- Text Embeddings
Embed V1 4B is Perplexity’s 4-billion-parameter text embedding model optimized for high-quality, web‑scale dense retrieval, supporting long 32K-token inputs and efficient INT8/binary representations.
Powered by Sentence Transformers
paraphrase-MiniLM-L6-v2
paraphrase-MiniLM-L6-v2 is a compact sentence-transformers embedding model from Sentence Transformers that maps text into 384-dimensional vectors. It is notable for offering a strong balance of quality and efficiency for semantic 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 paraphrase-MiniLM-L6-v2?
paraphrase-MiniLM-L6-v2 is a sentence-transformers model that encodes sentences and short paragraphs into 384-dimensional dense vector embeddings. It is mainly used for semantic search and information retrieval, where it helps find relevant texts based on meaning rather than keywords. It is also widely applied to clustering and paraphrase or similarity detection across large text collections. The model belongs to the MiniLM-based sentence-transformers family, related to models such as all-MiniLM-L6-v2 and paraphrase-multilingual-MiniLM-L12-v2.
Model capabilities
5 Core Capabilities
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Sentence Embeddings
Maps sentences and short paragraphs into 384-dimensional dense vector embeddings that capture semantic meaning for downstream applications.
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Semantic Similarity
Computes similarity between sentence embeddings, enabling comparison of meaning for paraphrase detection and related text identification tasks.
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Semantic Search
Supports semantic search by embedding queries and documents into the same vector space for relevance-based retrieval using similarity scores.
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Text Clustering
Enables clustering of texts by encoding them as vectors, allowing grouping of semantically related sentences or documents together.
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Efficient Deployment
Compact transformer model with about 22.7M parameters, suitable for resource-constrained environments and real-time text embedding workloads.
Use cases
6 Most Valuable Use Cases
- Semantic Text Search
- Duplicate Question Detection
- Customer Ticket Clustering
- E-commerce Product Matching
- Paraphrase Mining Pipeline
- FAQ Answer Retrieval
Transparent pricing
Cost Comparison
LLM API embeddings are priced lower and scale better than comparable MiniLM-based services.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | ~120ms | ~8,000 tps | 99.99% | ~$0.02 per 1M tokens | ~$0.02 per 1M tokens | ~8K tokens |
| Sentence Transformers (Hosted) | Global | ~250ms | ~2,000 tps | ~99.9% | ~$0.10 per 1M tokens | ~$0.80 per 1M tokens | ~4K tokens |
| Hugging Face Inference Endpoints | US East | ~220ms | ~1.5k tps | 99.9% | ~$1.20 per 1M tokens | ~$1.20 per 1M tokens | ~8K tokens |
| AWS SageMaker (MiniLM-based endpoint) | US West | ~250ms | ~1k tps | 99.9% | ~$1.50 per 1M tokens | ~$1.50 per 1M tokens | ~8K tokens |
| Azure ML Online Endpoint (MiniLM-based) | EU West | ~260ms | ~900 tps | 99.9% | ~$1.60 per 1M tokens | ~$1.60 per 1M tokens | ~8K tokens |
Performance benchmarks
Technical Specifications
| Metric | paraphrase-MiniLM-L6-v2 (Sentence Transformers) | all-MiniLM-L6-v2 (Sentence Transformers) | text-embedding-3-small (OpenAI) |
|---|---|---|---|
| Dimensions | 384 | 384 | 1536 |
| Max Input Tokens | ~256 tokens | ~256 tokens | 8K tokens |
| Price per 1M Tokens | ~$0.05 | ~$0.05 | $0.02 |
| Throughput | ~1,500 tps | ~1,500 tps | ~2,500 tps |
| Avg Latency | ~40ms | ~40ms | ~80ms |
| Uptime | ~99.5% | ~99.5% | ~99.9% |
30-day usage via LLM API
- 1.8B
- Prompt tokens processed (30 days)
- 9.4M
- API requests served (30 days)
- 420K
- 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 best-fit model across providers based on cost, latency, and quality—without changing your integration.
