Translify is an AI reading companion for foreign-language books. You upload a PDF or EPUB, Translify rebuilds it in your language with the original layout intact, then lets you chat with the whole book, highlight passages for AI explanations, and quiz yourself on what you read.

How is Translify different from Google Translate or DeepL?

Google Translate and DeepL translate text and return a file. Translify gives you a complete reading experience around the translation: chat with the whole book, AI-explained highlights, quiz mode, and side-by-side bilingual reading. It is a study tool, not a translation utility.

Is there a free trial?

Yes — every paid plan starts with a 14-day free trial, no credit card required. Translify also offers a 30-day money-back guarantee on every paid plan, with no questions asked.

Which file types and languages does Translify support?

PDF and EPUB up to 200 MB per book. Translify supports 14 target languages: English, French, Spanish, German, Italian, Portuguese, Dutch, Arabic, Chinese, Japanese, Korean, Russian, Hindi, and Turkish — with correct rendering for right-to-left scripts and CJK characters.

Does the translation keep the book's layout?

Yes. Translify rebuilds every page with the same structure: paragraphs in the same place, images where they were, tables intact, headings preserved. A side-by-side mode lets you switch back to the source at any time.

How accurate are Translify's chat answers?

Every chat answer links back to the exact passage in the source — page number plus a highlighted excerpt. If a faithful citation cannot be found in the book, Translify says so rather than guessing. The underlying model is Anthropic's Claude.

Can I cancel any time?

Yes — one click in account settings. You keep access through the period you have already paid for, and Translify never auto-charges the day after cancellation.

Does Translify train AI models on my uploaded books?

No. Uploaded books are private to your account, encrypted at rest, and never used to train any AI model. You can delete a book and all its derivatives at any time from your library.

Which is the best AI translator for books in 2026?

Different tools win for different content. DeepL is fastest and produces the most natural-sounding output for European languages but has weaker long-context coherence. ChatGPT (GPT-4o) handles long context well and is good at style consistency but occasionally invents text on long passages. Claude (4.7 Opus) is the strongest at preserving nuance and structural integrity over a full book, especially for technical and academic material. For most readers, the answer is: use Claude or DeepL through a tool that handles the book-level orchestration (EPUB structure, chapter alignment, glossary, highlights).

How much does it cost to translate a 300-page book with each tool?

DeepL Pro: $9-$25/month with monthly character limits that cover roughly 2-5 books. ChatGPT Plus: $20/month, no hard character limit but slow for full books. Claude Pro: $20/month, similar. Per-API costs (if you're scripting it yourself): DeepL $25 per million characters; OpenAI GPT-4o around $5 per million input + $15 output tokens (a 300-page book is roughly 200k tokens); Claude 4.7 Opus around $15 input + $75 output per million. For most users, a tool that bundles the cost into a flat subscription ($10-$25/month with reasonable quota) is cheaper than going direct.

Does DeepL still win for European languages?

For short paragraphs in isolation, yes — DeepL's German, French, Spanish, and Italian outputs read more naturally than the LLM-based alternatives, with fewer awkward phrasings. For long-context translation (a chapter, a book), LLMs catch up and often surpass because they handle terminology consistency, callbacks, and tone better than DeepL's sentence-level model. The threshold is roughly 5,000 words: shorter than that, DeepL wins; longer, LLMs.

Can any of these handle layout, footnotes, and equations?

None of the raw tools (DeepL, ChatGPT, Claude direct) handle EPUB or PDF structure. They translate text, not documents. To preserve layout, footnotes, equations, page numbers, and figure references you need a tool that wraps the translation engine in an EPUB/PDF processor. Translify, Immersive Translate, and BookTranslate.ai all do this; the underlying translation engine in each is one of the three (DeepL or an LLM).

Which is most accurate for technical or scientific translation?

Claude 4.7 Opus is the strongest for technical and scientific translation as of 2026. It preserves terminology consistency, handles equations described in prose, and is least likely to flatten field-specific jargon into generic phrasing. GPT-4o is a close second. DeepL is weaker here — it occasionally produces confident-sounding generic phrasing where a precise technical term is required.

What about less-common language pairs (e.g., English ↔ Indonesian)?

DeepL supports roughly 30 languages; the LLMs support 100+ but with quality drop-off for the long tail. For mainstream languages (English, French, Spanish, German, Italian, Portuguese, Russian, Chinese, Japanese, Korean, Arabic) all three are strong. For mid-tier languages (Indonesian, Malay, Vietnamese, Thai, Polish, Turkish, Persian) the LLMs win because DeepL either doesn't support them or supports them weakly. For rare languages, LLMs are the only option and quality varies.

Will AI replace human literary translators?

For commercial fiction, technical manuals, and academic papers, AI translation is already production-grade for most readers. For literary fiction with high prose stakes (a translated Sebald, an Annie Ernaux), human translators still outperform AI on the things that matter — voice consistency, register, rhythm. The likely 2026-2030 trajectory is: AI does the first pass; human translators edit. Pure-AI literary translation is acceptable but rarely best-in-class.

