Qwen3 Embedding 8B

Overview

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 this model’s cached 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.

Specs

Field	Value
Model ID	`qwen3-embedding-8b`
Best for	Multilingual retrieval, long documents, high-recall RAG
Context window	32,768
Input modalities	Text
Output modalities	Text
Tool calling	Yes
Structured outputs	Yes
Prompt caching	Yes
Speed	Medium
Cost band	Cheap
Release stage	Stable

Pricing

	Per 1M tokens
Input	$0.0135
Output	$0.00

Use this when

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.

Pick something else when

You need minimal compute cost and fast indexing: use embeddinggemma-300m.

Example

from openai import OpenAI

client = OpenAI(base_url="https://kymaapi.com/v1", api_key="kyma-...")

response = client.embeddings.create(
    model="qwen3-embedding-8b",
    input=["first document", "second document"],
)

Agent query example

Ask the API which models fit, instead of hardcoding an id:

curl "https://kymaapi.com/v1/models?recommended_for=search-documents&quality_tier=frontier-open"

FAQ

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, which have no reusable prompt prefix and therefore no cached 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.

​Overview

​Specs

​Pricing

​Use this when

​Not ideal for

​Pick something else when

​Example

​Agent query example

​FAQ