Processing or generating 2,500 words (approximately 3,333 tokens) with NVIDIA Nemotron-4 340B ranges from $0.00125 (cached input) to $0.009999 (full generation).
You send 2,500 words as prompt context, documentation, or background knowledge.
NVIDIA Nemotron-4 340B drafts a complete 2,500 words article, chapter, or code module.
2,000 words input prompt + 500 words output reply.
| Model | Provider | Input Cost (2,500 words) | Cached Input Cost | Output Cost (2,500 words) | Context Limit |
|---|---|---|---|---|---|
| NVIDIA Nemotron-4 340B (Current) | nvidia | $0.005 | $0.00125 | $0.009999 | 128,000 |
| GPT-5.6 Sol | openai | $0.016 | $0.0016 | $0.096 | 1,050,000 |
| GPT-5.6 Cyber | openai | $0.04 | $0.004 | $0.24 | 1,050,000 |
| GPT-5.5 Standard | openai | $0.016 | $0.0016 | $0.096 | 512,000 |
| GPT-5.5 Pro | openai | $0.096 | $0.0096 | $0.576 | 512,000 |
| o3-pro (Frontier Reasoning) | openai | $0.064 | $0.0064 | $0.256 | 1,000,000 |
For NVIDIA Nemotron-4 340B (tiktoken_cl100k tokenizer), 2,500 words is approximately 3,333 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 2,500 words as input costs $0.005 (or $0.00125 with prompt caching). Generating 2,500 words as output costs $0.009999. 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 $0.00125). For non-urgent asynchronous batch processing, 24-hour batch queues provide a flat 50% discount across all tokens (costing $0.0025 for input, $0.005 for output).
At an average human reading speed of 250 words per minute, 2,500 words takes approximately 10 minutes to read. In contrast, NVIDIA Nemotron-4 340B can process or generate this text in seconds.