Processing or generating 1,000 words (approximately 1,280 tokens) with o1 (Reasoning) ranges from $0.0096 (cached input) to $0.0768 (full generation).
You send 1,000 words as prompt context, documentation, or background knowledge.
o1 (Reasoning) drafts a complete 1,000 words article, chapter, or code module.
800 words input prompt + 200 words output reply.
| Model | Provider | Input Cost (1,000 words) | Cached Input Cost | Output Cost (1,000 words) | Context Limit |
|---|---|---|---|---|---|
| o1 (Reasoning) (Current) | openai | $0.0192 | $0.0096 | $0.0768 | 200,000 |
| GPT-5.6 Sol | openai | $0.0064 | $0.00064 | $0.0384 | 1,050,000 |
| GPT-5.6 Cyber | openai | $0.016 | $0.0016 | $0.096 | 1,050,000 |
| GPT-5.5 Standard | openai | $0.0064 | $0.00064 | $0.0384 | 512,000 |
| GPT-5.5 Pro | openai | $0.0384 | $0.00384 | $0.2304 | 512,000 |
| o3-pro (Frontier Reasoning) | openai | $0.0256 | $0.00256 | $0.1024 | 1,000,000 |
For o1 (Reasoning) (o200k_base tokenizer), 1,000 words is approximately 1,280 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 words as input costs $0.0192 (or $0.0096 with prompt caching). Generating 1,000 words as output costs $0.0768. 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 $0.0096). For non-urgent asynchronous batch processing, 24-hour batch queues provide a flat 50% discount across all tokens (costing $0.0096 for input, $0.0384 for output).
At an average human reading speed of 250 words per minute, 1,000 words takes approximately 4 minutes to read. In contrast, o1 (Reasoning) can process or generate this text in seconds.