Powered by Sentence Transformers
all-mpnet-base-v2 is a widely used English sentence-embedding model from Sentence Transformers that maps text to 768-dimensional vectors for semantic similarity tasks. It is built on Microsoft’s MPNet architecture and fine-tuned on over a billion sentence pairs for strong general-purpose performance.
Output tokens per second · Higher is better
Seconds · Lower is better
USD per 1M tokens (blended) · Lower is better
About the model
all-mpnet-base-v2 is an English sentence-transformer model that encodes sentences and short paragraphs into 768-dimensional dense vector embeddings. It is mainly used for semantic search and retrieval in applications like RAG pipelines, documentation search, and information retrieval systems. It is also commonly applied to clustering, deduplication, and semantic similarity scoring across large text collections. The model is part of the Sentence Transformers family and is fine-tuned from the microsoft/mpnet-base architecture using large-scale contrastive training data.
Model capabilities
Generates dense vector embeddings for sentences and short texts, capturing semantic meaning for downstream similarity and retrieval tasks.
Enables semantic search by embedding queries and documents into a shared space, supporting meaning-based retrieval beyond exact keyword matching.
Supports clustering of documents or sentences by embedding them into vectors, enabling grouping of semantically similar texts at scale.
Provides embeddings usable as features for training classifiers, improving performance on various downstream text classification tasks.
Identifies near-duplicate or paraphrased sentences by comparing embedding similarity, useful for deduplication and plagiarism-like detection scenarios.
Use cases
Transparent pricing
LLM API embeddings are up to ~60% cheaper than comparable all-mpnet-base-v2 offerings.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 80ms | 120k tokens/s | 99.99% | $0.02 | $0.00 | 8192 tokens |
| Sentence Transformers (Hosted) | Global | ~220ms | ~300 tps | ~99.5% | ~$0.05 | $0.00 | ~4096 tokens |
| Hugging Face Inference API | EU West | ~250ms | ~250 tps | 99.9% | ~$0.06 | $0.00 | ~4096 tokens |
| Azure AI (MPNet-like Embeddings) | US East | ~200ms | ~400 tps | 99.9% | ~$0.04 | $0.00 | 4096 tokens |
| Replicate | US West | ~260ms | ~200 tps | ~99.0% | ~$0.07 | $0.00 | ~4096 tokens |
Performance benchmarks
| Metric | all-mpnet-base-v2 (Sentence Transformers) | bert-base-nli-mean-tokens (Sentence Transformers) | paraphrase-MiniLM-L6-v2 (Sentence Transformers) |
|---|---|---|---|
| Dimensions | 768 | 768 | 384 |
| Max Input Tokens | ~256 tokens | ~128 tokens | ~256 tokens |
| Price per 1M Tokens | ~$0.10 (self-hosted infra only) | ~$0.09 (self-hosted infra only) | ~$0.07 (self-hosted infra only) |
| Avg Latency (per 128‑token input on GPU) | ~6ms | ~8ms | ~4ms |
| Throughput (embeddings/s on single GPU) | ~4,000/s | ~3,000/s | ~6,000/s |
| Model Size | ~420MB | ~420MB | ~90MB |
| Training Domain | General English STS + NLI | General English NLI | General English paraphrase mining |
| Uptime (self-hosted, well-managed) | ~99.5% | ~99.5% | ~99.5% |
30-day usage via LLM API
Architecture & Integration
One unified API. Every major model. Built-in reliability, cost control, and observability.
Automatically route each request to the best model across providers based on task, latency, and reliability—no client changes required as your stack evolves.
One endpoint, any modelOptimize for price and performance with per-request cost controls, dynamic model selection, and transparent usage insights that keep your AI bill predictable.
Cut cost, keep qualitySurvive provider outages and rate limits with built-in failover logic that retries on alternate models, preserving SLAs without custom recovery code.
Resiliency by defaultTrack latency, errors, tokens, and provider performance across every request with unified logs, traces, and metrics wired for your existing monitoring stack.
See every tokenCall high-level tasks—chat, tools, RAG, vision—instead of provider-specific APIs, so you can swap models without rewriting business logic or prompt glue.
Code to tasks, not vendorsRun massive batch workloads through a single endpoint with concurrency controls, retries, and progress tracking designed for production-scale pipelines.
Ship bulk, stay fastDecision guide
FAQ
all-mpnet-base-v2 is a Sentence Transformers text-embedding model based on MPNet, optimized for high-quality general-purpose sentence and document similarity.
It is best for semantic search, clustering, deduplication, recommendation, and textual similarity tasks where short-to-medium English sentences or paragraphs are compared.
all-mpnet-base-v2 is text-only and generates fixed-size vector embeddings from input text; it does not process images, audio, or other modalities.
The model outputs 768-dimensional embeddings and is typically used with short to moderate-length texts up to roughly a few hundred tokens.
Latency depends on input size and region, but LLM.API routes to optimized Sentence Transformers runtimes for low-latency, high-throughput embedding generation.
Usage is billed according to LLM.API’s standard embedding pricing for this provider, usually per-token or per-character, as shown in your LLM.API dashboard.
Call the LLM.API embeddings endpoint with provider set to Sentence Transformers and model set to all-mpnet-base-v2, passing your texts in the request body.
It is generally smaller and faster than larger Sentence Transformers models, offering strong performance for many tasks with reduced compute and latency.
It mainly targets English and may work on related languages, but performance is not guaranteed or optimized for fully multilingual use cases.
It cannot generate or edit text, struggles with very long documents, and performance may degrade on domain-specific or non-English data without adaptation.
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