# jina-embeddings-v2-small-en by Jina AI: Open-Weight Model
Source: https://savrn.com/models/jina-embeddings-v2-small-en
Markdown alternate of the page above; the site index is https://savrn.com/llms.txt

---

## Runs On

What it takes to serve jina-embeddings-v2-small-en (33M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
| --- | --- | --- | --- | --- | --- |
| 16-bit | 0.1 GB | 0.1 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 |
| 8-bit | 0.0 GB | 0.0 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 |
| 4-bit | 0.0 GB | 0.0 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 |

Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the [SAVRN Index](https://savrn.com/ai-index/pricing/gpus), read Oct 23, 2026.

[jina-embeddings-v2-small-en on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/jina-embeddings-v2-small-en/gpus)

## SAVRN's Notes on jina-embeddings-v2-small-en

Turning English text into vectors is the whole job here, and the 8,192 token window lets a full contract or filing go in as one piece. It is 33 million parameters in four layers, stored in float32, about 523 MB on disk. At 16-bit it needs 0.1 GB of memory, so the cheapest Index setup, one MI300X at $1.85 an hour, is far more card than the job calls for; in practice it shares a GPU with a generator, and the publisher also ships ONNX and CoreML builds.

Apache 2.0 permits commercial use, modification and redistribution, provided you keep the license and NOTICE files and state significant changes, and access is open. Before committing, confirm an English-only model fits your corpus, and read the ALiBi paper listed against it for how the 8,192 length is reached. The training relations name the C4 backbone and the publisher's negation dataset.

SAVRN Research, 2026-09-18

## Model Card

By Jina AI, published under apache-2.0, revision 44e7d1d6caec.

The text embedding set trained by [Jina AI](https://jina.ai/).

### Quick Start

The easiest way to starting using jina-embeddings-v2-small-en is to use Jina AI's [Embedding API](https://jina.ai/embeddings/).

### Intended Usage & Model Info

jina-embeddings-v2-small-en is an English, monolingual embedding model supporting 8192 sequence length. It is based on a BERT architecture (JinaBERT) that supports the symmetric bidirectional variant of [ALiBi](https://savrn.com/papers/train-short-test-long-attention-with-linear-biases-enables-input-length-extrapolation) to allow longer sequence length. The backbone jina-bert-v2-small-en is pretrained on the C4 dataset. The model is further trained on Jina AI's collection of more than 400 millions of sentence pairs and hard negatives. These pairs were obtained from various domains and were carefully selected through a thorough cleaning process.

[Read the full model card (784 words)](https://savrn.com/models/jina-embeddings-v2-small-en/card)

## Configuration

Architecture

JinaBertForMaskedLM

Context length (tokens)

8,192

Layers

4

Hidden size

512

Feed-forward size

2,048

Attention heads

8

Vocabulary size

30,528

Stored precision

float32

Model type

bert

## Identity and Version

Repository

jinaai/jina-embeddings-v2-small-en

Publisher

Jina AI

Task

Feature extraction

Modality

Text

Library

sentence-transformers

Parameters

33M parameters

Languages

en

Revision

44e7d1d6caec8c883c2d4b207588504d519788d0

First published

2023-09-27

Last updated

2025-01-06

## Files and Weights

19 files, 523.0 MB in total. The weights are 6 files totalling 522.0 MB in bin, mlmodel, onnx, safetensors.

