Processing or generating 100,000 words (approximately 133,300 tokens) with Gemma 4 31B Instruct ranges from $0.008331 (cached input) to $0.10 (full generation).
You send 100,000 words as prompt context, documentation, or background knowledge.
Gemma 4 31B Instruct drafts a complete 100,000 words article, chapter, or code module.
80,000 words input prompt + 20,000 words output reply.
| Model | Provider | Input Cost (100,000 words) | Cached Input Cost | Output Cost (100,000 words) | Context Limit |
|---|---|---|---|---|---|
| Gemma 4 31B Instruct (Current) | $0.0333 | $0.008331 | $0.10 | 128,000 | |
| GPT-5.6 Terra | openai | $0.256 | $0.0256 | $1.536 | 1,050,000 |
| GPT-5.4 Workhorse | openai | $0.32 | $0.032 | $1.92 | 256,000 |
| o3-mini | openai | $0.1408 | $0.0704 | $0.5632 | 200,000 |
| o4-mini | openai | $0.1408 | $0.0352 | $0.5632 | 256,000 |
| o1-mini | openai | $0.1408 | $0.0704 | $0.5632 | 128,000 |
For Gemma 4 31B Instruct (sentencepiece tokenizer), 100,000 words is approximately 133,300 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 100,000 words as input costs $0.0333 (or $0.008331 with prompt caching). Generating 100,000 words as output costs $0.10. 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 $0.008331). For non-urgent asynchronous batch processing, 24-hour batch queues provide a flat 50% discount across all tokens (costing $0.0167 for input, $0.05 for output).
At an average human reading speed of 250 words per minute, 100,000 words takes approximately 400 minutes to read. In contrast, Gemma 4 31B Instruct can process or generate this text in seconds.