OpenAI text-embedding-3-large Cost Calculator
Simulate high-accuracy 3072-dimensional vector embedding costs for enterprise semantic search ($0.13 per 1M tokens).
Parameters & Config
Pre-configured official API pricing for this specific model tier
Prompt payload size including system prompt, user prompt, and chat history
Generated output tokens returned by the model
Total API requests processed per day across all users
Cost per 1,000,000 incoming prompt tokens
Cost per 1,000,000 generated completion tokens
Estimated 30-day cloud billing based on current traffic and model selection
Average daily API infrastructure expenditure incurred
Key Metrics & Sub-Totals
3 itemsCalculation Summary & Unit Breakdown
| Metric | Calculated Value | Notes / Logic |
|---|---|---|
| Active Model Configuration | Custom Configured Model | - |
| Daily Request Volume | 20,000 | queries/day |
| Tokens per Request | 800 | Prompt: 800 / Output: 0 |
| Daily Token Burn | 16,000,000 | tokens/day |
| Monthly Total Tokens | 480,000,000 | 30-day projection |
| Daily Average Infrastructure Cost | $2.08 | USD |
| Monthly Prompt (Input) Token Cost | $62.40 | USD |
| Monthly Completion (Output) Token Cost | $0.00 | USD |
| Total Projected Monthly Cloud Bill | $62.40 | USD |
Overview & Methodology
text-embedding-3-large is OpenAI’s most capable embedding model, supporting 3072 dimensions for top-tier multilingual semantic search, legal document retrieval, and complex code search at $0.13 per 1M tokens.
Mathematical Formulation
Total Cost = (Document Tokens / 1,000,000) × $0.13.Optimization Best Practices
Use text-embedding-3-large for cross-lingual search where query language differs from document language.
Leverage Matryoshka representation learning to truncate vectors to 1024 or 1536 dimensions if vector index costs become prohibitive.
Batch up to 2,048 text inputs per API call to minimize HTTP connection overhead.
Frequently Asked Questions
Q.How much does it cost to index 50 million tokens of documentation?
50M tokens at $0.13 per 1M costs $6.50 total.
Q.How does text-embedding-3-large compare to small in MTEB benchmarks?
Large scores approximately 64.6 on the MTEB benchmark compared to 62.3 on small, with notable gains in code and multilingual retrieval.
Q.What vector dimensionality does text-embedding-3-large output?
By default it outputs 3072 dimensions, customizable down to 1024 or 256 dimensions.