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The AI Translation Cost Calculator estimates the expense of translating text using AI services like DeepL, Google Cloud Translation, AWS Translate, and LLM-based translation (GPT-4, Claude). It compares per-character and per-word pricing across services and language pairs.
Công thức
- C
- Character Count (characters) — Total characters in source text
- P
- Price per Million Characters ($/1M chars) — Service rate per million characters translated
- L_src
- Source Language (language code) — Original language of the content
- L_tgt
- Target Language (language code) — Translation target language
Hướng dẫn từng bước
- 1Enter the text volume in words, characters, or pages
- 2Select source and target languages
- 3Choose translation services to compare (DeepL, Google, AWS, LLM-based)
- 4View per-page cost, total cost, and quality comparison notes
Ví dụ có lời giải
Lỗi thường gặp cần tránh
- ✕Comparing AI translation cost to human translation without accounting for post-editing (MTPE) time needed for publication quality
- ✕Using character-based pricing formulas for Chinese/Japanese/Korean where character counts differ significantly from alphabetic languages
- ✕Not testing translation quality across multiple services for your specific domain — generic cost comparison misses accuracy differences
Câu hỏi thường gặp
Is AI translation good enough to replace human translators?
For internal documents, customer support, and general web content, AI translation (especially DeepL and GPT-4) produces publication-quality results for most European language pairs. For legal, medical, literary, or highly technical content, AI translation should be followed by human post-editing (MTPE). The best workflow is AI draft + human review, which is 3-5x cheaper than fully human translation.
Which AI translation service produces the best quality?
DeepL consistently ranks highest for European languages. GPT-4 and Claude produce excellent results with custom instructions (tone, terminology) and handle context better than traditional NMT. Google Translate is strongest for rare language pairs. For Asian languages, native-trained models often outperform general-purpose services.
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