Processing or generating 10,000 words (approximately 13,330 tokens) with Qwen 2.5 Coder 32B ranges from $0.000267 (cached input) to $0.007998 (full generation).
You send 10,000 words as prompt context, documentation, or background knowledge.
Qwen 2.5 Coder 32B drafts a complete 10,000 words article, chapter, or code module.
8,000 words input prompt + 2,000 words output reply.
| Model | Provider | Input Cost (10,000 words) | Cached Input Cost | Output Cost (10,000 words) | Context Limit |
|---|---|---|---|---|---|
| Qwen 2.5 Coder 32B (Current) | qwen | $0.002666 | $0.000267 | $0.007998 | 128,000 |
| GPT-5.3 Codex | openai | $0.0224 | $0.00224 | $0.1792 | 256,000 |
| DeepSeek Coder V2.5 | deepseek | $0.001866 | $0.000187 | $0.003732 | 128,000 |
| Codestral 2501 | mistral | $0.003999 | $0.0004 | $0.012 | 256,000 |
| Grok Build 0.1 | xai | $0.0133 | $0.002666 | $0.0267 | 256,000 |
| DeepInfra — Qwen 2.5 Coder 32B | deepinfra | $0.001066 | $0.000267 | $0.003199 | 128,000 |
For Qwen 2.5 Coder 32B (qwen_bpe tokenizer), 10,000 words is approximately 13,330 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 10,000 words as input costs $0.002666 (or $0.000267 with prompt caching). Generating 10,000 words as output costs $0.007998. 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 $0.000267). For non-urgent asynchronous batch processing, 24-hour batch queues provide a flat 50% discount across all tokens (costing $0.001333 for input, $0.003999 for output).
At an average human reading speed of 250 words per minute, 10,000 words takes approximately 40 minutes to read. In contrast, Qwen 2.5 Coder 32B can process or generate this text in seconds.