DeepL vs ChatGPT vs Claude for translating books (2026 comparison)

DeepL, ChatGPT (GPT-4o), and Claude (4.7 Opus) are the three production-grade translation engines as of mid-2026. Each is strong, each has weaknesses, and the choice depends substantially on what you're translating. This is a reference comparison: same source texts run through all three, with notes on quality, cost, language coverage, and the tooling around each.

The short version

DeepL: fastest, most natural-sounding for short passages in major European languages. Weaker at long-context coherence and technical terminology. Best for: commercial documents, short articles, emails, and as a translation engine inside a structure-aware tool.
ChatGPT (GPT-4o): strong long-context handling, good style consistency, fluent output across more languages than DeepL. Occasionally hallucinates on long passages — confident phrasing for content not in the source. Best for: general-purpose translation, dialogue-heavy fiction, less-common language pairs.
Claude (4.7 Opus): the strongest at preserving nuance and structural integrity over a full book. Lowest hallucination rate of the three on long-form translation. Best for: literary fiction, technical and academic material, anything where terminology consistency matters across many pages.

None of the three, used directly, handle EPUB or PDF structure. For full-book translation you need a tool that wraps one of them and processes the document — Translify, Immersive Translate, BookTranslate.ai. Most of these tools let you choose the underlying engine.

Test methodology

Three sources, run through each engine:

Fiction: a 12-page chapter of a contemporary Spanish novel (Almudena Grandes, Los pacientes del Doctor García), translated into English.
Technical: a 6-page section of a German computer science textbook (Sedgewick, Algorithmen), translated into English.
Academic: a French research paper from Annales (history journal), translated into English.

Each output was reviewed by a fluent reader of the source language with comparison to the existing professional English translation where one existed. Scoring focused on three criteria: accuracy, readability, and terminology consistency.

Results by content type

Fiction

DeepL: smoothest at the sentence level. Dialogue reads naturally; descriptive prose reads naturally. Loses some consistency over the chapter — a recurring object referred to with slightly different English nouns in different paragraphs. No hallucination.

GPT-4o: stylistically the strongest. Caught a joke in dialogue that DeepL flattened. One instance of confident-sounding text that wasn't in the original (added a sentence of context that the source didn't have).

Claude 4.7 Opus: closest to the published professional translation. Preserved the joke, the rhythm, and the chapter's tone shift. No hallucination. Slightly more formal English than the original Spanish warranted — Claude's default register skews literary, which helps for some fiction and hurts for others.

Ranking for fiction: Claude ≈ GPT-4o > DeepL, with Claude winning on faithfulness and GPT winning on style.

Technical

DeepL: produced fluent English with several terminology errors. “Hashfunktion” was translated as “hash function” in one paragraph and “hashing function” in the next. Mathematical notation described in prose was occasionally rephrased in ways that changed the mathematical meaning.

GPT-4o: better terminology consistency than DeepL. Mathematical descriptions were preserved correctly. Some awkward phrasing in passages that mixed code, prose, and notation.

Claude 4.7 Opus: best terminology consistency, cleanest handling of mixed prose-and-notation passages. Used the canonical English term for each German technical term and held it across the section.

Ranking for technical: Claude > GPT-4o > DeepL.

Academic

DeepL: fluent French-to-English but flattened some historiographical jargon. The French longue durée was sometimes translated, sometimes left in French — inconsistently. Footnote references were preserved correctly.

GPT-4o: handled historiographical terminology better, consistently. Preserved citation structure. Occasionally added explanatory text not in the original — useful for readers, but adding text to an academic translation is wrong.

Claude 4.7 Opus: most faithful, including correct treatment of in-text citations and footnote structure. Did not add explanatory text. Preserved scholarly register correctly.

Ranking for academic: Claude > GPT-4o > DeepL.

Cost comparison (for a 300-page book)

A 300-page book is roughly 90,000 words, or about 540,000 characters. At typical 2026 pricing:

DeepL Pro Advanced: $29/month, 1M character limit — fits one full book per month. API: roughly $14 for the book if paid per character.
GPT-4o via API: roughly $4 for a 300-page book (200k input tokens at $5/M, 200k output tokens at $15/M). Via ChatGPT Plus: $20/month flat, but the interface isn't designed for long-form translation and you'll spend most of the time wrangling context windows.
Claude 4.7 Opus via API: roughly $18 for a 300-page book (200k input at $15/M, 200k output at $75/M). Via Claude Pro: $20/month flat, same interface caveats.

For most users, the right move is a tool that bundles one or more of these engines into a flat subscription with book-level orchestration. Doing it yourself via API requires writing the EPUB/PDF pipeline, the chunking strategy, the terminology consistency layer, and the cost controls — which is what the tools provide. Translify uses Claude as its primary engine and falls back to DeepL for short passages where DeepL's outputs are stronger.

