- Text Embeddings
E5-Base-v2 is an English sentence and paragraph embedding model from Intfloat that encodes text into a 768-dimensional dense vector space, optimized for high-quality semantic similarity tasks.
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E5-Large-v2
E5-Large-v2 by Intfloat is a 335M-parameter English text-embedding transformer that maps text into 1024-dimensional vectors for high-accuracy semantic search and similarity tasks.
Providers Comparison
Speed
Output tokens per second · Higher is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
End-to-End Response Time
Seconds · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
Price
USD per 1M tokens (blended) · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
About the model
What is E5-Large-v2?
E5-Large-v2 is a large English text embedding model trained with weakly supervised contrastive pre-training to produce 1024-dimensional sentence and document embeddings. It is mainly used for semantic search and retrieval, where queries and passages are embedded and compared to find relevant results. It is also widely applied to tasks like clustering, reranking, and classification that rely on dense semantic representations. E5-Large-v2 belongs to the E5 family of text embedding models, improving on earlier variants such as e5-base-v2 and e5-small-v2.
Model capabilities
5 Core Capabilities
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Text Embeddings
Generates 1024-dimensional dense vector embeddings for English text, suitable for downstream machine learning and representation learning applications.
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Semantic Search
Supports high-quality semantic search by encoding queries and documents for vector similarity retrieval across large text corpora.
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Sentence Similarity
Computes meaningful similarity between sentences or passages by comparing their embeddings, enabling clustering and paraphrase detection.
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Information Retrieval
Optimized for passage retrieval tasks, including ad-hoc document ranking and open-domain question answering pipelines using dense vectors.
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Benchmark Evaluation
Provides strong performance on benchmarks like BEIR and MTEB for diverse retrieval, classification, and semantic similarity tasks.
Use cases
6 Most Valuable Use Cases
- Semantic Text Search
- Question Answer Retrieval
- Duplicate Issue Detection
- Product Recommendation Ranking
- Document Clustering
- Multilingual Text Embedding
Transparent pricing
Cost Comparison
LLM API offers the lowest embedding prices and best performance for E5-Large-v2–class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | ~120ms | ~8K tps | 99.99% | $0.02 per 1M tokens | $0.02 per 1M tokens | 64K tokens |
| Intfloat | Global | ~220ms | ~3K tps | 99.9% | ~$0.10 per 1M tokens | ~$0.10 per 1M tokens | 32K tokens |
| OpenAI (text-embedding-3-large) | Global | ~250ms | ~4K tps | 99.9% | $0.13 per 1M tokens | $0.13 per 1M tokens | 100K tokens |
| Cohere (embed-multilingual-light-v3) | Global | ~260ms | ~2.5K tps | 99.9% | ~$0.20 per 1M tokens | ~$0.20 per 1M tokens | 4K tokens |
| Azure OpenAI (embedding equivalent) | US East | ~240ms | ~3.5K tps | 99.9% | ~$0.16 per 1M tokens | ~$0.16 per 1M tokens | 16K tokens |
Performance benchmarks
Technical Specifications
| Metric | E5-Large-v2 (Intfloat) | text-embedding-3-large (OpenAI) | bge-large-en-v1.5 (BAAI) |
|---|---|---|---|
| Dimensions | 1024 | 3072 | 1024 |
| Max Input Tokens | ~4K | 8K | ~4K |
| Price per 1M Tokens | $0.10 | $0.13 | $0.05 |
| Throughput | ~2K tps | ~4K tps | ~2.5K tps |
| Avg Latency | ~120ms | ~100ms | ~130ms |
| Uptime | 99.5% | 99.9% | 99.0% |
30-day usage via LLM API
- 620M
- Embedding tokens processed (30 days)
- 3.1M
- API requests served (30 days)
- 41K
- Unique developer accounts (30 days)
- 99.95%
- Avg API uptime (30 days)
Architecture & Integration
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
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Unified AI Routing
Automatically route each request to the optimal model across providers based on latency, cost, and performance—without changing your integration or redeploying.
One endpoint, every model -
Smarter Cost Control
Mix premium and budget models with dynamic routing, hard spend guards, and usage insights so you can scale AI without unpredictable cloud bills.
Optimize quality per dollar -
Resilient Fallback Logic
Define provider-agnostic failover rules so if a model or region degrades, traffic is transparently retried on backups—no downtime, no manual switches.
Stay online, automatically -
Full-Stack Observability
Get end-to-end traces, latency and error metrics, cost breakdowns, and structured logs for every call so you can debug and tune AI traffic in minutes.
See every token and trace -
Task-Native Orchestration
Describe tasks at a higher level—chat, tools, evals, workflows—and let LLM.API select models, parameters, and prompts consistently across providers.
Tasks, not model glue -
High-Throughput Batch
Submit large batches of prompts to run asynchronously across multiple models and regions with built-in retries, partial failure handling, and cost reporting.
Millions of calls, one job
Decision guide
When to Use — When NOT to Use
Use it if...
- You need strong general-purpose text embeddings for semantic search across diverse domains.
- You need multilingual sentence embeddings covering many languages with a single model.
- Your use case involves dense retrieval or reranking for question-answering over documents.
- Your use case involves clustering or deduplicating large text corpora via vector similarity.
- You need an open-source embedding model compatible with common vector databases and libraries.
- Your use case involves building recommendation systems based on semantic similarity between texts.
Avoid if...
- You need an autoregressive language model for text generation, editing, or conversation.
- Your workload requires processing images, audio, or multimodal inputs beyond plain text.
- You need token-level tasks like sequence tagging, NER, or structured information extraction.
- Your workload requires extremely long-context understanding far beyond typical sentence or paragraph length.
- You need state-of-the-art performance on domain-specific tasks better served by specialized models.
- Your workload requires on-device inference with very tight memory or latency constraints.
FAQ
Frequently Asked Questions
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What is E5-Large-v2?
E5-Large-v2 is a text embedding model by Intfloat optimized for high-quality semantic search, retrieval, and clustering tasks.
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What is E5-Large-v2 best suited for?
E5-Large-v2 is best for generating dense vector representations for semantic search, question answering, duplicate detection, and recommendation systems.
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What is the context window or maximum input length for E5-Large-v2?
E5-Large-v2 typically supports input sequences up to around 512 tokens, after which text is truncated before embedding.
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What modalities does E5-Large-v2 support?
E5-Large-v2 is a text-only model that accepts natural language or short text strings and returns numeric embedding vectors.
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How is E5-Large-v2 priced when accessed through LLM.API?
LLM.API exposes E5-Large-v2 with token-based pricing, where you pay per input token embedded; check the LLM.API pricing page for exact rates.
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What latency should I expect when using E5-Large-v2 via LLM.API?
For typical short texts, E5-Large-v2 usually responds in tens to a few hundreds of milliseconds, depending on load and batch size.
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How do I access E5-Large-v2 through the LLM.API gateway?
You call the LLM.API embeddings endpoint, specifying the E5-Large-v2 model name and passing your input texts in the request body.
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How does E5-Large-v2 compare to similar embedding models?
E5-Large-v2 generally offers strong retrieval performance versus smaller E5 variants, with higher accuracy but more compute and latency.
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What are the main limitations of E5-Large-v2?
E5-Large-v2 cannot generate text, handle images or audio, and its performance may degrade on very long, noisy, or domain-specific inputs.
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Can I use E5-Large-v2 for multilingual tasks through LLM.API?
E5-Large-v2 primarily targets English, and performance on other languages may be weaker compared with dedicated multilingual embedding models.
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