Qwen3 Embedding 8B generates 4096-dimensional text embeddings optimized for multilingual retrieval and long-context RAG pipelines. Reach for it when document length or cross-language recall outweighs the need for the lowest possible per-token rate.
Modalities
Text → Text
Usage
How much this model is actually called here.
Rank
#66
of 101 active models
Tokens served
150.3K
all-time
Platform share
0.0%
of all tokens
Pricing
Pay per token. Cached input bills at this model’s own cached rate, listed below.
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Estimated monthly cost
$0.8100
$0.0270 / day on Qwen3 Embedding 8B
Same workload on:
Estimates use list pricing. Actual bills depend on real token counts, and every response includes its exact cost.
When to use Qwen3 Embedding 8B
Where this model earns its cost — and where it doesn't.
This model produces dense vector representations with a 32,768-token context window. It is designed for text-only embedding tasks and does not support reasoning, vision, or structured output generation.
On Kyma, it runs as an OpenAI-compatible endpoint behind a single API key. Requests benefit from automatic failover if a serving path degrades, and responses return the exact cost in usage.cost alongside an X-Kyma-Model header. Prompt caching does not apply to embeddings: every input token bills at the input rate.
Because it is strictly an embedding model, it returns zero output tokens and cannot generate conversational text. It operates at a medium speed tier, making it better suited for batch indexing or retrieval-heavy workflows than for real-time, low-latency interactive search.
Multilingual Document Search
Finds relevant passages across different languages without translation overhead.
Long-Form Context Indexing
Embeds full documents up to 32K tokens to avoid aggressive chunking.
High-Recall Retrieval Pipelines
Prioritizes semantic match quality over minimal compute cost for RAG systems.
Not ideal for: Do not use this model for real-time chat, text generation, or tasks requiring sub-100ms latency, as it only outputs vectors and runs at a medium speed tier.
How it compares
Against the peers people actually weigh it against.
| Spec | Qwen3 Embedding 8B | Qwen 3.8 27B | Qwen 3.7 Flash |
|---|---|---|---|
| Input /1M | $0.0135 | $0.3576 | $0.0504 |
| Output /1M | $0.00 | $2.554 | $0.2187 |
| Context | 33K | 1M | 1M |
| Tools | No | Yes | Yes |
| Reasoning | No | Yes | Yes |
| Throughput | 299 tok/s | 36 tok/s | 68 tok/s |
Quick start
Up and running in under two minutes.
- 1
Create an API key
Sign up and grab a key from the dashboard — $0.50 free credit on the free tier, which covers Qwen3 Embedding 8B. No card required.
Get API key → - 2
Make your first request
Submit a generation job and poll until it succeeds.
curl https://kymaapi.com/v1/embeddings \ -H "Authorization: Bearer YOUR_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "qwen3-embedding-8b", "input": ["first document", "second document"] }'
FAQ
Common questions about this model.
What is the context window of Qwen3 Embedding 8B?
How much does the Qwen3 Embedding 8B API cost?
Does Qwen3 Embedding 8B support function calling?
How do I use Qwen3 Embedding 8B?
Does this model generate text responses?
How does Kyma handle prompt caching for this endpoint?
Can I use the same API key for this model as I do for others?
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|---|---|---|---|
 Qwen 3.8 Flash | 1M | $0.2045 | $0.641 |
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| Qwen 3.7 Flash | 1M | $0.0504 | $0.2187 |
| Qwen 3.7 Plus | 1M | $0.4431 | $1.773 |
| Qwen 3.7 Max | 1M | $1.863 | $5.586 |
| Qwen 3.6 Plus | 1M | $0.454 | $2.724 |
| Qwen 3 Coder | 131K | $0.3586 | $1.629 |