tbuy

qwen3.7-text-embedding API pricing & access

qwen3.7-text-embedding API pricing: $0.07 per 1M tokens. Alibaba Qwen 3.7 text embedding model

qwen3.7-text-embedding API access

Alibaba Qwen 3.7 text embedding model

Compare supported API endpoints and capabilities, then access qwen3.7-text-embedding through the unified API gateway.

Category
vector
Supported APIs
OpenAI Chat Completions API
API endpoints
POST /v1/chat/completions

qwen3.7-text-embedding API pricing

Base input price
$0.07 per 1M tokens
Base output price
$0.07 per 1M tokens

Base prices are shown in USD before group-specific adjustments. Open the live pricing page for current access-group prices.

About qwen3.7-text-embedding

Qwen3.7 Text Embedding converts text into semantic vectors for retrieval, clustering and cross-language similarity. It returns vectors rather than answers; model and dimension choices must match the index.

Use fixed cases for the tasks described above. Integration follows the listed endpoint, selected route parameters and current group pricing; official capabilities do not establish acceptance for every channel. Compare text-embedding-v4.

Official reference 1, checked 2026-10-14.

Use cases and prompting

  1. Use the listed embedding interface and confirm vector output and actual dimensions on a few texts.
  2. Fix model, dimensions and preprocessing per index; rebuild rather than mixing incompatible vectors.
  3. Split by semantic boundaries and validate document/query usage parameters with representative samples.
  4. Measure retrieval on known relevant documents and record failures and usage before adding reranking.

API documentation.

How to call qwen3.7-text-embedding

qwen3.7-text-embedding is served through tbuy's unified gateway. Existing OpenAI or Anthropic SDK code keeps working: point base_url at https://modelsok.com and use your tbuy API key.

curl (OpenAI-compatible)

curl https://modelsok.com/v1/chat/completions \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "qwen3.7-text-embedding", "messages": [{"role": "user", "content": "Hello"}]}'

Python (openai SDK)

from openai import OpenAI

client = OpenAI(base_url="https://modelsok.com/v1", api_key="YOUR_API_KEY")
response = client.chat.completions.create(
    model="qwen3.7-text-embedding",
    messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)

Frequently asked questions about qwen3.7-text-embedding

What is qwen3.7-text-embedding?

Alibaba Qwen 3.7 text embedding model

How do I call qwen3.7-text-embedding?

Create an API key with access to qwen3.7-text-embedding, then use the exact model ID and a supported endpoint from the API access section. Request fields depend on the selected endpoint.

How is qwen3.7-text-embedding priced?

Pricing depends on the selected provider group and the model billing unit. Refer to the current input, output, request, or media prices in the pricing section.

How should I evaluate qwen3.7-text-embedding for my project?

Start with the use cases and prompting guidance on this page, then evaluate the model with representative inputs from your project.

Back to AI model API pricing