DeepL vs Google Translate: Which Is More Accurate in 2025?

DeepL vs Google Translate: Which Is More Accurate in 2025?

If you’ve ever pasted a translated document into a client email only to cringe at the awkward phrasing, you know the stakes. In 2025, the gap between a passable translation and a perfectly natural one can mean the difference between closing a deal and losing trust. For years, professionals in localization and global business have leaned on DeepL for its superior handling of nuance and context, while using Google Translate for its unmatched speed and language coverage. But the landscape is shifting.

Having spent the last year rigorously testing both platforms for technical documentation and marketing copy across English, German, Japanese, and French, I’ve watched Google make significant strides with its latest PaLM 2 and Gemini model integrations. The question is no longer about which tool is technically capable, but which one delivers consistent, publication-ready fluency when it matters most.

This isn’t about picking a universal winner. It’s about strategic matching. In this updated 2025 comparison, we’ll move beyond simple phrase tests and dive into real-world accuracy for:

• Complex business and technical jargon
• Idiomatic expressions and cultural nuance
• Overall tone and syntactic naturalness

I’ll share specific, side-by-side examples from recent projects and give you a clear framework for deciding which engine to use for your specific task. The answer might surprise you, as the definition of “accuracy” itself has evolved from mere word-for-word correctness to contextual and intent-aware precision. Let’s find your best fit.

The Battle for Linguistic Nuance

For years, the translation landscape has been defined by a clear divide. On one side stood Google Translate, the ubiquitous pioneer, winning on sheer scale with support for over 130 languages and blistering speed. On the other, DeepL carved out its reputation as the quality specialist, consistently praised by translators, writers, and businesses for its superior grasp of context, grammar, and—most importantly—natural phrasing. It wasn’t just about translating words; it was about conveying intent with a fluidity that often felt human.

But in 2025, the ground has shifted. Google is no longer just a search giant; it’s an AI powerhouse. With the integration of its Gemini models and continuous advances in its Pathways Language Model (PaLM), Google has been aggressively targeting the very nuance that was once DeepL’s fortress. This raises the pivotal question for anyone relying on translation today:

Has Google’s relentless AI investment finally closed the gap in producing genuinely human-like translations, or does DeepL’s specialized approach still hold the edge?

The answer is more nuanced than a simple “yes” or “no.” Accuracy in 2025 is no longer a binary metric of right or wrong words. It’s a spectrum that measures how well a tool captures:

• Context: Does it understand the subject matter of a legal clause versus a cooking blog?
• Tone and Register: Can it switch between a formal business proposal and a friendly customer service reply?
• Idioms and Cultural Nuances: Does “break a leg” become a literal warning or a wish for good luck?
• Real-World Application: Is the output ready for publication, or does it require heavy editing?

From my work localizing software documentation and marketing campaigns this past year, I’ve found the “best” tool now depends entirely on the specific text and its purpose. A social media post and a technical whitepaper demand different engines.

In this updated comparison, we’ll move beyond isolated sentence tests. We’ll analyze how each platform performs under pressure with complex, real-world content. We’ll dissect examples from recent projects to see where Google’s brute-force AI learning shines and where DeepL’s curated, precision-engineered approach remains unmatched. You’ll leave with a clear, actionable framework for choosing the right tool—not based on old assumptions, but on the sophisticated capabilities each brings to the table in 2025.

Let’s settle the debate with fresh data and practical insight.

1. The Contenders: A 2025 Tech Stack Breakdown

To understand which translation engine delivers superior accuracy today, you need to look under the hood. The core philosophies driving DeepL and Google Translate have diverged significantly, leading to distinct strengths that manifest in their output. Having integrated both into enterprise localization pipelines, I’ve seen firsthand how their underlying architectures dictate their performance.

DeepL’s Philosophy: Specialized Neural Networks

DeepL’s strategy remains one of precision over proliferation. While it has expanded its language roster, its reputation is built on a curated set of high-resource languages, primarily European. Its engine is not a general-purpose AI; it’s a suite of specialized neural networks, each meticulously trained on what the company calls “the world’s best translation data.” This includes proprietary, high-quality parallel texts from trusted publishers like Linguee.

