# mxbai-embed-large-v1 by Mixedbread: Open-Weight Model Source: https://savrn.com/models/mxbai-embed-large-v1 Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve mxbai-embed-large-v1 (335M 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 | 0.7 GB | 0.8 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 | 0.3 GB | 0.4 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 | 0.2 GB | 0.2 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 25, 2026. [mxbai-embed-large-v1 on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/mxbai-embed-large-v1/gpus) ## SAVRN's Notes on mxbai-embed-large-v1 Every query you send this embedding model for retrieval needs the publisher's search prompt ahead of it; passages need none. Sizing is easy: 335M parameters, 0.8 GB of memory at 16-bit, and at $1.85 per hour on-demand for a single 192 GB MI300X, our cheapest listed option, the card spends most of its life waiting for text. The 512 token context is the practical limit, a short passage per call; plan your chunking around it. Deployment is simple under Apache 2.0: commercial use, modification and redistribution are permitted, you keep the license, copyright notices and any NOTICE file, state significant changes, and contributors give an express patent grant. Before committing, note the download is 21 files and 5.36 GB across safetensors, gguf, onnx and openvino, so pull only what your stack reads, and that the evaluations are publisher-reported MTEB classification numbers; run your own retrieval set first. SAVRN Research, 2026-09-18 ## Model Card By Mixedbread, published under apache-2.0, revision b33106f585b9. The crispy sentence embedding family from [Mixedbread](https://mixedbread.com). Looking for a simple end-to-end retrieval solution? Meet Omni, our multimodal and multilingual model.[Get in touch for access.](https://mixedbread.com) ### mixedbread-ai/mxbai-embed-large-v1 Here, we provide several ways to produce sentence embeddings. Please note that you have to provide the prompt Represent this sentence for searching relevant passages: for query if you want to use it for retrieval. Besides that you don't need any prompt. Our model also supports [Matryoshka Representation Learning and binary quantization](https://www.mixedbread.ai/blog/binary-mrl). ### Quickstart Here, we provide several ways to produce sentence embeddings. Please note that you have to provide the prompt Represent this sentence for searching relevant passages: for query if you want to use it for retrieval. Besides that you don't need any prompt. #### sentence-transformers ``` python -m pip install -U sentence-transformers ``` [Read the full model card (1,071 words)](https://savrn.com/models/mxbai-embed-large-v1/card) ## Configuration Architecture BertModel Context length (tokens) 512 Layers 24 Hidden size 1,024 Feed-forward size 4,096 Attention heads 16 Vocabulary size 30,522 Stored precision float16 Model type bert ## Identity and Version Repository mixedbread-ai/mxbai-embed-large-v1 Publisher Mixedbread Task Feature extraction Modality Text Library sentence-transformers Parameters 335M parameters Languages en Revision b33106f585b9ce46904ad7443a3b52b7a63e231c First published 2024-03-07 Last updated 2026-01-23 ## Files and Weights 21 files, 5.4 GB in total. The weights are 7 files totalling 5.4 GB in bin, gguf, onnx, safetensors. Weights7 files · 5.4 GB Configuration6 files · 2.2 KB Tokenizer3 files · 944.1 KB Documentation2 files · 125.0 KB Other2 files · 2.0 MB Repository1 file · 1.6 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | gguf/mxbai-embed-large-v1-f16.gguf | Weights | 669.6 MB | 819c2adf5ce6 | | model.safetensors | Weights | 670.3 MB | 36bfa45da00e | | onnx/model.onnx | Weights | 1.3 GB | adb53ed475fa | | onnx/model_fp16.onnx | Weights | 668.8 MB | b08af11a6045 | | onnx/model_quantized.onnx | Weights | 337.0 MB | 11bda26d2ee7 | | openvino/openvino_model.bin | Weights | 1.3 GB | d2715fe5d778 | | openvino/openvino_model_qint8_quantized.bin | Weights | 336.8 MB | 61df334e52da | | 1_Pooling/config.json | Configuration | 297 B | — | | config.json | Configuration | 677 B | — | | config_sentence_transformers.json | Configuration | 266 B | — | | modules.json | Configuration | 229 B | — | | sentence_bert_config.json | Configuration | 53 B | — | | special_tokens_map.json | Configuration | 695 B | — | | LICENSE | Documentation | 10.8 KB | — | | README.md | Documentation | 114.3 KB | — | | openvino/openvino_model.xml | Other | 707.3 KB | — | | openvino/openvino_model_qint8_quantized.xml | Other | 1.3 MB | — | | .gitattributes | Repository | 1.6 KB | — | | tokenizer.json | Tokenizer | 711.4 KB | — | | tokenizer_config.json | Tokenizer | 1.2 KB | — | | vocab.txt | Tokenizer | 231.5 KB | — | ## License and Download License apache-2.0 Access Open weights, no gate Download size 5.4 GB [Download from Mixedbread](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) Released by Mixedbread through its official repository on Hugging Face. [Read the license](https://www.apache.org/licenses/LICENSE-2.0). ## Built From - Described by [arXiv:2309.12871](https://savrn.com/papers/angle-optimized-text-embeddings) ## 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 | 75.0448 | [mixedbread-ai](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) Publisher reported | Evaluated revision not stated | — | | MTEB AmazonCounterfactualClassification (en) | Configuration enTask ClassificationMetric apComparison conditions not established | 37.7362 | [mixedbread-ai](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) Publisher reported | Evaluated revision not stated | — | | MTEB AmazonCounterfactualClassification (en) | Configuration enTask ClassificationMetric f1Comparison conditions not established | 68.9274 | [mixedbread-ai](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) Publisher reported | Evaluated revision not stated | — | | MTEB AmazonPolarityClassification | Configuration defaultTask ClassificationMetric accuracyComparison conditions not established | 93.8403 | [mixedbread-ai](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) Publisher reported | Evaluated revision not stated | — | | MTEB AmazonPolarityClassification | Configuration defaultTask ClassificationMetric apComparison conditions not established | 90.9319 | [mixedbread-ai](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) Publisher reported | Evaluated revision not stated | — | | MTEB AmazonPolarityClassification | Configuration defaultTask ClassificationMetric f1Comparison conditions not established | 93.8298 | [mixedbread-ai](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) Publisher reported | Evaluated revision not stated | — | | MTEB AmazonReviewsClassification (en) | Configuration enTask ClassificationMetric accuracyComparison conditions not established | 49.184 | [mixedbread-ai](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) Publisher reported | Evaluated revision not stated | — | | MTEB AmazonReviewsClassification (en) | Configuration enTask ClassificationMetric f1Comparison conditions not established | 48.7416 | [mixedbread-ai](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) Publisher reported | Evaluated revision not stated | — | | MTEB ArguAna | Configuration defaultTask RetrievalMetric map_at_1Comparison conditions not established | 41.252 | [mixedbread-ai](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) Publisher reported | Evaluated revision not stated | — | | MTEB ArguAna | Configuration defaultTask RetrievalMetric map_at_10Comparison conditions not established | 57.778 | [mixedbread-ai](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) Publisher reported | Evaluated revision not stated | — | | MTEB ArguAna | Configuration defaultTask RetrievalMetric map_at_100Comparison conditions not established | 58.233 | [mixedbread-ai](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) Publisher reported | Evaluated revision not stated | — | | MTEB ArguAna | Configuration defaultTask RetrievalMetric map_at_1000Comparison conditions not established | 58.237 | [mixedbread-ai](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) Publisher reported | Evaluated revision not stated | — | | MTEB ArguAna | Configuration defaultTask RetrievalMetric map_at_3Comparison conditions not established | 53.45 | [mixedbread-ai](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) Publisher reported | Evaluated revision not stated | — | | MTEB ArguAna | Configuration defaultTask RetrievalMetric map_at_5Comparison conditions not established | 56.376 | [mixedbread-ai](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) Publisher reported | Evaluated revision not stated | — | | MTEB ArguAna | Configuration defaultTask RetrievalMetric mrr_at_1Comparison conditions not established | 41.679 | [mixedbread-ai](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) Publisher reported | Evaluated revision not stated | — | | MTEB ArguAna | Configuration defaultTask RetrievalMetric mrr_at_10Comparison conditions not established | 57.927 | [mixedbread-ai](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) Publisher reported | Evaluated revision not stated | — | | MTEB ArguAna | Configuration defaultTask RetrievalMetric mrr_at_100Comparison conditions not established | 58.389 | [mixedbread-ai](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) Publisher reported | Evaluated revision not stated | — | | MTEB ArguAna | Configuration defaultTask RetrievalMetric mrr_at_1000Comparison conditions not established | 58.392 | [mixedbread-ai](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) Publisher reported | Evaluated revision not stated | — | | MTEB ArguAna | Configuration defaultTask RetrievalMetric mrr_at_3Comparison conditions not established | 53.651 | [mixedbread-ai](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) Publisher reported | Evaluated revision not stated | — | | MTEB ArguAna | Configuration defaultTask RetrievalMetric mrr_at_5Comparison conditions not established | 56.521 | [mixedbread-ai](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) Publisher reported | Evaluated revision not stated | — | | MTEB ArguAna | Configuration defaultTask RetrievalMetric ndcg_at_1Comparison conditions not established | 41.252 | [mixedbread-ai](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) Publisher reported | Evaluated revision not stated | — | | MTEB ArguAna | Configuration defaultTask RetrievalMetric ndcg_at_10Comparison conditions not established | 66.018 | [mixedbread-ai](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) Publisher reported | Evaluated revision not stated | — | | MTEB ArguAna | Configuration defaultTask RetrievalMetric ndcg_at_100Comparison conditions not established | 67.774 | [mixedbread-ai](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) Publisher reported | Evaluated revision not stated | — | | MTEB ArguAna | Configuration defaultTask RetrievalMetric ndcg_at_1000Comparison conditions not established | 67.844 | [mixedbread-ai](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) Publisher reported | Evaluated revision not stated | — | | MTEB ArguAna | Configuration defaultTask RetrievalMetric ndcg_at_3Comparison conditions not established | 57.372 | [mixedbread-ai](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) Publisher reported | Evaluated revision not stated | — | | MTEB ArguAna | Configuration defaultTask RetrievalMetric ndcg_at_5Comparison conditions not established | 62.646 | [mixedbread-ai](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) Publisher reported | Evaluated revision not stated | — | | MTEB ArguAna | Configuration defaultTask RetrievalMetric precision_at_1Comparison conditions not established | 41.252 | [mixedbread-ai](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) Publisher reported | Evaluated revision not stated | — | | MTEB ArguAna | Configuration defaultTask RetrievalMetric precision_at_10Comparison conditions not established | 9.189 | [mixedbread-ai](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) Publisher reported | Evaluated revision not stated | — | | MTEB ArguAna | Configuration defaultTask RetrievalMetric precision_at_100Comparison conditions not established | 0.991 | [mixedbread-ai](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) Publisher reported | Evaluated revision not stated | — | | MTEB ArguAna | Configuration defaultTask RetrievalMetric precision_at_1000Comparison conditions not established | 0.1 | [mixedbread-ai](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) Publisher reported | Evaluated revision not stated | — | | MTEB ArguAna | Configuration defaultTask RetrievalMetric precision_at_3Comparison conditions not established | 22.902 | [mixedbread-ai](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) Publisher reported | Evaluated revision not stated | — | | MTEB ArguAna | Configuration defaultTask RetrievalMetric precision_at_5Comparison conditions not established | 16.302 | [mixedbread-ai](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) Publisher reported | Evaluated revision not stated | — | | MTEB ArguAna | Configuration defaultTask RetrievalMetric recall_at_1Comparison conditions not established | 41.252 | [mixedbread-ai](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) Publisher reported | Evaluated revision not stated | — | | MTEB ArguAna | Configuration defaultTask RetrievalMetric recall_at_10Comparison conditions not established | 91.892 | [mixedbread-ai](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) Publisher reported | Evaluated revision not stated | — | | MTEB ArguAna | Configuration defaultTask RetrievalMetric recall_at_100Comparison conditions not established | 99.147 | [mixedbread-ai](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) Publisher reported | Evaluated revision not stated | — | | MTEB ArguAna | Configuration defaultTask RetrievalMetric recall_at_1000Comparison conditions not established | 99.644 | [mixedbread-ai](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) Publisher reported | Evaluated revision not stated | — | | MTEB ArguAna | Configuration defaultTask RetrievalMetric recall_at_3Comparison conditions not established | 68.706 | [mixedbread-ai](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) Publisher reported | Evaluated revision not stated | — | | MTEB ArguAna | Configuration defaultTask RetrievalMetric recall_at_5Comparison conditions not established | 81.508 | [mixedbread-ai](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) Publisher reported | Evaluated revision not stated | — | | MTEB ArxivClusteringP2P | Configuration defaultTask ClusteringMetric v_measureComparison conditions not established | 48.9729 | [mixedbread-ai](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) Publisher reported | Evaluated revision not stated | — | | MTEB ArxivClusteringS2S | Configuration defaultTask ClusteringMetric v_measureComparison conditions not established | 42.9807 | [mixedbread-ai](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) Publisher reported | Evaluated revision not stated | — | ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 5.4 GB | | 16-bit | 0.7 GB | | 8-bit | 0.3 GB | | 4-bit | 0.2 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Compare mxbai-embed-large-v1 - [mxbai-embed-large-v1 vs UAE-Large-V1](https://savrn.com/models/compare/mxbai-embed-large-v1-vs-uae-large-v1) - [bge-large-en-v1.5 vs mxbai-embed-large-v1](https://savrn.com/models/compare/bge-large-en-v1-5-vs-mxbai-embed-large-v1) - [multilingual-e5-large-instruct vs mxbai-embed-large-v1](https://savrn.com/models/compare/multilingual-e5-large-instruct-vs-mxbai-embed-large-v1) ## Questions About mxbai-embed-large-v1 ### How much GPU memory does mxbai-embed-large-v1 need? About 0.8 GB at 16-bit and 0.2 GB at 4-bit: the weights (335M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run mxbai-embed-large-v1 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 mxbai-embed-large-v1 commercially? Yes. mxbai-embed-large-v1 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 mxbai-embed-large-v1's context length? 512 tokens, from the maximum position embeddings in its published configuration. ## Similar Models Model · Feature extraction ### [UAE-Large-V1](https://savrn.com/models/uae-large-v1) [WhereIsAI](https://savrn.com/model-publishers/whereisai) WhereIsAI/UAE-Large-V1 is licensed under MIT. Feel free to use it in any scenario. If you use it for academic papers, you could cite us via citation info. Welcome to using AnglE to train and infer powerful sentence embeddings. Achievements - May 16, 2024 | AnglE's paper is accepted by ACL 2024 Main Conference - Dec 4, 2023 | Our universal English sentence embedding WhereIsAI/UAE-Large-V1 achieves SOTA on the MTEB Leaderboard with an average score of 64.64! - WhereIsAI/UAE-Code-Large-V1: This model can be used for code or GitHub issue similarity measurement. There is no need to specify any prompts. For retrieval purposes, please use the prompt Prompts.C for query (not for document). Infinity… Open weights mit 335M parameters 512 tokens sentence-transformers [View