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qwen3-embedding-8b Cost Calculator - Llamagate

Calculate the cost of using qwen3-embedding-8b from Llamagate for your AI applications

qwen3-embedding-8b Cost Calculator

Mode: Embedding

Max: 40,960 tokens

Cost Breakdown

Input Cost$0.00002000
Total Cost$0.00002000

Pricing Details

Input: $0.0000000200 per token
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Model Specifications

Limits

Max Input Tokens40,960
Max Tokens40,960

About qwen3-embedding-8b

qwen3-embedding-8b is a powerful embedding AI model offered by Llamagate. This comprehensive guide provides detailed pricing information, technical specifications, and capabilities to help you understand the costs and features of using qwen3-embedding-8b in your embedding applications.

Pricing Information

Input Cost$0.02 per 1M tokens

Note: Use the interactive calculator above to estimate costs for your specific usage patterns.

Technical Specifications

Maximum Input Tokens40,960
Maximum Total Tokens40,960

Pro Tip

Use the maximum token limits shown above to understand the model's capacity. This model can handle up to 40,960 input tokens.

When should you use qwen3-embedding-8b?

qwen3-embedding-8b is best suited for the following scenarios:

  • Semantic search and similarity matching
  • RAG pipelines and vector databases
  • Document clustering and recommendations
When should you avoid qwen3-embedding-8b?
  • Complex multi-step reasoning or planning tasks
  • Applications requiring image, audio, or multimodal inputs
  • Very large documents or long conversational histories
  • General-purpose text generation or conversational AI
  • Creative writing or content generation tasks
How does qwen3-embedding-8b compare to similar models?

This model offers competitive input token pricing, making it cost-effective for applications that require extensive context or frequent input processing.

Understanding qwen3-embedding-8b pricing
  • qwen3-embedding-8b is a embedding and vector search model provided by Llamagate.
  • Input tokens are priced at $0.02 per 1M tokens.
  • The model supports a maximum input capacity of 40,960 tokens.
  • Llamagate offers qwen3-embedding-8b for embedding and vector search workloads — semantic search, similarity matching, recommendation systems, and vector-based applications.

How to Use This Calculator

Step 1: Enter the number of input tokens you expect to use. Input tokens include your prompt, system messages, and any context you provide to the model.

Step 2: Specify the number of output tokens you anticipate. Output tokens are the text generated by the model in response to your input.

Step 3: Review the cost breakdown to see the total estimated cost for your usage. The calculator automatically updates as you adjust the token counts.

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