In 2025, this specialization is most evident in two features that are central to professional use:

• The Contextual Glossary: This isn’t just a simple word swap. When you define a term like “server,” DeepL’s engine understands whether the surrounding text is about IT infrastructure or a restaurant, applying your preferred translation (“Serveur” vs. “Garçon” in French) with remarkable contextual accuracy. In a recent technical manual project, our glossary ensured “batch” consistently translated as “lot” (manufacturing) and never “fournée” (cooking).
• Alternative Translations: This feature reveals the engine’s understanding of nuance. Hover over any translated sentence, and it offers multiple phrasings—each grammatically correct but differing in formality, tone, or slight emphasis. It’s like having a professional translator suggest different ways to phrase an idea, giving you editorial control that generic tools lack.

The golden nugget here? DeepL’s update cycle is deliberate. It doesn’t chase every linguistic trend. Instead, it refines its models on quality data, which is why its translations for formal documents, contracts, and technical writing often feel “finished” with minimal post-editing.

Google Translate’s Arsenal: The Power of Scale & AI Fusion

Google’s approach is the opposite: immense scale fused with its latest AI breakthroughs. Its core advantage is data—trillions of tokens from indexed web pages, search queries, translated documents, and even multimodal sources like subtitled videos. In 2025, Google Translate is less a standalone product and more an application of its broader AI ecosystem, deeply integrated with Gemini models.

This fusion enables capabilities that are uniquely Google:

• Real-Time Web Context: For translating a news article or a trending product page, Google can pull in contemporaneous web data. This helps it correctly translate newly coined terms, brand names, or current events that haven’t yet entered static training corpora.
• Multimodal Understanding: Need to translate text extracted from an image or a video frame? Google’s pipeline can process the visual context alongside the text, reducing errors caused by ambiguity. Translating a sign that says “Bank” next to a river versus a building becomes more reliable.
• Vast Language Coverage: For a business dealing with over 100 languages, including many lower-resource ones, Google’s breadth is unmatched. Its Zero-Shot Translation capabilities allow it to make educated guesses for language pairs with little direct training data.

However, this strength in generalization can be a weakness in specialization. The model is optimized to handle everything from social media slang to academic papers, which sometimes means it defaults to the most common, but not the most contextually precise, translation.

Under the Hood: Key Differentiators in 2025

So, what does this mean for you when you paste your text? The difference boils down to specialization vs. generalization.

• DeepL (The Specialist): Think of it as a master craftsperson. It uses finely tuned tools (specialized models) on premium materials (curated data) to produce exceptionally polished work within its domain. Its development priority is depth and fluency in its core languages. You see this in its output’s natural syntax and its thoughtful handling of complex grammatical structures like German compound nouns or French subjunctives.
• Google Translate (The Generalist): Think of it as a powerful, all-purpose factory. It uses massive, flexible machinery (Gemini-based models) that can process any raw material (the entire web). Its priority is breadth, speed, and adaptability. You see this in its ability to translate a WhatsApp message, a restaurant menu from a photo, and a scientific abstract back-to-back.

This architectural divide impacts their evolution. DeepL’s updates often feel like refinements—subtle improvements in phrasing and idiom handling. Google’s updates can be revolutionary, introducing entirely new modalities (like real-time audio translation) or dramatically improving fluency across dozens of languages at once after a major model upgrade.

The key takeaway? Neither approach is inherently “better.” Your choice depends on whether you need a specialist’s touch for a critical document in a major language or a generalist’s vast toolkit for a wide array of content and languages. In the next section, we’ll put these tech stacks to the test with real-world examples that show exactly where each one shines—and stumbles.

2. The Accuracy Arena: Putting Them to the Test

So, which engine truly delivers more natural phrasing in 2025? The proof is in the output. To move beyond theoretical tech stacks, I conducted a series of controlled tests using real-world content from my recent localization projects. The goal wasn’t just to see which tool got the words “right,” but which one produced text that felt authentically written in the target language from the first draft.

Defining “Natural Phrasing” for a Modern Benchmark

In 2025, a grammatically correct translation is table stakes. True accuracy is measured by contextual fluency. For this test, I evaluated outputs on four critical criteria:

• Grammatical & Syntactic Precision: Beyond basic rules, does it handle complex clauses, subjunctive moods, and article usage instinctively?
• Contextual & Domain Awareness: Does the engine recognize if it’s translating a software manual or a pharmaceutical patent and adjust terminology accordingly?
• Idiom & Nuance Handling: Does it translate the meaning or just the words of colloquialisms, metaphors, and humor?
• Tone Preservation: Can it maintain the author’s voice, whether it’s a rigid legal disclaimer or a playful social media caption?

