- Text Generation
Qwen3 ASR Flash is Qwen’s high-accuracy, multilingual automatic speech recognition (ASR) service optimized for real-time transcription of short audio. It is built on the Qwen3-Omni foundation…
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Multilingual-E5-Large
- Text Generation
Multilingual-E5-Large by Intfloat is a large multilingual text-embedding model that maps text from 90+ languages into a shared dense vector space for semantic similarity and retrieval 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 Multilingual-E5-Large?
Multilingual-E5-Large is a sentence- and document-level text embedding model that encodes multilingual inputs into 1024-dimensional vectors optimized for semantic similarity and retrieval across over 90 languages. It is mainly used for applications such as semantic search, multilingual similarity search, and cross-lingual information retrieval in RAG and search systems. It is also applied to clustering, classification, and other downstream NLP tasks that rely on high-quality multilingual embeddings. The model is part of the E5 family of embedding models from Intfloat, which includes small and base multilingual variants as well as English-only E5 models.
Model capabilities
5 Core Capabilities
-
Multilingual Embeddings
Generates dense text embeddings for over 90 languages, enabling unified semantic representations across diverse multilingual content and applications.
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Semantic Similarity
Encodes sentences and documents so similar meanings are close in vector space, supporting clustering, deduplication, and semantic grouping tasks.
-
Semantic Search
Optimized for retrieval tasks where user queries and passages are embedded and compared to power high-quality search and RAG pipelines.
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Cross-Lingual Retrieval
Supports searching documents in one language using queries in another by mapping all texts into a shared multilingual embedding space.
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General Feature Extraction
Provides versatile text feature vectors usable in downstream models for tasks like classification, ranking, recommendation, and anomaly detection.
Use cases
6 Most Valuable Use Cases
- Multilingual Semantic Search
- Cross-Lingual Retrieval
- Text Similarity Matching
- Multilingual RAG Retrieval
- Recommendation Vector Search
- Multi-Language Topic Clustering
Transparent pricing
Cost Comparison
LLM API offers the lowest embedding prices and best performance for Multilingual-E5-Large–class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | ~120ms | ~8k tps | 99.99% | ~$0.03 per 1M tokens | $0.00 | ~64K tokens |
| Intfloat (Direct / HF Inference) | Global | ~220ms | ~3k tps | ~99.5% | ~$0.08 per 1M tokens | $0.00 | ~32K tokens |
| OpenAI (text-embedding-3-large) | Global | ~180ms | ~5k tps | ~99.9% | ~$0.13 per 1M tokens | $0.00 | 8K tokens |
| Azure OpenAI (Embeddings) | US East | ~200ms | ~4k tps | 99.9% | ~$0.15 per 1M tokens | $0.00 | 8K tokens |
| Together AI (Similar Embedding Model) | US West | ~210ms | ~3.5k tps | ~99.5% | ~$0.10 per 1M tokens | $0.00 | ~32K tokens |
Performance benchmarks
Technical Specifications
| Metric | Multilingual-E5-Large | text-embedding-3-large (OpenAI) | gte-large (Alibaba-NLP) |
|---|---|---|---|
| Dimensions | 1024 | 3072 | 1024 |
| Max Input Tokens | 4K | 8K | 2K |
| Price per 1M Tokens | $0.15 | $0.13 | $0.05 |
| Avg Latency | ~120ms | ~140ms | ~110ms |
| Throughput | 900 tps | 850 tps | 950 tps |
| Languages Supported | 100+ | 90+ | 80+ |
| Uptime | 99.5% | 99.9% | 99.0% |
30-day usage via LLM API
- 3.8B
- Prompt tokens processed (30 days)
- 9.4M
- API requests served (30 days)
- 1.1M
- Unique applications & services (30 days)
- 99.8%
- 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.
-
Unified AI Routing
Automatically route each request to the optimal model across providers based on latency, cost, and quality—without changing your integration or redeploying code.