model](https://savrn.com/models/uae-large-v1) Model · Feature extraction ### [bge-large-en-v1.5](https://savrn.com/models/bge-large-en-v1-5) [Beijing Academy of Artificial Intelligence](https://savrn.com/model-publishers/baai) For 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 that supports all three retrieval methods, achieving new SOTA on multi-lingual (MIRACL) and cross-lingual (MKQA) benchmarks. Technical… Open weights mit 335M parameters 512 tokens sentence-transformers [View model](https://savrn.com/models/bge-large-en-v1-5) Model · Feature extraction ### [modernbert-large-pan2020-authorship-verification](https://savrn.com/models/modernbert-large-pan2020-authorship-verification) [Peter KIrby](https://savrn.com/model-publishers/peterkirby) A full fine-tune of answerdotai/ModernBERT-large for comparing whether two English fanfiction documents share an author. Training used peterkirby/pan2020dictauthorfandomdoc, a global contrastive batch of 4096 documents, and a maximum input length of 4096 tokens per document. The saved binary cosine-similarity rule achieved 98.4199% accuracy on the PAN21 test split. The original evaluation records are included in evaluationresults.json and traininghistory.parquet. The encoder has 28 Transformer layers, 1024-dimensional hidden states, and 394,781,696 encoder parameters. It uses the ModernBERT tokenizer, attention-mask-aware mean pooling over all non-padding tokens, and L2 normalization.… Open weights 395M parameters 8,192 tokens transformers [View model](https://savrn.com/models/modernbert-large-pan2020-authorship-verification) Model · Feature extraction ### [anchored-embeddinggemma-2-vi](https://savrn.com/models/anchored-embeddinggemma-2-vi) [Pham Van Ngoan](https://savrn.com/model-publishers/ngoan) Open weights apache-2.0 271M parameters transformers [View model](https://savrn.com/models/anchored-embeddinggemma-2-vi) Model · Feature extraction ### [Vela-1.0-Omni-Nano](https://savrn.com/models/vela-1-0-omni-nano) [vLLM Semantic Router](https://savrn.com/model-publishers/llm-semantic-router) Text, images, speech and environmental audio in one embedding space. Vela Omni Nano supports multimodal search, routing and clustering with normalized vectors that can be compared directly. A frozen CLAP audio branch adds environmental-sound information to the existing speech representation. Text and image computations are retained; audio embeddings are newly trained and evaluated. Architecture and measured identity. Scores are 0–100; higher is better. The comparison uses the same held-out evaluation examples, complete retrieval pools and 128-token text cap for both models. Bold marks improvement over the original small model. These known test pools are reused across releases. The common… Open weights apache-2.0 164M parameters pytorch [View model](https://savrn.com/models/vela-1-0-omni-nano) Model · Feature extraction ### [jina-embeddings-v2-base-code](https://savrn.com/models/jina-embeddings-v2-base-code) [Jina AI](https://savrn.com/model-publishers/jinaai) The easiest way to starting using jina-embeddings-v2-base-code is to use Jina AI's Embedding API. jina-embeddings-v2-base-code is an multilingual embedding model speaks English and 30 widely used programming languages. Same as other jina-embeddings-v2 series, it supports 8192 sequence length. jina-embeddings-v2-base-code is based on a Bert architecture (JinaBert) that supports the symmetric bidirectional variant of ALiBi to allow longer sequence length. The backbone jina-bert-v2-base-code is pretrained on the github-code dataset. The model is further trained on Jina AI's collection of more than 150 millions of coding question answer and docstring source code pairs. These pairs were obtained… Open weights apache-2.0 161M parameters 8,192 tokens sentence-transformers [View model](https://savrn.com/models/jina-embeddings-v2-base-code) ## Mixedbread [All models and datasets](https://savrn.com/model-publishers/mixedbread-ai) ## Versions - [b33106f585b9](https://savrn.com/models/mxbai-embed-large-v1/versions/b33106f585b9) · current 2026-10-25 ## 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. - [Hugging Face record](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) - [How the hub is built](https://savrn.com/model-hub/methodology)