One endpoint, every model -
Cost-Aware Orchestration
Optimize spend by dynamically mixing premium and budget models, enforcing price caps, and simulating cost impact before changes hit production.
More output, less spend -
Resilient Fallback Engine
Stay online during model or provider outages with automatic retries, failover routes, and graceful degradation tuned to your app’s SLAs.
Never go dark -
End-to-End Observability
Trace every request across providers with logs, metrics, and evaluations so you can debug prompts, track regressions, and confidently ship changes.
See every token -
Task-Level Abstractions
Call high-level tasks like chat, tools, embeddings, and rerankers through a consistent API so you can swap models without rewriting logic.
Tasks, not providers -
High-Throughput Batch API
Process large workloads efficiently with batched inference, parallel execution, and provider-aware rate limits for faster, cheaper bulk operations.
Scale jobs, not code
Decision guide
When to Use — When NOT to Use
Use it if...
- You need lightweight sentence embeddings for semantic similarity with tight memory constraints.
- You need fast paraphrase detection or duplicate question identification at scale.
- Your use case involves clustering short texts or sentences into semantic groups.
- Your use case involves building a simple semantic search over short documents.
- You need a compact model for on-device or edge semantic text applications.
- Your use case involves generating embeddings as features for downstream ML classifiers.
Avoid if...
- You need state-of-the-art accuracy on complex semantic similarity or entailment benchmarks.
- Your workload requires understanding long documents far beyond a few sentences.
- You need multilingual support beyond the primarily English capabilities of this model.
- Your workload requires domain-specific embeddings tuned for legal, medical, or scientific texts.
- You need generative capabilities like summarization, translation, or question answering directly.
- Your workload requires robust performance on noisy, code-mixed, or highly informal text.
FAQ
Frequently Asked Questions
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What is paraphrase-MiniLM-L6-v2?
paraphrase-MiniLM-L6-v2 is a Sentence Transformers model that encodes sentences into dense vectors optimized for semantic similarity and paraphrase detection.
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What is paraphrase-MiniLM-L6-v2 best suited for?
It is best for semantic search, clustering, duplicate detection, and measuring sentence-level similarity in low-latency, resource-constrained applications.
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How much does it cost to use paraphrase-MiniLM-L6-v2 via LLM.API?
LLM.API pricing is usage-based; check the paraphrase-MiniLM-L6-v2 entry in the LLM.API pricing page for the latest per-request and per-token rates.
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What is the context window of paraphrase-MiniLM-L6-v2?
paraphrase-MiniLM-L6-v2 is typically used with short texts or sentences, and does not support long-document context windows like large generative LLMs.
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How fast is paraphrase-MiniLM-L6-v2 on LLM.API?
As a small MiniLM-based encoder, it provides low-latency embeddings, making it suitable for real-time or interactive use cases on LLM.API.
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What modalities does paraphrase-MiniLM-L6-v2 support?
paraphrase-MiniLM-L6-v2 is a text-only model that produces fixed-size vector embeddings from input text.
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How do I access paraphrase-MiniLM-L6-v2 through LLM.API?
Call the LLM.API embeddings endpoint with the model name paraphrase-MiniLM-L6-v2 and your text inputs, using your LLM.API authentication key.
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How does paraphrase-MiniLM-L6-v2 compare to larger Sentence Transformers models?
It trades some embedding quality for significantly lower latency and memory usage, making it ideal when performance and cost are priorities.
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Does paraphrase-MiniLM-L6-v2 support multilingual text?
paraphrase-MiniLM-L6-v2 is primarily optimized for English; performance on other languages may be inconsistent and should be empirically validated.
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What are the main limitations of paraphrase-MiniLM-L6-v2?
It may underperform on complex reasoning, domain-specific jargon, or long documents compared to larger, more specialized embedding or generative models.
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