia/llama-nemotron-rerank-vl-1b-v2:free NVIDIA: Llama Nemotron Rerank VL 1B V2 (free) is listed for chat, reasoning, embedding workloads and supports a 10K context window.
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We only found this NVIDIA: Llama Nemotron Rerank VL 1B V2 (free) listing on OpenRouter in the current catalog. Use the related models section for nearby alternatives.
| Provider | Model listing | Access | Context | API | Limits |
|---|---|---|---|---|---|
| | NVIDIA: Llama Nemotron Rerank VL 1B V2 (free) | Check provider | 10K | OpenAI-style | 200 req/day (free tier) |
| Model | Provider | Context | Access |
|---|---|---|---|
| Poolside: Laguna S 2.1 (free) | OpenRouter | 262K | Free tier |
| Poolside: Laguna XS 2.1 (free) | OpenRouter | 262K | Free tier |
| Cohere: North Mini Code (free) | OpenRouter | 256K | Free tier |
| NVIDIA: Nemotron 3.5 Content Safety (free) | OpenRouter | 128K | Free tier |
| NVIDIA: Nemotron 3 Ultra (free) | OpenRouter | 1.0M | Free tier |
NVIDIA: Llama Nemotron Rerank VL 1B V2 (free) is tagged for chat in this catalog and works with OpenAI-compatible client libraries.
NVIDIA: Llama Nemotron Rerank VL 1B V2 (free) is tagged for reasoning in this catalog and works with OpenAI-compatible client libraries.
NVIDIA: Llama Nemotron Rerank VL 1B V2 (free) is tagged for embedding in this catalog and works with OpenAI-compatible client libraries.
NVIDIA: Llama Nemotron Rerank VL 1B V2 (free) appears in the free model catalog for OpenRouter, but its current endpoint availability should be confirmed with the provider before use.
The model ID shown in this catalog is nvidia/llama-nemotron-rerank-vl-1b-v2:free.
The listed free tier limit is 200 req/day (free tier). Limits can change per account tier, so confirm against the provider dashboard.
The listed context window is 10K tokens with up to 8K output tokens.
Llama Nemotron Rerank VL 1B V2 is a 1.7B multimodal reranking model from NVIDIA. It evaluates the relevance of document images and text against user queries, designed for vision RAG pipelines handling charts, tables, infographics, and mixed-media documents. Functions as a cross-encoder that accepts text queries paired with image, text, or combined document inputs, delivering approximately 6-7% recall improvements over embedding-only baselines on visual document retrieval benchmarks.
For API keys, setup steps, and provider-level limits, see the OpenRouter provider page.