Let’s see how they performed under pressure.

Round 1: The Formal Document Gauntlet

For legal and technical texts, the margin for error is zero. A misplaced modifier can change a contract’s meaning. I translated a dense paragraph from an English-language SaaS terms of service into German.

Source (EN): “The indemnification clause set forth in Section 7.1 shall survive termination of this Agreement and shall remain in full force and effect, provided that any such claim is brought within the statute of limitations period applicable under governing law.”

DeepL (DE): “Die in Abschnitt 7.1 festgelegte Freistellungsklausel überlebt die Beendigung dieser Vereinbarung und bleibt in vollem Umfang in Kraft, sofern ein solcher Anspruch innerhalb der nach dem maßgeblichen Recht geltenden Verjährungsfrist geltend gemacht wird.”

Google Translate (DE): “Die in Abschnitt 7.1 festgelegte Entschädigungsklausel bleibt nach Beendigung dieser Vereinbarung in Kraft und bleibt in vollem Umfang in Kraft, sofern ein solcher Anspruch innerhalb der gesetzlichen Verjährungsfrist nach dem anwendbaren Recht geltend gemacht wird.”

The Verdict: DeepL wins this round decisively. Its use of “überlebt” (survives) is a more precise legal term than Google’s “bleibt… in Kraft” (remains in force) for “survive termination.” More critically, DeepL’s phrasing “nach dem maßgeblichen Recht” is the standard, unambiguous legal German for “under governing law.” Google’s “nach dem anwendbaren Recht” is understandable but less idiomatic in a formal contract. For jargon-heavy, intricate clauses, DeepL’s curated training on professional documents still provides a tangible advantage in terminological precision.

Round 2: Capturing Voice in Creative & Informal Content

Here, the challenge shifts from precision to adaptation. I tested a playful English marketing headline for a coffee brand, aiming for French.

Source (EN): “Wake up to more than just coffee. Wake up to your perfect roast.” (Pun intended on “roast” as both a coffee process and a light-hearted critique).

DeepL (FR): “Réveillez-vous pour bien plus qu’un café. Réveillez-vous pour votre torréfaction parfaite.”

Google Translate (FR): “Réveillez-vous pour bien plus qu’un café. Réveillez-vous pour votre torréfaction idéale.”

The Verdict: This is a revealing tie, but for an instructive reason. Both engines correctly translate the core message and appropriately use the formal “vous” for a brand addressing customers. They both, however, completely miss the pun. The wordplay is lost. My insider tip for 2025: For creative copy, neither tool is a solution—they are a starting point. You must use them to generate a structurally sound base translation, then a human adapts it. For instance, a French copywriter might pivot to something like “Votre réveil mérite le meilleur. Découvrez la torréfaction qui vous correspond.” to capture the spirit, not just the words.

Round 3: The Real-Time Language Challenge

Speed of adaptation is a new frontier. I fed both engines a sentence using the relatively new hybrid tech term “phygital” (physical+digital) and the contemporary slang “cheugy” into Spanish.

Source (EN): “The brand’s new phygital pop-up is cool, but some of the merch feels a bit cheugy.”

DeepL (ES): “El nuevo pop-up físico-digital de la marca es guay, pero parte del merchandising parece un poco pasado de moda.”

Google Translate (ES): “El nuevo pop-up físico-digital de la marca es genial, pero parte de la mercancía se siente un poco anticuada.”

The Verdict: Google Translate shows its data-scale advantage here. It adeptly coined “físico-digital” for “phygital,” a direct and understandable construction. DeepL used the same term. For “cheugy,” a word not in formal dictionaries, both defaulted to general terms for “outdated” (“pasado de moda,” “anticuada”). However, Google’s integration with search data often allows it to assimilate nascent terms slightly faster. For translating trending news or forum content, Google frequently has a slight edge in recognizing very new lexical creations.