Weights6 files · 522.0 MB

Configuration8 files · 2.6 KB

Tokenizer3 files · 943.5 KB

Documentation1 file · 70.6 KB

Repository1 file · 1.5 KB

Every file

| File | Type | Size | SHA-256 |
| --- | --- | --- | --- |
| coreml/float32_model.mlpackage/Data/com.apple.CoreML/model.mlmodel | Weights | 48.7 KB | df2a5079c312 |
| coreml/float32_model.mlpackage/Data/com.apple.CoreML/weights/weight.bin | Weights | 131.6 MB | ab10bc04d4f1 |
| model-w-mean-pooling.onnx | Weights | 129.8 MB | c5412c83114b |
| model.onnx | Weights | 129.8 MB | 974fdefe71fc |
| model.safetensors | Weights | 65.4 MB | c9a9a7ec012d |
| pytorch_model.bin | Weights | 65.4 MB | 54c94f036922 |
| 1_Pooling/config.json | Configuration | 190 B | — |
| config.json | Configuration | 1.2 KB | — |
| config_sentence_transformers.json | Configuration | 117 B | — |
| coreml/float32_model.mlpackage/Manifest.json | Configuration | 617 B | — |
| generation_config.json | Configuration | 90 B | — |
| modules.json | Configuration | 229 B | — |
| sentence_bert_config.json | Configuration | 99 B | — |
| special_tokens_map.json | Configuration | 125 B | — |
| README.md | Documentation | 70.6 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
| tokenizer.json | Tokenizer | 711.6 KB | — |
| tokenizer_config.json | Tokenizer | 373 B | — |
| vocab.txt | Tokenizer | 231.6 KB | — |

## License and Download

License

apache-2.0

Access

Open weights, no gate

Download size

522.0 MB

[Download from Jina AI](https://huggingface.co/jinaai/jina-embeddings-v2-small-en)

Released by Jina AI through its official repository on Hugging Face. [Read the license](https://www.apache.org/licenses/LICENSE-2.0).

## Built From

- Described by [arXiv:2108.12409](https://savrn.com/papers/train-short-test-long-attention-with-linear-biases-enables-input-length-extrapolation)
- Described by [arXiv:2310.19923](https://savrn.com/papers/jina-embeddings-2-8192-token-general-purpose-text-embeddings-for-long-documents)
- Trained on (disclosed) jinaai/negation-dataset

## Evaluations

Each result is shown as reported, with the conditions its reporter stated. None is a SAVRN measurement. A comparison lines two results up only when their configuration, unit and setup are all stated and identical.

