Youâre here because you want a clear winner: will it be ChatGPT or machine translation?
Prompt it well and you will see that ChatGPT can write like a pro. It nails tone, context, even humor. But ask it to translate a product string (or hundreds of them), things quickly fall apart. It starts guessing, making things up, or skipping over context completely. Thatâs what LLMs are prone to doing.
Machine translation engines, on the other hand, are built for structure. They follow rules, stick to glossaries, and respect segmentation. Theyâre not perfect, but they are predictable. Letâs see if predictableâs good enough and where ChatGPT can actually be of use.
đ A realistic look at ChatGPT vs. machine translation dilemma
This article breaks down the real differences between generative AI and neural machine translation (NMT), explains why neither is a silver bullet, and shows how you can get the best of both. Weâll cover when to use ChatGPT, when to rely on NMT, and where machine translation post editing (MTPE) fits in. Letâs see how you can scale translation without sacrificing quality.
Why ChatGPT struggles with translation at scale
ChatGPT is great at sounding human. It generates fluent, natural-sounding text that feels like it was written by a person. But under the hood, it works very differently from machine translation engines.
How do LLMs in localization work
Large language models (LLMs) like ChatGPT are trained to predict the most likely next word in a sentence. Thatâs useful for creating context-rich content, but it comes with serious limitations in localization workflows.
LLMs donât follow structured rules, they donât respect segmentation, and they donât reliably use translation memory or glossaries.
In other words, what you gain in creativity, you lose in consistency.
Thatâs why AI translation quality drops when you try to scale with ChatGPT. Product copy, legal disclaimers, or help articles need exact matches and zero room for improvisation. Something LLMs simply arenât designed to do.
They prioritize consistency over creativity, making them far more reliable for technical content
So while ChatGPT sounds smoother, NMT plays by the rules. In localization, thatâs often exactly what you need.
Can you still trust ChatGPT for translations?
As we explained, ChatGPT doesnât âtranslateâ in the way a machine translation engine does. It predicts.
Specifically, it uses probability to guess what the next word should be based on the input it receives and its massive training data. That makes it great for free-form generation. However, itâs risky for structured content.
Thatâs why LLMs are prone to:
Hallucination: Making up terms, phrases, or even entire sentences that sound correct but arenât
Omitting context: Skipping tags, placeholders, or gendered forms, especially if theyâre not frequent in training data
Inconsistency: Translating the same term differently across files or locales, especially in longer content
Overriding instructions: Ignoring glossaries, translation memories, or brand guidelines (unless fine-tuned or heavily prompted)
Thatâs just how LLMs function. They are optimized for fluency, not for compliance or consistency. Itâs what makes them powerful in creative use cases and risky in localization workflows that require precision and repetition.
đ Can you trust Google Translate and DeepL for translations?
You probably used Google Translate for personal needs, but have you used it for large-scale translation projects? Can it be trusted? Take a look at our analysis of Google Translate vs. DeepL to learn more about opportunities and limitations of these tools.
Testing ChatGPT, Google Translate, and DeepL for poem translation
A 2025 study found that when ChatGPT was prompted with minimal instruction (e.g., âTranslate the following text into [target language] creativelyâ) it outperformed commercial NMT engines (including DeepL) on creative translation tasks.
Letâs test this to paint a clearer picture. Weâll use ChatGPT, Google Translate, and DeepL for a poem translation.
Iâll pick a segment of the poem from my favorite childrenâs book âHedgehogâs Homeâ, written in Serbian. The task is simple: translate the verses from Serbian to English.
Letâs take a look at ChatGPT translation first:
This actually isnât bad. Itâs quite impressive that ChatGPT managed to translate this while preserving the end rhyme and without changing the meaning too much.
But it is still subpar when compared to a human translation. Hereâs an example of one coming from the University of Belgrade:
As you will see, the translator made interesting choices and even decided to localize the name of the hedgehog quite cleverly.
âJeĹžurka JeĹžiÄâ in Serbian gets translated to âHenry the Hedgehogâ in English, where the sentiment, tone, and the stylistic alliteration have been respected.
Letâs see Google Translate now.