One endpoint, every model -
Cost-Aware Orchestration
Control spend with per-route budgets, model-level pricing rules, and smart downgrades that keep quality high while preventing surprise bills in production.
Optimize quality per dollar -
Resilient Fallback Flows
Define provider-agnostic failover chains so requests transparently retry on backup models when providers throttle, fail, or degrade—no custom error handling needed.
Stay online, even upstream -
End-to-End Observability
Get full visibility into every call with traces, metrics, logs, and payload sampling to debug latency, errors, and cost across all providers in one place.
See every token, everywhere -
Task-Level Abstractions
Describe tasks—chat, RAG, tools, classification—once and let LLM.API choose and tune the right models and parameters per use case and environment.
Code to tasks, not models -
High-Throughput Batch
Ship large jobs efficiently with batched and asynchronous execution, automatic chunking, and concurrency controls that maximize throughput while respecting provider limits.
Scale jobs, not headaches
Decision guide
When to Use — When NOT to Use
Use it if...
- You need high-quality semantic text embedding for multilingual search or retrieval applications.
- Your use case involves building cross-lingual semantic search across many languages and scripts.
- You need sentence-level embeddings for clustering, deduplication, and topic discovery over large corpora.
- Your use case involves encoding queries and documents for dense retrieval or RAG pipelines.
- You need a well-known, open-source embedding model easily deployable on your own infrastructure.
- Your use case involves aligning multilingual user queries with English-centric knowledge bases.
- You need to replace heuristic keyword search with semantic retrieval in many locales.
Avoid if...
- You need a generative model that writes or edits text rather than produces embeddings.
- Your workload requires handling very long documents far beyond the model’s input length.
- You need domain-specialized embeddings, such as for code understanding or biological sequences.
- Your workload requires state-of-the-art performance on English-only benchmarks over all alternatives.
- You need fine-grained token-level representations instead of pooled sentence or passage embeddings.
- Your workload requires strict enterprise support, SLAs, and managed hosting from the model provider.
- You need guaranteed backward-compatible embedding behavior for long-term index stability and drift control.
FAQ
Frequently Asked Questions
-
What is Multilingual-E5-Large?
Multilingual-E5-Large is an Intfloat text-embedding model optimized for multilingual semantic search, clustering, and retrieval across many languages.
-
What modalities does Multilingual-E5-Large support?
Multilingual-E5-Large is a purely text-based model that converts text inputs into dense vector embeddings.
-
How do I access Multilingual-E5-Large through LLM.API?
You call the LLM.API embeddings endpoint, specifying provider 'Intfloat' and model 'Multilingual-E5-Large' in your request parameters.
-
What is Multilingual-E5-Large best suited for?
It is best for multilingual semantic search, dense retrieval, reranking pipelines, and deduplication where cross-language similarity detection is important.
-
How is Multilingual-E5-Large priced on LLM.API?
Pricing is usage-based per embedding token on LLM.API, and you should check the LLM.API pricing page for current Multilingual-E5-Large rates.
-
What is the context window for Multilingual-E5-Large embeddings?
Multilingual-E5-Large supports reasonably long text inputs, but you should chunk very long documents before embedding for best performance and latency.
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How fast is Multilingual-E5-Large when called via LLM.API?
Latency is typically low and dominated by text length and batch size, making it suitable for real-time or near real-time search applications.
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How does Multilingual-E5-Large compare to English-only embedding models?
It generally offers better performance on non-English and cross-lingual tasks, while strong English-only models may outperform it on purely English benchmarks.
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Does Multilingual-E5-Large support batch embedding requests on LLM.API?
Yes, you can send multiple texts in one embeddings request to Multilingual-E5-Large to reduce overhead and improve throughput.
-
What are key limitations of Multilingual-E5-Large?
It cannot generate text, may underperform on languages outside its training distribution, and can encode training-time biases into the resulting embeddings.
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