| Benchmark | Conditions | Result | Reported by | Revision | Date |
| --- | --- | --- | --- | --- | --- |
| MTEB AmazonCounterfactualClassification (en) | Configuration enTask ClassificationMetric accuracyComparison conditions not established | 71.3582 | [jinaai](https://huggingface.co/jinaai/jina-embeddings-v2-small-en) Publisher reported | Evaluated revision not stated | — |
| MTEB AmazonCounterfactualClassification (en) | Configuration enTask ClassificationMetric apComparison conditions not established | 33.9993 | [jinaai](https://huggingface.co/jinaai/jina-embeddings-v2-small-en) Publisher reported | Evaluated revision not stated | — |
| MTEB AmazonCounterfactualClassification (en) | Configuration enTask ClassificationMetric f1Comparison conditions not established | 65.3854 | [jinaai](https://huggingface.co/jinaai/jina-embeddings-v2-small-en) Publisher reported | Evaluated revision not stated | — |
| MTEB AmazonPolarityClassification | Configuration defaultTask ClassificationMetric accuracyComparison conditions not established | 82.9014 | [jinaai](https://huggingface.co/jinaai/jina-embeddings-v2-small-en) Publisher reported | Evaluated revision not stated | — |
| MTEB AmazonPolarityClassification | Configuration defaultTask ClassificationMetric apComparison conditions not established | 78.0143 | [jinaai](https://huggingface.co/jinaai/jina-embeddings-v2-small-en) Publisher reported | Evaluated revision not stated | — |
| MTEB AmazonPolarityClassification | Configuration defaultTask ClassificationMetric f1Comparison conditions not established | 82.8336 | [jinaai](https://huggingface.co/jinaai/jina-embeddings-v2-small-en) Publisher reported | Evaluated revision not stated | — |
| MTEB AmazonReviewsClassification (en) | Configuration enTask ClassificationMetric accuracyComparison conditions not established | 40.89 | [jinaai](https://huggingface.co/jinaai/jina-embeddings-v2-small-en) Publisher reported | Evaluated revision not stated | — |
| MTEB AmazonReviewsClassification (en) | Configuration enTask ClassificationMetric f1Comparison conditions not established | 39.2094 | [jinaai](https://huggingface.co/jinaai/jina-embeddings-v2-small-en) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric map_at_1Comparison conditions not established | 23.257 | [jinaai](https://huggingface.co/jinaai/jina-embeddings-v2-small-en) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric map_at_10Comparison conditions not established | 37.946 | [jinaai](https://huggingface.co/jinaai/jina-embeddings-v2-small-en) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric map_at_100Comparison conditions not established | 39.17 | [jinaai](https://huggingface.co/jinaai/jina-embeddings-v2-small-en) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric map_at_1000Comparison conditions not established | 39.181 | [jinaai](https://huggingface.co/jinaai/jina-embeddings-v2-small-en) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric map_at_3Comparison conditions not established | 32.99 | [jinaai](https://huggingface.co/jinaai/jina-embeddings-v2-small-en) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric map_at_5Comparison conditions not established | 35.468 | [jinaai](https://huggingface.co/jinaai/jina-embeddings-v2-small-en) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric mrr_at_1Comparison conditions not established | 23.542 | [jinaai](https://huggingface.co/jinaai/jina-embeddings-v2-small-en) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric mrr_at_10Comparison conditions not established | 38.057 | [jinaai](https://huggingface.co/jinaai/jina-embeddings-v2-small-en) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric mrr_at_100Comparison conditions not established | 39.289 | [jinaai](https://huggingface.co/jinaai/jina-embeddings-v2-small-en) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric mrr_at_1000Comparison conditions not established | 39.299 | [jinaai](https://huggingface.co/jinaai/jina-embeddings-v2-small-en) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric mrr_at_3Comparison conditions not established | 33.096 | [jinaai](https://huggingface.co/jinaai/jina-embeddings-v2-small-en) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric mrr_at_5Comparison conditions not established | 35.628 | [jinaai](https://huggingface.co/jinaai/jina-embeddings-v2-small-en) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric ndcg_at_1Comparison conditions not established | 23.257 | [jinaai](https://huggingface.co/jinaai/jina-embeddings-v2-small-en) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric ndcg_at_10Comparison conditions not established | 46.729 | [jinaai](https://huggingface.co/jinaai/jina-embeddings-v2-small-en) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric ndcg_at_100Comparison conditions not established | 51.901 | [jinaai](https://huggingface.co/jinaai/jina-embeddings-v2-small-en) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric ndcg_at_1000Comparison conditions not established | 52.16 | [jinaai](https://huggingface.co/jinaai/jina-embeddings-v2-small-en) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric ndcg_at_3Comparison conditions not established | 36.323 | [jinaai](https://huggingface.co/jinaai/jina-embeddings-v2-small-en) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric ndcg_at_5Comparison conditions not established | 40.767 | [jinaai](https://huggingface.co/jinaai/jina-embeddings-v2-small-en) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric precision_at_1Comparison conditions not established | 23.257 | [jinaai](https://huggingface.co/jinaai/jina-embeddings-v2-small-en) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric precision_at_10Comparison conditions not established | 7.511 | [jinaai](https://huggingface.co/jinaai/jina-embeddings-v2-small-en) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric precision_at_100Comparison conditions not established | 0.976 | [jinaai](https://huggingface.co/jinaai/jina-embeddings-v2-small-en) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric precision_at_1000Comparison conditions not established | 0.1 | [jinaai](https://huggingface.co/jinaai/jina-embeddings-v2-small-en) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric precision_at_3Comparison conditions not established | 15.339 | [jinaai](https://huggingface.co/jinaai/jina-embeddings-v2-small-en) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric precision_at_5Comparison conditions not established | 11.351 | [jinaai](https://huggingface.co/jinaai/jina-embeddings-v2-small-en) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric recall_at_1Comparison conditions not established | 23.257 | [jinaai](https://huggingface.co/jinaai/jina-embeddings-v2-small-en) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric recall_at_10Comparison conditions not established | 75.107 | [jinaai](https://huggingface.co/jinaai/jina-embeddings-v2-small-en) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric recall_at_100Comparison conditions not established | 97.582 | [jinaai](https://huggingface.co/jinaai/jina-embeddings-v2-small-en) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric recall_at_1000Comparison conditions not established | 99.573 | [jinaai](https://huggingface.co/jinaai/jina-embeddings-v2-small-en) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric recall_at_3Comparison conditions not established | 46.017 | [jinaai](https://huggingface.co/jinaai/jina-embeddings-v2-small-en) Publisher reported | Evaluated revision not stated | — |
| MTEB ArguAna | Configuration defaultTask RetrievalMetric recall_at_5Comparison conditions not established | 56.757 | [jinaai](https://huggingface.co/jinaai/jina-embeddings-v2-small-en) Publisher reported | Evaluated revision not stated | — |
| MTEB ArxivClusteringP2P | Configuration defaultTask ClusteringMetric v_measureComparison conditions not established | 44.0242 | [jinaai](https://huggingface.co/jinaai/jina-embeddings-v2-small-en) Publisher reported | Evaluated revision not stated | — |
| MTEB ArxivClusteringS2S | Configuration defaultTask ClusteringMetric v_measureComparison conditions not established | 35.1614 | [jinaai](https://huggingface.co/jinaai/jina-embeddings-v2-small-en) Publisher reported | Evaluated revision not stated | — |

