- Text Generation
MiniMax M2.7 is a 230B-parameter Mixture-of-Experts large language model from MiniMax, with 10B active parameters and a 204,800-token context window, optimized for coding, agentic tool use,…
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Qwen3 Embedding 8B
- Text Generation
Qwen3 Embedding 8B is Alibaba Qwen’s largest text embedding model in the Qwen3 Embedding series, producing high‑dimensional multilingual vector representations for retrieval and ranking tasks. It is optimized for long-context inputs and strong performance on multilingual embedding benchmarks.
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 Qwen3 Embedding 8B?
Qwen3 Embedding 8B is an 8‑billion‑parameter text embedding model from Alibaba’s Qwen3 family designed to generate 4096‑dimensional vector representations of text for downstream tasks. It is mainly used for semantic search and retrieval‑augmented generation pipelines, where it encodes queries and documents into a shared vector space for similarity search. It is also used for applications such as code and documentation search, text classification, and clustering in multilingual and cross‑lingual settings. It belongs to the Qwen3 Embedding model series, released in 0.6B, 4B, and 8B variants as part of the broader Qwen3 model family.
Model capabilities
5 Core Capabilities
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Text Embedding
Generates dense vector representations of text inputs suitable for search, semantic retrieval, and similarity-based applications.
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Semantic Similarity
Encodes sentences and documents so semantically related texts are mapped to nearby vectors, supporting clustering and relevance ranking.
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Multilingual Embeddings
Produces embeddings for multiple languages, enabling cross-lingual search and comparison within a shared semantic vector space.
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Document Retrieval
Supports building vector-based retrieval systems, enabling efficient nearest-neighbor search over large text corpora.
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Content Categorization
Facilitates classification tasks by providing embeddings that capture topics and intent for downstream machine learning models.
Use cases
6 Most Valuable Use Cases
- Semantic Text Search
- Document Clustering
- Topic Tagging
- Legal Case Retrieval
- Product Recommendation
- Code Snippet Search
Transparent pricing
Cost Comparison
LLM API offers the lowest cost and latency for Qwen3 Embedding–class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 80ms | 120k tps | 99.99% | $0.03 | $0.00 | 200K tokens |
| Qwen | Global | ~140ms | ~60k tps | ~99.9% | ~$0.09 | $0.00 | ~128K tokens |
| Alibaba Cloud | APAC | ~160ms | ~50k tps | ~99.9% | ~$0.10 | $0.00 | ~128K tokens |
| OpenRouter | Global | ~180ms | ~40k tps | ~99.5% | ~$0.12 | $0.00 | ~100K tokens |
Performance benchmarks
Technical Specifications
| Metric | Qwen3 Embedding 8B | text-embedding-3-large (OpenAI) | E5-Mistral-7B-Instruct (Mistral AI) |
|---|---|---|---|
| Dimensions | ~3072 | 3072 | ~2048 |
| Max Input Tokens | 8K | 8K | ~8K |
| Price per 1M Tokens | ~$0.05 | $0.13 | ~$0.10 |
| Avg Latency | ~220ms | ~200ms | ~260ms |
| Throughput | ~1,500 tps | ~2,000 tps | ~1,200 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 11.4B
- Prompt tokens processed (30 days)
- 3.1M
- API requests served (30 days)
- 620K
- Unique applications & users (30 days)
- 99.8%
- Average 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
Intelligently route each request to the best model across providers based on latency, price, and quality—without changing your code or re-deploying.
One API, every model. -
Cost-Aware Orchestration
Automatically balance premium and budget models per request, enforce spend policies, and get clear per-call cost visibility so you never lose control of your AI bill.
Optimize for every token. -
Resilient Fallback Flows
Define failover chains so if a model, region, or provider goes down, traffic transparently retries to healthy alternatives—no manual rewiring or on-call fire drills.
Stay online, automatically. -
End-to-End Observability
Trace every call across providers with unified logs, metrics, and structured events, making it easy to debug prompts, tune routing, and prove reliability to stakeholders.
See every token hop. -
Task-Level Abstractions
Describe tasks—chat, generation, RAG, tools—once and let LLM.API map them to the right models and parameters, keeping your app logic clean and portable.
Code to tasks, not models. -
High-Throughput Batching
Send thousands of requests in structured batches with concurrency controls, automatic chunking, and retries, maximizing throughput while protecting provider rate limits.
Scale without throttling.
Decision guide
When to Use — When NOT to Use
Use it if...
- You need general-purpose text embeddings for semantic search across multilingual content.
- You need dense vector representations to power retrieval-augmented generation pipelines efficiently.
- Your use case involves clustering or deduplicating large text corpora by semantic similarity.
- You need embeddings to match user queries with product descriptions or FAQs.
- Your use case involves intent classification or topic tagging using vector similarity search.
- You need to index long-form documents into chunk embeddings for downstream LLM retrieval.
- Your use case involves multilingual recommendation or content ranking based on semantic proximity.
Avoid if...
- You need a generative model that can write or edit text directly.
- Your workload requires real-time conversational responses rather than offline embedding computation.
- You need embeddings directly optimized for images, audio, or video inputs.
- Your workload requires task-specific supervised fine-tuning of the embedding model internals.
- You need strict, battle-tested PII redaction or safety filtering during inference itself.
- Your workload requires full transparency on proprietary training data sources and licensing.
- You need highly specialized domain embeddings pre-trained on your narrow industry corpus.
FAQ
Frequently Asked Questions
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What is Qwen3 Embedding 8B?
Qwen3 Embedding 8B is a large embedding model by Qwen designed to generate high-quality vector representations for text retrieval, search, and recommendation tasks.
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What is Qwen3 Embedding 8B best suited for?
It is best suited for semantic search, dense retrieval, reranking pipelines, clustering, and recommendation systems that require high-precision text similarity embeddings.
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What modalities does Qwen3 Embedding 8B support?
Qwen3 Embedding 8B is a text-only embedding model, taking text as input and returning numerical vector embeddings as output.
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What context window does Qwen3 Embedding 8B support on LLM.API?
On LLM.API, Qwen3 Embedding 8B typically supports long text inputs up to tens of thousands of tokens per request, depending on platform limits.
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How fast is Qwen3 Embedding 8B when called through LLM.API?
Latency is usually low enough for real-time retrieval use cases, but exact speed depends on request size and your selected LLM.API region and tier.
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How is pricing for Qwen3 Embedding 8B handled on LLM.API?
Qwen3 Embedding 8B pricing is metered per input token through LLM.API, following LLM.API’s unified pricing rather than Qwen’s native billing.
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How do I call Qwen3 Embedding 8B via the LLM.API?
You select the Qwen3 Embedding 8B model name in the embeddings endpoint on LLM.API and send your input texts as an array of strings.
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How does Qwen3 Embedding 8B compare to smaller Qwen embedding models?
Compared to smaller Qwen embedding models, Qwen3 Embedding 8B generally offers higher embedding quality at the cost of greater compute and latency.
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Can Qwen3 Embedding 8B handle multilingual text on LLM.API?
Qwen3 Embedding 8B can embed multiple languages, but performance may be strongest for languages most represented in its training data.
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What are the main limitations of Qwen3 Embedding 8B?
It cannot generate natural language, does not process images or audio, and may struggle with highly domain-specific or extremely long documents.
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