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Most Popular LLM Embedding Models with High API Limits in 2024

In 2024, several Large Language Model (LLM) embedding models have emerged as leaders, offering high performance, robust capabilities, and scalable API limits. These models are crucial for various applications, including semantic search, recommendation systems, text clustering, and knowledge retrieval. This overview details the most popular LLM embedding models, focusing on their capabilities, performance, use cases, and API limits.

1. OpenAI's Embedding Models

Capabilities: OpenAI provides state-of-the-art embedding models, including text-embedding-3-large and text-embedding-ada-002, designed for high-dimensional semantic representations. These models excel in tasks requiring nuanced understanding of context, such as text similarity, clustering, and document retrieval. They are also capable of handling both text and code embeddings.
Performance: These models demonstrate high precision and recall for semantic search tasks. Benchmark scores indicate superior performance on tasks requiring a deep understanding of context. The text-embedding-3-large model, while resource-intensive, offers industry-leading accuracy.
Use Cases:
- Semantic search in large document repositories.
- Personalized recommendation systems.
- Knowledge graph construction.
- Text clustering and topic modeling.
- Content moderation.
API Limits: OpenAI offers scalable API limits for enterprise-level usage, with tiered pricing for higher volume. Specific API limits depend on the subscription plan, but they generally provide generous rate limits and high token limits for input text (up to 8192 tokens for some models). They operate on a pay-as-you-go pricing model, without strict limits.
Metrics:
- text-embedding-ada-002: 1536 dimensions
- text-embedding-3-large: Dimensions vary, but are high-dimensional
- Cost: $0.0004 per 1,000 tokens for text-embedding-ada-002 (as of 2024).
Source: OpenAI Documentation

2. Cohere's Embedding Models

Capabilities: Cohere offers a range of embedding models, including embed-english-v3.0, embed-multilingual-v3.0, and Cohere V3 Light. These models are optimized for efficiency, offering embeddings with smaller dimensions while maintaining high precision. They are particularly noted for their excellent multilingual support, handling over 100 languages.
Performance: Cohere's models demonstrate competitive performance with minimal storage requirements. They offer faster processing times compared to larger models. The multilingual models excel in cross-lingual tasks and maintain high accuracy across different languages and domains.
Use Cases:
- Real-time search and retrieval applications.
- Lightweight recommendation engines.
- Low-latency applications in resource-constrained environments.
- Cross-lingual applications.
- Technical documentation analysis.
- Academic research categorization.
- Industry-specific content organization.
API Limits: Cohere provides flexible API limits with options for high-volume usage. It is particularly cost-effective for large-scale deployments. They offer flexible pricing with enterprise options, allowing for scalable usage. Input text limits are around 2048 tokens.
Metrics:
- Embedding dimensionality: 768–1024 (depending on the model).
- Cost: Varies by model size and usage tier.
Source: Cohere API, Cohere Embed API

3. NVIDIA NV-Embed

Capabilities: NVIDIA's NV-Embed model is a high-performance embedding model that leads the Massive Text Embedding Benchmark (MTEB) leaderboard. It is fine-tuned for specialized embedding tasks and offers cutting-edge performance for high-dimensional embeddings.
Performance: This model demonstrates industry-leading benchmark results and is optimized for tasks requiring deep semantic understanding and high contextual accuracy.
Use Cases:
- Enterprise-grade semantic search.
- AI-driven content recommendation systems.
- Advanced natural language understanding tasks.
API Limits: NVIDIA provides enterprise-grade API access with high limits, suitable for large-scale applications. Specific details depend on licensing agreements.
Metrics:
- Benchmark score of 69.32 on MTEB across 56 embedding tasks.
Source: RAG About It Blog on Top AI Embedding Models

4. Mistral AI Embedding Models

Capabilities: Mistral AI offers embedding models focused on lightweight, high-performance embeddings. These models are designed for low-latency applications and edge deployments, supporting multilingual embeddings. The Mistral 7B model, fine-tuned for embedding tasks, balances model size and performance.
Performance: Mistral's models are competitive in terms of speed and memory efficiency, making them suitable for real-time applications. They demonstrate high performance on embedding benchmarks like MTEB.
Use Cases:
- Real-time recommendation systems.
- Semantic search for mobile and edge devices.
- Content personalization.
- Custom embedding tasks for domain-specific applications.
- Semantic clustering and categorization.
API Limits: Open-source deployment allows for customizable API limits, depending on the hosting infrastructure. API rate limits depend on the subscription plan. Input token limits are between 1024 and 2048 tokens.
Metrics:
- Embedding dimensionality: 512–1024.
- Cost: Affordable pricing for small and medium-sized businesses.
Source: Mistral AI, RAG About It Blog on Top AI Embedding Models

5. Hugging Face Sentence Transformers (e.g., all-MiniLM-L6-v2)

Capabilities: Hugging Face offers a wide range of open-source embedding models through its Sentence Transformers library. Models like all-MiniLM-L6-v2 are optimized for semantic similarity and clustering. These models are pretrained on large datasets for robust performance and are available for deployment via the Hugging Face API or self-hosting.
Performance: Lightweight models like all-MiniLM-L6-v2 achieve fast inference speeds with competitive accuracy. They support fine-tuning for domain-specific tasks.
Use Cases:
- Document retrieval and FAQ systems.
- Contextual text embeddings for chatbots.
- Knowledge graph construction.
- Semantic search in chatbots and virtual assistants.
- Text deduplication and clustering.
API Limits: API token limits depend on the Hugging Face Inference API plan. The free tier offers limited API calls, while paid tiers provide higher limits. Self-hosted models have no inherent API limits.
Metrics:
- all-MiniLM-L6-v2: 384 dimensions
- Cost: Free for open-source use; API pricing varies.
Source: Hugging Face Models, Helicone Blog on LLM API Providers, RAG About It Blog on Top AI Embedding Models

