Processing or generating 500,000 words (approximately 666,500 tokens) with Text Embedding 004 ranges from $0.003333 (cached input) to $0.00 (full generation).
Quick answer
For 500,000 words, Text Embedding 004 processes approximately 666,500 tokens. That costs $0.0133 as uncached input, $0.003333 with the modeled cache discount, or $0.00 when generated as output.
Method & trust
The estimate converts the selected English word count with the model tokenizer family, then applies the published input/output price per million tokens. Real prompts vary with code, formatting, language, and system instructions.
You send 500,000 words as prompt context, documentation, or background knowledge.
Text Embedding 004 drafts a complete 500,000 words article, chapter, or code module.
400,000 words input prompt + 100,000 words output reply.
| Model | Provider | Input Cost (500,000 words) | Cached Input Cost | Output Cost (500,000 words) | Context Limit |
|---|---|---|---|---|---|
| Text Embedding 004 (Current) | $0.0133 | $0.003333 | $0.00 | 8,192 | |
| Text Embedding 3 (Small) | openai | $0.0133 | $0.003333 | $0.00 | 8,191 |
| Text Embedding 3 (Large) | openai | $0.0866 | $0.0217 | $0.00 | 8,191 |
For Text Embedding 004 (sentencepiece tokenizer), 500,000 words is approximately 666,500 tokens (an average ratio of 1.33 tokens per word in English). Code, technical vocabulary, and non-English scripts will have higher token densities.
Sending 500,000 words as input costs $0.0133 (or $0.003333 with prompt caching). Generating 500,000 words as output costs $0.00. Output tokens are more expensive because autoregressive token generation requires significantly more computation per token.
Yes! If your input text is part of a repeated context, prompt caching saves 50% to 90% (costing $0.003333). For non-urgent asynchronous batch processing, 24-hour batch queues provide a flat 50% discount across all tokens (costing $0.006665 for input, $0.00 for output).
At an average human reading speed of 250 words per minute, 500,000 words takes approximately 2000 minutes to read. In contrast, Text Embedding 004 can process or generate this text in seconds.