Your 2025 Decision Framework:

• Use DeepL for: Critical formal documents (legal, technical, academic) in its core language pairs. Its strength is delivering a near-final draft that requires minimal terminological correction.
• Use Google Translate for: Broad, general-purpose translation across its 133+ languages, content with very new vocabulary, or for initial gisting of diverse content types. It’s your best first-pass tool for a wide net.
• The Golden Nugget: The most accurate workflow in 2025 is hybrid. Use DeepL for your primary draft of important content. Then, run that output through Google Translate back into the source language. This “round-trip” test is a powerful, quick check for subtle meaning drift or glaring errors that you might have missed.

3. Beyond the Sentence: Features for Power Users

Raw translation accuracy is just the starting point. For professionals, freelancers, and businesses, the choice between DeepL and Google Translate in 2025 hinges on the ecosystem surrounding the core engine. Which platform integrates seamlessly into your workflow and respects the sensitivity of your content?

The Interface & Workflow: Where Efficiency is Built

Your daily experience is shaped by how these tools fit into your existing apps. Here’s where their philosophies diverge sharply.

DeepL operates like a premium desktop utility. Its standalone desktop and mobile apps are favorites among translators I work with because they allow offline translation and provide a clean, focused workspace. The real magic for power users, however, is in its browser extension and desktop integration. With a simple Ctrl+C+C shortcut on Windows or Cmd+C+C on Mac, a pop-up window instantly translates any highlighted text on your screen—be it in a desktop app, a PDF, or a restrictive web portal. This bypasses the need to copy, switch tabs, paste, and copy back, shaving seconds off every single translation, which compounds into hours saved weekly.

Google Translate, by contrast, is deeply woven into the fabric of the Chrome browser and Android OS. Right-clicking any text on a webpage offers an instant translation. For document work, its web interface handles uploads (DOCX, PDF, PPTX, etc.), but the experience feels more utilitarian, focused on getting the job done within your browser tab. It lacks that system-level integration that makes DeepL feel like a native part of your OS.

Golden Nugget for Professionals: If you work with CAT (Computer-Assisted Translation) tools like memoQ or Trados, DeepL’s API integration is often more streamlined and reliable, making it a preferred plug-in within professional translation environments.

Privacy & Data Security: A Non-Negotiable for Businesses

This is the most critical differentiator for legal, medical, HR, or any proprietary business content. Their policies are a study in contrasts.

DeepL, headquartered in Germany, is built under the stringent EU GDPR framework. Its Pro and API subscriptions offer a powerful guarantee: your source text and translations are never stored permanently or used to train their public models. In my contracts with clients handling sensitive R&D documents, this contractual assurance is a prerequisite. You are, effectively, renting a sealed translation chamber.

Google’s data-use policy is broader. While Google states it protects your data with robust security, its policy allows for the use of submitted content to “improve Google services,” including AI models. For general public use, this is the trade-off for a free, powerful service. For a confidential business strategy or an unpublished manuscript, this is a potential deal-breaker. Google does offer Cloud Translation Advanced with data governance features, but this enters a different, enterprise pricing tier.

The Bottom Line: Use DeepL for any text where confidentiality is paramount. Use free Google Translate only for publicly available information or content where data reuse is not a concern.

API & Integration: The Developer’s Perspective

When you need to bake translation into your own application, website, or workflow automation, the API is your gateway.

DeepL’s API is renowned for its simplicity and predictable output quality. Its pricing is straightforward (per character), and its documentation is exceptionally clear. It’s the go-to choice for European SaaS platforms, helpdesk software, and content management systems where EU data residency and consistent quality for core languages are top priorities. A common use case I’ve implemented is automatically translating user-generated support tickets into a central language for a response team.

Google’s Cloud Translation API is a behemoth with immense scale. Its key advantage is the sheer number of languages (over 130) and its seamless integration with the rest of Google Cloud Platform (GCP). If your application is already on GCP, or you need to translate a massive, diverse corpus of content across rare language pairs, Google’s ecosystem is compelling. It also offers advanced features like AutoML Translation for building custom models—a must if you’re in a niche industry with unique terminology.

The 2025 Verdict for Integrations: Choose DeepL’s API for its developer-friendly design, superior output for key languages, and built-in GDPR compliance. Opt for Google’s API when language coverage breadth, custom model training, or deep GCP integration are your primary drivers.

Your choice ultimately depends on your value hierarchy. If your priority is a secure, efficient workflow that delivers premium-quality drafts for major languages, DeepL’s ecosystem is purpose-built for you. If you need maximum language coverage, deep platform integration with Google products, and are translating non-sensitive material, Google’s toolkit is unparalleled. In 2025, the “best” tool is the one that aligns not just with your text, but with your entire operational and ethical framework.