6. Google Vertex AI Embedding Models

Capabilities: Google Vertex AI offers advanced embeddings through its platform, supporting integration with other Google Cloud services. These models are designed for large-scale applications with high throughput.
Performance: Google's models demonstrate excellent performance on semantic search and recommendation tasks. They are optimized for scalability and enterprise-grade reliability.
Use Cases:
- Enterprise knowledge management.
- Large-scale recommendation engines.
- AI-powered customer support systems.
API Limits: Token limits and rate limits are highly scalable, depending on the Google Cloud plan. Enterprise-grade SLAs are available for high availability.
Metrics:
- Embedding dimensionality: Customizable based on the model.
- Cost: Based on usage and Google Cloud pricing tiers.
Source: Google Vertex AI

7. Sentence-BERT (SBERT)

Capabilities: SBERT is an open-source embedding model based on the BERT architecture, fine-tuned for sentence-level semantic similarity tasks. It supports multilingual embeddings.
Performance: SBERT offers high accuracy for semantic similarity and clustering tasks, with moderate inference speed compared to lighter models.
Use Cases:
- Question-answering systems.
- Semantic text matching.
- Text deduplication.
API Limits: No API limits for self-hosted models. The Hugging Face Inference API offers scalable limits based on the plan.
Metrics:
- Embedding dimensionality: 768.
- Cost: Free for self-hosting; API pricing varies.
Source: SBERT Documentation

8. Meta AI's Llama 2 Embedding Models

Capabilities: Meta AI's Llama 2 models offer open-source embeddings optimized for semantic understanding and multilingual tasks. They are available in various sizes (7B, 13B, 70B) to balance performance and resource usage.
Performance: These models demonstrate high accuracy in semantic search and clustering tasks and are scalable for large datasets and enterprise use cases.
Use Cases:
- Knowledge management systems.
- Semantic search for large-scale databases.
- Personalized content recommendations.
API Limits: Input token limits are up to 4096 tokens. API rate limits depend on the hosting provider (e.g., Hugging Face, AWS).
Metrics:
- Embedding dimensionality: Varies by model size.
- Cost: Free for open-source use; hosting costs depend on the provider.
Source: Meta AI Llama 2

9. Other Notable Models

Alibaba-NLP/gte-Qwen2 Models: These models, including gte-Qwen2-7B-instruct and gte-Qwen2-1.5B-instruct, offer high performance for complex embedding tasks, with the 7B model being particularly powerful. API limits depend on the specific deployment and API plans.
Salesforce/SFR-Embedding-2_R: This model enhances text retrieval and semantic search capabilities, optimized for retrieving relevant text documents. API limits depend on Salesforce API plans.
intfloat/e5-large-v2: Designed for efficient embedding generation, this model is suitable for various NLP tasks, offering a good balance between performance and resource efficiency. API limits depend on the specific deployment and API plans.
jinaai/jina-embeddings-v2 Models: These models, including jina-embeddings-v2-base-en and jina-embeddings-v2-base-code, are lightweight and efficient, suitable for applications with resource constraints. API limits depend on the specific deployment and API plans.
Fireworks AI Embeddings API: This API offers high-performance embeddings with low latency, suitable for applications requiring fast and efficient embeddings. They offer scalable API access with a focus on performance and cost-efficiency.
BGE Embeddings: Models like bge-large-en and bge-base-en are open-source, offering strong MTEB benchmark results and cost-effective production deployments.

Comparison of API Limits and Metrics

Model	Token Limit	Embedding Dimensionality	Cost (per 1,000 tokens)	Use Case Focus
`text-embedding-ada-002`	8192	1536	$0.0004	General-purpose embeddings
Cohere Embeddings	2048	768–1024	Varies	Multilingual applications
NVIDIA NV-Embed	N/A	High-dimensional	Varies	Enterprise-grade semantic tasks
Mistral 7B	1024-2048	512-1024	Affordable	Real-time and edge deployments
`all-MiniLM-L6-v2`	Varies	384	Varies	Semantic search and clustering
SBERT	N/A (self-hosted)	768	Free (self-hosted)	Semantic similarity
Llama 2	4096	Varies	Free (open-source)	Knowledge management
Google Vertex AI	Customizable	Customizable	Based on usage	Enterprise-grade applications
BGE Embeddings	N/A (self-hosted)	Varies	Free (self-hosted)	Cost-effective production deployments

Key Takeaways

OpenAI's Text-Embedding-3-Large and NVIDIA NV-Embed offer the highest accuracy and are suitable for enterprise-grade applications, but they require significant resources.
Cohere V3 Light and all-MiniLM-L6-v2 are ideal for lightweight, cost-effective deployments.
Open-source models like SBERT, Mistral 7B, and Llama 2 offer flexibility and customization for specific use cases.
API limits vary significantly based on pricing tier, and self-hosted options provide unlimited usage but require infrastructure.
Performance varies by specific task, and the cost vs. performance tradeoff should be evaluated for specific use cases.

For the most up-to-date performance comparisons, check the MTEB leaderboard at: https://huggingface.co/spaces/mteb/leaderboard

Note: API limits and pricing may change over time, so it's recommended to check the official documentation for the most current information.