## Memory Requirements

| Precision | Weights in memory |
| --- | --- |
| As published | 522.0 MB |
| 16-bit | 0.1 GB |
| 8-bit | 0.0 GB |
| 4-bit | 0.0 GB |

Weights only, from the published parameter count; the key-value cache and runtime add to this.

## Compare jina-embeddings-v2-small-en

- [bge-small-en-v1.5 vs jina-embeddings-v2-small-en](https://savrn.com/models/compare/bge-small-en-v1-5-vs-jina-embeddings-v2-small-en)
- [bge-small-zh-v1.5 vs jina-embeddings-v2-small-en](https://savrn.com/models/compare/bge-small-zh-v1-5-vs-jina-embeddings-v2-small-en)
- [granite-embedding-small-english-r2 vs jina-embeddings-v2-small-en](https://savrn.com/models/compare/granite-embedding-small-english-r2-vs-jina-embeddings-v2-small-en)

## Questions About jina-embeddings-v2-small-en

### How much GPU memory does jina-embeddings-v2-small-en need?

About 0.1 GB at 16-bit and 0 GB at 4-bit: the weights (33M parameters) plus a working margin. A long context needs more.

### What is the cheapest GPU to run jina-embeddings-v2-small-en on?

At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

### Can I use jina-embeddings-v2-small-en commercially?

Yes. jina-embeddings-v2-small-en is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.

### What is jina-embeddings-v2-small-en's context length?

8,192 tokens, from the maximum position embeddings in its published configuration.

## Similar Models

Model · Feature extraction

### [bge-small-en-v1.5](https://savrn.com/models/bge-small-en-v1-5)

[Beijing Academy of Artificial Intelligence](https://savrn.com/model-publishers/baai)

More details please refer to our Github: FlagEmbedding. If you are looking for a model that supports more languages, longer texts, and other retrieval methods, you can try using bge-m3. FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently: - 1/30/2024: Release BGE-M3, a new member to BGE model series! M3 stands for Multi-linguality (100+ languages), Multi-granularities (input length up to 8192), Multi-Functionality (unification of dense, lexical, multi-vec/colbert retrieval). It is the first embedding model which supports all three retrieval methods, achieving new SOTA on multi-lingual (MIRACL) and cross-lingual (MKQA) benchmarks. Technical…

Open weights mit 33M parameters 512 tokens sentence-transformers

[View model](https://savrn.com/models/bge-small-en-v1-5)

Model · Feature extraction

### [starwell](https://savrn.com/models/starwell)

[Charles Norton](https://savrn.com/model-publishers/phanerozoic)

Starwell is a generative model for stellarator design. Given a number of field periods, an aspect ratio and a target magnetic-gradient scale length LgradB, it samples a plasma boundary for the mhdstable problem of the ConStellaration benchmark, predicts its benchmark metrics, and lays out modular coils for it. Boundaries are SurfaceRZFourier dicts (rcos, zsin) that constellaration evaluates directly; sample also takes tau, guidance, steps and seed, and model.config.sampling holds the integrator's defaults. Coils are simsopt CurveXYZFourier coefficients for one half period with their currents, and simsopt.field.coilsviasymmetries builds the full set; limits (in metres and per metre) sets the…

