Model Detail
jina-embeddings-v5-omni-small
—jina-embeddings-v5-omni-small is a code generation model with 813M parameters released by jinaai. The model is registered under the feature-extraction pipeline tag on Hugging Face, distributed under the permissive cc-by-nc-4.0 license.
jina-embeddings-v5-omni-small ships with 813M parameters. Total weight footprint is approximately 1.6 GB, which is the relevant figure when planning local-inference VRAM. The cc-by-nc-4.0 license is permissive, allowing commercial deployment and derivative work without per-seat fees, though attribution requirements still apply.
jina-embeddings-v5-omni-small is best fit for code completion, repository-scale Q&A, and pair-programming integrations, and semantic search, retrieval, and clustering pipelines. It is a less obvious choice for one-shot generation of security-critical code without review. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
Jina-OCR-v1: Efficient Document Parsing with Speculative Decoding and Dense Verifiable Rewards
arXiv:2609.03181v1 Announce Type: new Abstract: We present Jina-OCR-v1, an end-to-end document parsing model built to serve on low-budget GPUs. It combines the compressed-vision encoder and the 3B mixture-of-experts decoder of DeepSeek-OCR, which activates about 570M parameters per token, with a Fas
jina-embeddings-v5-omni: Geometry-preserving Embeddings via Locked Aligned Towers
arXiv:2605.08384v2 Announce Type: replace Abstract: In this work, we introduce GELATO (Geometry-preserving Embeddings via Locked Aligned TOwers), a novel approach to multimodal embedding models. We build on the VLM-style architecture, in which non-text encoders are adapted to produce input for a lan
jina-embeddings-v5-omni: Text-Geometry-Preserving Multimodal Embeddings via Frozen-Tower Composition
arXiv:2605.08384v1 Announce Type: new Abstract: In this work, we introduce frozen-encoder model composition, a novel approach to multimodal embedding models. We build on the VLM-style architecture, in which non-text encoders are adapted to produce input for a language model, which in turn generates
jina-vlm: Small Multilingual Vision Language Model
arXiv:2512.04032v3 Announce Type: replace-cross Abstract: We present jina-vlm, a token-efficient 2.4B parameter vision-language model that achieves state-of-the-art multilingual VQA performance among open 2B-scale VLMs. The model couples a SigLIP2 vision encoder with a Qwen3 language decoder and mak
jina-embeddings-v5-text: Task-Targeted Embedding Distillation
arXiv:2602.15547v2 Announce Type: replace Abstract: Text embedding models are widely used for semantic similarity tasks, including information retrieval, clustering, and classification. General-purpose models are typically trained with single- or multi-stage processes using contrastive loss function
