Processing or generating 1,000,000 words (approximately 1,280,000 tokens) with GPT-5.4 nano ranges from $0.0256 (cached input) to $1.60 (full generation).
You send 1,000,000 words as prompt context, documentation, or background knowledge.
GPT-5.4 nano drafts a complete 1,000,000 words article, chapter, or code module.
800,000 words input prompt + 200,000 words output reply.
| Model | Provider | Input Cost (1,000,000 words) | Cached Input Cost | Output Cost (1,000,000 words) | Context Limit |
|---|---|---|---|---|---|
| GPT-5.4 nano (Current) | openai | $0.256 | $0.0256 | $1.60 | 128,000 |
| GPT-5.6 Luna | openai | $0.256 | $0.0256 | $1.536 | 1,050,000 |
| GPT-5.4 mini | openai | $0.96 | $0.096 | $5.76 | 256,000 |
| GPT-4o mini | openai | $0.192 | $0.096 | $0.768 | 128,000 |
| Claude Haiku 4.5 | anthropic | $1.32 | $0.132 | $6.60 | 1,000,000 |
| Claude 3.5 Haiku | anthropic | $1.056 | $0.1056 | $5.28 | 200,000 |
For GPT-5.4 nano (o200k_base tokenizer), 1,000,000 words is approximately 1,280,000 tokens (an average ratio of 1.28 tokens per word in English). Code, technical vocabulary, and non-English scripts will have higher token densities.
Sending 1,000,000 words as input costs $0.256 (or $0.0256 with prompt caching). Generating 1,000,000 words as output costs $1.60. 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 $0.0256). For non-urgent asynchronous batch processing, 24-hour batch queues provide a flat 50% discount across all tokens (costing $0.128 for input, $0.80 for output).
At an average human reading speed of 250 words per minute, 1,000,000 words takes approximately 4000 minutes to read. In contrast, GPT-5.4 nano can process or generate this text in seconds.