gte-large Cost Calculator - Fireworks Ai-embedding-models

Calculate the cost of using gte-large from Fireworks Ai-embedding-models for your AI applications

gte-large Cost Calculator

Mode: Embedding

Input Tokens

Max: 512 tokens

Cost Breakdown

Input Cost$0.00001600

Total Cost$0.00001600

Pricing Details

Input: $0.0000000160 per token

Output: $0.0000000160 per token

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Model Specifications

Limits

Max Input Tokens512

Max Tokens512

About gte-large

gte-large is a powerful embedding AI model offered by Fireworks Ai-embedding-models. This comprehensive guide provides detailed pricing information, technical specifications, and capabilities to help you understand the costs and features of using gte-large in your embedding applications.

Pricing Information

Input Cost$0.02 per 1M tokens

Output Cost$0.02 per 1M tokens

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

Technical Specifications

Maximum Input Tokens512

Maximum Total Tokens512

Pro Tip

Use the maximum token limits shown above to understand the model's capacity. This model can handle up to 512 input tokens.

When should you use gte-large?

gte-large 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 gte-large?

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 gte-large 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 gte-large pricing

gte-large is a embedding and vector search model provided by Fireworks Ai-embedding-models.
Input tokens are priced at $0.02 per 1M tokens.
Output tokens are priced at $0.02 per 1M tokens.
The model supports a maximum input capacity of 512 tokens.
For this model, input tokens are more expensive than output tokens, so optimizing your prompts can help manage costs.
Fireworks Ai-embedding-models offers gte-large 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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