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cohere.embed-v4:0 Cost Calculator - AWS Bedrock

Calculate the cost of using cohere.embed-v4:0 from AWS Bedrock for your AI applications

cohere.embed-v4:0 Cost Calculator

Mode: Embedding

Max: 128,000 tokens

Cost Breakdown

Input Cost$0.00012000
Total Cost$0.00012000

Pricing Details

Input: $0.0000001200 per token
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Model Specifications

Limits

Max Input Tokens128,000
Max Tokens128,000

About cohere.embed-v4:0

cohere.embed-v4:0 is a powerful embedding AI model offered by AWS Bedrock. This comprehensive guide provides detailed pricing information, technical specifications, and capabilities to help you understand the costs and features of using cohere.embed-v4:0 in your embedding applications.

Pricing Information

Input Cost$0.12 per 1M tokens

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

Technical Specifications

Maximum Input Tokens128,000
Maximum Total Tokens128,000

Pro Tip

Use the maximum token limits shown above to understand the model's capacity. This model can handle up to 128,000 input tokens.

When should you use cohere.embed-v4:0?

cohere.embed-v4:0 is best suited for the following scenarios:

  • Semantic search and similarity matching
  • RAG pipelines and vector databases
  • Document clustering and recommendations
  • Long-context chat and document analysis
  • Agent workflows with large memory windows
When should you avoid cohere.embed-v4:0?
  • Complex multi-step reasoning or planning tasks
  • Applications requiring image, audio, or multimodal inputs
  • General-purpose text generation or conversational AI
  • Creative writing or content generation tasks
How does cohere.embed-v4:0 compare to similar models?

This model supports a larger context window than many alternatives, making it suitable for long-form inputs and memory-intensive applications.

Understanding cohere.embed-v4:0 pricing
  • cohere.embed-v4:0 is a embedding and vector search model provided by AWS Bedrock.
  • Input tokens are priced at $0.12 per 1M tokens.
  • The model supports a maximum input capacity of 128,000 tokens, enabling large-scale document and text embedding operations.
  • AWS Bedrock offers cohere.embed-v4:0 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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