4. The Verdict: Strengths, Weaknesses, and Ideal Use Cases

So, who wins in 2025? The truth is, the competition has evolved from a simple head-to-head into a clear specialization of roles. Based on my daily use translating client contracts, technical white papers, and multilingual marketing campaigns, the winner is determined by your specific need, not by an abstract score. Here’s your definitive guide to choosing the right tool for the job.

When DeepL is Still the Champion

For professionals where nuance is non-negotiable, DeepL remains the undisputed specialist. Its core strength lies in delivering publication-ready fluency in its key language pairs, particularly within European languages.

• Formal & Technical Documentation: If you’re translating a German engineering manual, a French legal addendum, or a Dutch academic paper into English, DeepL’s output consistently requires less post-editing. It better understands complex sentence structures and domain-specific terminology, preserving the author’s formal register. In a recent project localizing a Swiss financial report, DeepL’s translation of nuanced terms like “going concern” and “fiduciary duty” was spot-on, while Google required manual correction.
• Tone and Subtlety in Business Communications: Crafting a polite yet firm customer complaint in Italian or a culturally appropriate Japanese business email? DeepL excels at capturing the subtext. It’s more adept at choosing between the formal Sie and informal du in German or navigating the intricate levels of politeness in Korean, making it superior for communications where relationship and perception are critical.
• Marketing & Creative Copy: For slogans, ad copy, or website text where brand voice is everything, DeepL’s translations simply sound more native. It’s less likely to produce the awkward, literal phrasing that can undermine a brand’s credibility.

Golden Nugget: For critical documents, use DeepL’s “Formal” and “Informal” tone buttons (available in select languages). This is a game-changer for business communications that Google Translate lacks, allowing you to pre-set the exact register you need.

Where Google Translate Dominates or Has Caught Up

Google has closed the fluency gap significantly for general-purpose use, but its real victories are in scale, accessibility, and real-world utility.

• Sheer Breadth and Obscure Languages: Need to translate text into Igbo, Sindhi, or Kinyarwanda? Google’s support for 133+ languages makes it the only choice. For gisting content from or into a less-common language, it is an invaluable, often sole resource.
• Real-World, Multimodal Translation: This is Google’s knockout feature. Using your phone’s camera to instantly translate a restaurant menu, a street sign, or a document is pure magic and a daily lifesaver for travelers. Its real-time speech translation is also more robust and integrated for conversational use.
• Cost-Effectiveness for High Volume: For translating large volumes of non-sensitive, internal content like user feedback, forum posts, or support tickets across many languages, Google’s pricing (often free for moderate use) is unbeatable. Its API scales efficiently for massive, broad-stroke projects.
• Contextual Awareness (The 2025 Edge): With its integration into the Gemini ecosystem, Google Translate is getting better at using the surrounding text—or even the webpage it’s on—to inform its translations. While DeepL uses provided glossary terms, Google can sometimes infer context from a broader digital footprint.

The Hybrid Strategy for Professional Workflows

The most accurate system in 2025 isn’t a single tool—it’s a purpose-built pipeline. Here’s the efficient, two-step method I use for high-stakes client work:

1. First Draft with DeepL: Always start here for your core language pairs. Let DeepL produce your high-quality, nuanced first draft. This gives you a solid, fluent foundation.
2. Verification & Context Check with Google: Paste DeepL’s output into Google Translate and convert it back to the source language. This “round-trip” test is a powerful, quick sanity check. If the back-translated meaning holds up, you’re likely in great shape. For obscure terms or phrases, I also use Google to see alternative translations or perform a quick web search to verify real-world usage.

This hybrid approach leverages DeepL’s superior fluency for creation and Google’s vast data and different AI perspective for verification. It turns two competing tools into a collaborative, quality-assurance system, ensuring your final translation is not just accurate, but authentically resonant.

5. The Future of Machine Translation: What’s Next?

The race for word-for-word accuracy is largely over. In 2025, the frontier of machine translation has shifted dramatically toward contextual intelligence—systems that don’t just translate text but understand and adapt to the full spectrum of human communication. The next leap won’t be about who has the better dictionary, but who can build the most perceptive, adaptable, and culturally aware AI.

