Sold by: Waltsoft Inc.
Deployed on AWS
Mixedbread mxbai-embed-large-v1 on SageMaker. Scores 64.68 on MTEB -- beating OpenAI Ada-002 (60.99) and BGE Large (64.23). 1024-dimensional CLS embeddings, no per-token charges.
Overview
mxbai-embed-large-v1 from Mixedbread AI scores 64.68 average on the MTEB benchmark across 56 tasks, placing it above OpenAI text-embedding-ada-002 (60.99) and BAAI BGE Large EN v1.5 (64.23). It produces 1024-dimensional L2-normalized vectors from inputs up to 512 tokens.
The model supports Matryoshka Representation Learning, meaning vectors can be truncated to 512 or 256 dimensions without retraining -- useful for cutting vector database storage costs on large corpora while retaining most retrieval quality.
Deploy it as a SageMaker endpoint in your own AWS account. Your documents stay inside your VPC -- no external API calls, no third-party access, no rate limits. You control the endpoint, the scaling policy, and the CloudWatch logs.
Integrates with LangChain, LlamaIndex, and Haystack. Compatible with pgvector on Aurora/RDS, Amazon OpenSearch, Pinecone, Weaviate, Qdrant, Chroma, and Milvus.
Primary use cases: highest-precision document retrieval for RAG pipelines, semantic search over internal knowledge bases, product catalog and patent similarity, and any workload where retrieval accuracy is the primary constraint.
Highlights
- MTEB score 64.68 -- beats OpenAI Ada-002 (60.99) and BGE Large (64.23), running entirely inside your AWS VPC
- Matryoshka support: truncate to 512 or 256 dimensions to cut vector DB storage without losing model quality
- Flat $0.10/hr on ml.m5.xlarge -- no per-token charges, no rate limits, 1024-dimensional CLS output
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Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.xlarge instance type, real-time mode | $0.10 |
ml.m5.xlarge Inference (Batch) Recommended | Model inference on the ml.m5.xlarge instance type, batch mode | $0.10 |
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Initial release
Additional details
Inputs
- Summary
Mixedbread mxbai-embed-large-v1 on SageMaker. Scores 64.68 on MTEB -- beating OpenAI Ada-002 (60.99) and BGE Large (64.23). 1024-dimensional CLS embeddings, no per-token charges.
- Input MIME type
- application/json
https://waltsoft-marketplace-assets.s3.amazonaws.com/ml-validation/text-sample/sample.jsonl
https://waltsoft-marketplace-assets.s3.amazonaws.com/ml-validation/text-sample/sample.jsonl
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Contact support@waltsoft.net for deployment assistance.
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