Open weights mit 26M parameters transformers

[View model](https://savrn.com/models/starwell)

Model · Feature extraction

### [Corpus-ViT](https://savrn.com/models/corpus-vit)

[Gugu8](https://savrn.com/model-publishers/gugu8)

A vision encoder with a runtime-selectable token budget, built to sit between an image and an LLM. A coarse 32px grid covers the whole image; a router spends extra 16px detail only on the regions that need it. Output length is 49 + 3k tokens — 49 at k=0, 121 at k=24 — so you choose the tokens/fidelity trade-off per image, at inference, with no retraining. 24.94M parameters. Trained in 6000 steps (~30 min) on 2× Kaggle T4 by distillation from google/siglip-base-patch16-224 (86M). This is a working, honest baseline, not a competitive encoder. On the Flickr30k official test split it reaches 8.95 mean R@1 against the teacher's 83.25 — roughly 10% of its teacher. Chance on 1000 images is 0.10.…

Open weights apache-2.0 25M parameters transformers

[View model](https://savrn.com/models/corpus-vit)

Model · Feature extraction

### [jano-tts](https://savrn.com/models/jano-tts)

[Jano](https://savrn.com/model-publishers/jano3)

This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Open weights 25M parameters transformers

[View model](https://savrn.com/models/jano-tts)

Model · Feature extraction

### [ToarimEmbedder](https://savrn.com/models/toarimembedder)

[Manuel Antonio Camacho Reyes](https://savrn.com/model-publishers/manycamds)

TóarimEmbedder es una arquitectura algorítmica de embeddings con estado (stateful) diseñada específicamente para resolver la amnesia contextual y la pérdida de continuidad en sistemas de Recuperación Aumentada por Generación (RAG) aplicados a corpus técnicos extensos y altamente regulados (manuales de maquinaria pesada, compendios normativos, bitácoras operativas y expedientes legales). A diferencia de los modelos basados en Transformers convencionales con atención densa cuadrática $O(n^2)$, TóarimEmbedder opera con complejidad lineal $O(n)$ en procesadores convencionales (CPU), permitiendo a las organizaciones indexar internamente su propiedad intelectual de forma ágil, blindando sus…

Open weights apache-2.0 24M parameters transformers

[View model](https://savrn.com/models/toarimembedder)

Model · Feature extraction

### [bge-small-zh-v1.5](https://savrn.com/models/bge-small-zh-v1-5)

[Beijing Academy of Artificial Intelligence](https://savrn.com/model-publishers/baai)

More details please refer to our Github: FlagEmbedding. FlagEmbedding can map any text to a low-dimensional dense vector which can be used for tasks like retrieval, classification, clustering, or semantic search. And it also can be used in vector databases for LLMs. Updates - 10/12/2023: Release LLM-Embedder, a unified embedding model to support diverse retrieval augmentation needs for LLMs. Paper:fire: - 09/15/2023: The technical report of BGE has been released - 09/15/2023: The masive training data of BGE has been released - 09/12/2023: New models: - 09/07/2023: Update fine-tune code: Add script to mine hard negatives and support adding instruction during fine-tuning. - 08/09/2023: BGE…

Open weights mit 24M parameters 512 tokens transformers

[View model](https://savrn.com/models/bge-small-zh-v1-5)

## Jina AI

[All models and datasets](https://savrn.com/model-publishers/jinaai)

## Versions

- [44e7d1d6caec](https://savrn.com/models/jina-embeddings-v2-small-en/versions/44e7d1d6caec) · current 2026-10-23

## Explore More

- [All feature extraction models](https://savrn.com/models/tasks/feature-extraction)
- [All models under apache-2.0](https://savrn.com/models/licenses/apache-2-0)
- [Model comparisons](https://savrn.com/models/comparisons)
- [The model directory](https://savrn.com/models)
- [Open model prices by host](https://savrn.com/ai-index/pricing/open-models)

## Source

- Repository metadata, read 2026-10-19.
- [Jina AI website](https://jina.ai/models/)
- [Hugging Face record](https://huggingface.co/jinaai/jina-embeddings-v2-small-en)
- [How the hub is built](https://savrn.com/model-hub/methodology)