Alibaba

Qwen3 Embedding 8B

Name: Qwen3 Embedding 8B
Brand: Alibaba
Price: 0.0135 USD
Availability: InStock

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

Input

$0.0135 /1M

Output

$0.00 /1M

Cached input

$0.00135 /1M90% off

Context

33K

Speed

medium

Performance

Live production data from real requests on Kyma — not synthetic benchmarks.

Rank

#73

of 74 active models

Tokens served

all-time

Total requests1

Platform share0.0%

Pricing

Pay per token. Cached input is billed at 10% of the input rate.

$0.0135 /1M input$0.00 /1M output

Hobby10 req/day · 2K in / 500 out

~$0.01/mo

Production1,000 req/day · 2K in / 500 out

~$0.81/mo

Scale20,000 req/day · 2K in / 500 out

~$16.20/mo

+ Estimate your workload

Requests / day1,000

Input tokens / request2,000

Output tokens / request500

Cache hit rate30%

Estimated monthly cost

$0.5913

$0.0197 / day on Qwen3 Embedding 8B

Same workload on:

Qwen 3.7 Flash$5.06+756%

Qwen 3.7 Max$209+35258%

Estimates use list pricing with cached input billed at the 90%-discount rate. Actual bills depend on real token counts — every response includes its exact cost.

When to use Qwen3 Embedding 8B

Updated 2026-07-31

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 is supported, billing repeated prefixes at 10% of the standard 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 3.7 Flash	Qwen 3.7 Max
Input /1M	$0.0135	$0.0405	$1.991
Output /1M	$0.00	$0.1755	$5.974
Context	33K	1M	1M
Tools	Yes	Yes	Yes
Reasoning	No	Yes	Yes
Speed	medium	fast	medium

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, no card required.
Get API key →

Make your first request

Drop in your key and send a chat completion — fully OpenAI-compatible.

curl https://kymaapi.com/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen3-embedding-8b",
    "messages": [
      {"role": "user", "content": "Explain prompt caching in one paragraph."}
    ]
  }'

Stream responses

Add "stream": true to receive tokens as they arrive.

curl https://kymaapi.com/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen3-embedding-8b",
    "stream": true,
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

FAQ

Common questions about this model.

What is the context window of Qwen3 Embedding 8B?

Qwen3 Embedding 8B has a 33K-token context window — roughly 48 pages of text in a single request.

How much does the Qwen3 Embedding 8B API cost?

$0.0135 per 1M input tokens and $0.00 per 1M output tokens, with cached input at $0.00135/1M — a 90% discount on repeated prompt prefixes. No subscription; you pay only for what you use.

Does Qwen3 Embedding 8B support function calling?

Yes — Qwen3 Embedding 8B supports tool/function calling and structured outputs (JSON mode), so it works with agent frameworks out of the box.

How do I use Qwen3 Embedding 8B?

Kyma is OpenAI-compatible: point your SDK's base URL at https://kymaapi.com/v1, use your Kyma API key, and set the model to qwen3-embedding-8b. Signing up is free and includes $0.50 of credit — no card required.

Does this model generate text responses?

No, it is strictly an embedding model that outputs 4096-dimensional vectors and returns zero output tokens.

How does Kyma handle prompt caching for this endpoint?

Kyma supports caching for embedding requests, billing repeated prompt prefixes at 10% of the standard input rate.

Can I use the same API key for this model as I do for others?

Yes, Kyma is OpenAI-compatible and uses a single API key across all models at the standard base URL.

Start with $0.50 free credit — no card required.Create account →

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