Language coverage

DeepL: 31 languages as of 2026, with quality consistently high for the European languages and good for the major Asian languages (Japanese, Chinese, Korean). No support for Indonesian, Malay, Vietnamese, Thai, Hebrew, Arabic.

GPT-4o: 100+ languages claimed, with strong quality for roughly the top 50 (the languages with substantial web presence). For low-resource languages, quality varies — competent for survival translation, less reliable for literary.

Claude 4.7 Opus: similar coverage to GPT-4o, with roughly comparable quality. Slightly stronger on Arabic, Persian, and Hebrew in our testing; slightly weaker on Chinese poetry. Both substantially exceed DeepL for non-European languages.

Recommendations

If you're translating short documents or emails: DeepL directly. Free tier handles short pieces, Pro for daily use.

If you're translating a single book and want maximum control: Claude 4.7 Opus via API, with a script to chunk by chapter. ~$15-$25 per book, several hours of setup time.

If you're translating books regularly and don't want to write plumbing: a book-aware tool (Translify, Immersive Translate, BookTranslate.ai) that wraps one of these engines and handles EPUB/PDF structure, chapter alignment, and terminology consistency. Pricing comparable to direct LLM subscriptions for one or two books a month; better cost structure than direct API for sustained use.

For most readers, the question isn't which engine but which product. The engines are largely interchangeable for the long-form use case; the difference is whether you have to write the EPUB processing yourself.

Translify uses Claude 4.7 Opus as its primary translation engine, with DeepL fallback for European-language short passages. Try the 14-day free trial.

Frequently asked

Which is the best AI translator for books in 2026?: Different tools win for different content. DeepL is fastest and produces the most natural-sounding output for European languages but has weaker long-context coherence. ChatGPT (GPT-4o) handles long context well and is good at style consistency but occasionally invents text on long passages. Claude (4.7 Opus) is the strongest at preserving nuance and structural integrity over a full book, especially for technical and academic material. For most readers, the answer is: use Claude or DeepL through a tool that handles the book-level orchestration (EPUB structure, chapter alignment, glossary, highlights).
How much does it cost to translate a 300-page book with each tool?: DeepL Pro: $9-$25/month with monthly character limits that cover roughly 2-5 books. ChatGPT Plus: $20/month, no hard character limit but slow for full books. Claude Pro: $20/month, similar. Per-API costs (if you're scripting it yourself): DeepL $25 per million characters; OpenAI GPT-4o around $5 per million input + $15 output tokens (a 300-page book is roughly 200k tokens); Claude 4.7 Opus around $15 input + $75 output per million. For most users, a tool that bundles the cost into a flat subscription ($10-$25/month with reasonable quota) is cheaper than going direct.
Does DeepL still win for European languages?: For short paragraphs in isolation, yes — DeepL's German, French, Spanish, and Italian outputs read more naturally than the LLM-based alternatives, with fewer awkward phrasings. For long-context translation (a chapter, a book), LLMs catch up and often surpass because they handle terminology consistency, callbacks, and tone better than DeepL's sentence-level model. The threshold is roughly 5,000 words: shorter than that, DeepL wins; longer, LLMs.
Can any of these handle layout, footnotes, and equations?: None of the raw tools (DeepL, ChatGPT, Claude direct) handle EPUB or PDF structure. They translate text, not documents. To preserve layout, footnotes, equations, page numbers, and figure references you need a tool that wraps the translation engine in an EPUB/PDF processor. Translify, Immersive Translate, and BookTranslate.ai all do this; the underlying translation engine in each is one of the three (DeepL or an LLM).
Which is most accurate for technical or scientific translation?: Claude 4.7 Opus is the strongest for technical and scientific translation as of 2026. It preserves terminology consistency, handles equations described in prose, and is least likely to flatten field-specific jargon into generic phrasing. GPT-4o is a close second. DeepL is weaker here — it occasionally produces confident-sounding generic phrasing where a precise technical term is required.
What about less-common language pairs (e.g., English ↔ Indonesian)?: DeepL supports roughly 30 languages; the LLMs support 100+ but with quality drop-off for the long tail. For mainstream languages (English, French, Spanish, German, Italian, Portuguese, Russian, Chinese, Japanese, Korean, Arabic) all three are strong. For mid-tier languages (Indonesian, Malay, Vietnamese, Thai, Polish, Turkish, Persian) the LLMs win because DeepL either doesn't support them or supports them weakly. For rare languages, LLMs are the only option and quality varies.
Will AI replace human literary translators?: For commercial fiction, technical manuals, and academic papers, AI translation is already production-grade for most readers. For literary fiction with high prose stakes (a translated Sebald, an Annie Ernaux), human translators still outperform AI on the things that matter — voice consistency, register, rhythm. The likely 2026-2030 trajectory is: AI does the first pass; human translators edit. Pure-AI literary translation is acceptable but rarely best-in-class.

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