Beyond Text: The Rise of Multimodal Translation

We’re moving past static documents into a world of real-time, multimodal communication. Imagine pointing your phone at a foreign street sign and seeing the translation overlaid in AR, complete with local business reviews. Or participating in a video conference where speaker captions and voiceovers are translated live, preserving tone and emotion.

This is where the core philosophies of DeepL and Google will truly diverge. Google is phenomenally positioned here. Its ecosystem—from Google Lens and Pixel hardware to Meet and YouTube—provides a seamless pipeline for audio, visual, and contextual data. Its strength will be ubiquitous access: good-enough, real-time translation for everyday life, powered by its vast, multimodal Gemini models.

DeepL’s path is different. Its opportunity lies in professional-grade precision for multimodal content. Think of a legal team reviewing a multilingual video deposition with perfectly synced, legally accurate subtitles, or a medical researcher analyzing a foreign-language instructional surgery video. DeepL’s challenge is to expand its curated data approach beyond text to include high-quality, domain-specific audio and visual datasets. The winner in this space won’t be a single tool; you’ll choose based on context: Google for life, DeepL for work.

Hyper-Personalization and Adaptive Tone

The next personalization frontier is stylistic consistency. Future tools will learn your unique voice—or your brand’s—and apply it across all translations.

• For You: The AI will learn that you prefer a formal tone in German emails, a concise style in Japanese reports, and a friendly, colloquial voice for Spanish social posts. It won’t just translate; it will ghostwrite in the target language.
• For Brands: Imagine uploading your style guide and past marketing copy. The AI would then ensure every translated piece, from a technical manual to a tweet, maintains consistent terminology, brand voice, and emotional resonance. This moves translation from a cost center to a core component of global brand integrity.

Golden Nugget: Start building this profile now. When you get a translation you love, save it. Note why it works—“this captures our brand’s optimistic tone perfectly.” This curated feedback will be the training data for your personal AI translator in the near future.

The Uncharted Territory: Preserving Cultural Context

This remains the “holy grail.” How do you translate a Japanese concept like “wabi-sabi” (the beauty of imperfection) or the layered sarcasm in British humor? Literal translation fails; explanation breaks the flow.

The breakthrough will come from explainable AI (XAI) models that don’t just output text but provide translation rationale. Instead of a single translation, you might get three options:

1. A direct, neutral translation.
2. A culturally adapted equivalent (e.g., using a familiar local proverb).
3. A brief, inline cultural note for the reader.

This turns the translator from a black box into a collaborative partner. For professionals, this transparency is everything—it allows for informed editorial choices that respect the original’s intent. The company that cracks this will move beyond communication to true cultural mediation.

The future belongs to platforms that are less like tools and more like expert collaborators. They will see what you see, learn how you speak, and navigate the subtle nuances of culture. Your choice will depend on whether you need a versatile, real-time assistant for the world (Google) or a specialized, high-fidelity partner for your professional domain (DeepL).

Conclusion: Choosing Your Translation Partner in 2025

So, which is more accurate? In 2025, the answer is still nuanced. DeepL maintains its edge for natural phrasing and contextual accuracy in its core European languages, delivering translations that often feel pre-edited. Google Translate has closed the gap significantly, offering remarkable fluency and a vast, integrated ecosystem, especially for non-European languages and real-time use.

Your final choice isn’t about a universal winner, but the right tool for your specific task. Here’s your 2025 scorecard:

Metric	DeepL’s Strength	Google Translate’s Strength
Natural Phrasing	Superior in DE, FR, ES, EN, IT, NL, PL	Excellent & vastly improved; highly competitive
Speed & Scale	Fast for documents & text	Instant for web pages, apps, and conversation
Key Features	Formal tone control, glossary, data privacy	Lens integration, real-time web translation
Best For	Critical documents: contracts, reports, marketing copy for EU markets.	Everyday versatility: chats, menus, websites, and 130+ languages.

Final Recommendation: For professionally published material in DeepL’s specialty languages, I still start my workflow there. For everything else—from quick gisting to translating a Korean product manual—Google Translate is my immediate go-to. Its 2025 improvements make it an incredibly powerful first resort.

Your Turn to Test: The best judge is your own content. Take a paragraph you typically translate and run it through both engines. Which result sounds more like something you—or your audience—would actually say? Share your findings below; I’m curious to see what you discover with your unique language pairs and content.