Processing or generating 500 words (approximately 667 tokens) with NVIDIA Nemotron-4 340B ranges from $0.00025 (cached input) to $0.002001 (full generation).
You send 500 words as prompt context, documentation, or background knowledge.
NVIDIA Nemotron-4 340B drafts a complete 500 words article, chapter, or code module.
400 words input prompt + 100 words output reply.
| Model | Provider | Input Cost (500 words) | Cached Input Cost | Output Cost (500 words) | Context Limit |
|---|---|---|---|---|---|
| NVIDIA Nemotron-4 340B (Current) | nvidia | $0.001 | $0.00025 | $0.002001 | 128,000 |
| GPT-5.6 Sol | openai | $0.0032 | $0.00032 | $0.0192 | 1,050,000 |
| GPT-5.6 Cyber | openai | $0.008 | $0.0008 | $0.048 | 1,050,000 |
| GPT-5.5 Standard | openai | $0.0032 | $0.00032 | $0.0192 | 512,000 |
| GPT-5.5 Pro | openai | $0.0192 | $0.00192 | $0.1152 | 512,000 |
| o3-pro (Frontier Reasoning) | openai | $0.0128 | $0.00128 | $0.0512 | 1,000,000 |
For NVIDIA Nemotron-4 340B (tiktoken_cl100k tokenizer), 500 words is approximately 667 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 words as input costs $0.001 (or $0.00025 with prompt caching). Generating 500 words as output costs $0.002001. 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 $0.00025). For non-urgent asynchronous batch processing, 24-hour batch queues provide a flat 50% discount across all tokens (costing $0.0005 for input, $0.001 for output).
At an average human reading speed of 250 words per minute, 500 words takes approximately 2 minutes to read. In contrast, NVIDIA Nemotron-4 340B can process or generate this text in seconds.