As you can see, itâs not as good. Feels a bit clunky and robotic, and you definitely wouldnât guess itâs a poem. This proves that ChatGPT indeed handles creativity and fluency better than machine translations. Letâs move on to DeepL.
Whoops! Looks like Serbian isnât a supported language on DeepL.
This brings me to another point: you need to think about the actual languages that are supported, not just the quality youâd get from different tools.
While thereâs no official, fullyâpublished exhaustive list of all language pairs for translation for ChatGPT, several independent sources say it can handle input and output in 80+ to 95+ languages.
Bear in mind that, because ChatGPT is a generalâpurpose LLM, the notion of âsource languageâ is less formally defined than dedicated translation tools. It may accept input in many languages but isnât optimized or guaranteed for translation in all of them.
Coverage doesnât guarantee translation quality or consistency across all languages.
When to use ChatGPT vs. machine translation
Thereâs no universal winner in the ChatGPT vs. machine translation debate. It depends on the job. Plus, there are many different types of machine translation and their output can vary.
Each tool has its strengths, and choosing the right one comes down to content type, consistency needs, and your tolerance for risk.
You can use ChatGPT when:
Fluency matters more than precision: ChatGPT shines in content that needs to sound natural, human, and nuanced. Think marketing copy, product descriptions, or brand storytelling.
You need fast first drafts: Itâs great for early-stage content generation, especially when youâre exploring tone or experimenting with phrasing across different languages.
Youâre working with short-form creative content: Social captions, emails, internal comms, ad copy; this is where ChatGPT can deliver contextual flair, fast.
â Important note
LLMs like ChatGPT arenât plug-and-play for localization. To get usable translations, youâll need to invest time into prompt engineering, context injection, and often manual post-editing.
That might work for a few strings or a one-off task. But in reality, these efforts donât scale. As volume grows, so does the risk of inconsistency, hallucinations, and missed formatting.
If you want reliable output at scale, youâll need a system built for it. Clever prompting wonât cut it.
Curious to learn more? Read about AI localization and workflows.
Use machine translation when:
Consistency is non-negotiable: Product UIs, support articles, legal disclaimers all require structured, repeatable output that follows TM and glossaries.
You need to scale: MT engines like DeepL or Google Translate are built to handle high-volume content with predictable performance.
You want to integrate into workflows: MT is easier to plug into localization pipelines. It supports segmentation, metadata, and automation by design.
You can see that ChatGPT performs nicely if you care about fluency. Machine translation is better for control and precision, provided you give it context.
But letâs make this information more actionable by looking at different use cases.
[CHEAT SHEET] ChatGPT vs. machine translation, based on the use case
This cheat sheet breaks down the strengths and weaknesses of ChatGPT and machine translation, so you can pick the right engine for every content type.
Use case
ChatGPT (LLM)
Machine translation (NMT)
Tone and fluency
LLMs like GPT-4 produce natural-sounding, context-rich output
MT tends to be literal and sometimes awkward (depends on how much context you feed it)
Consistency
Not guaranteed, unless fine-tuned or prompted carefully
MT uses TMs, glossaries, enforced terminology
Creative copy
LLMs are a good choice here because of high fluency and adaptive tone
MT lacks nuance, often too formal or bland (again, depends on the context you share)
Technical or legal content
LLMs may hallucinate, miss context, or omit placeholders
MT is rule-based and structured
Scaling large volumes
Each LLM output needs more QA (harder to scale reliably)
MT is fast, consistent, and scalable
UI strings and placeholders
LLMs may ignore tags or rephrase content unexpectedly
MT handles segmentation, metadata, placeholders
Workflow integration
Needs prompting logic or API calls (not built-in; itâs not purpose-built for translations)
Often heavy, unless you pre-load all context and constraints
Varies by engine (usually lighter compared to ChatGPT)
The sweet spot would be a platform that lets you use both, intelligently. One that routes the right engine to the right content type and adds machine translation post-editing (MTPE) when needed.
Before we dive into that deeper, letâs take a look at why machine translation post-editing matters.
Why MTPE matters
Raw machine translation gets you speed, but with questionable quality. Thatâs where machine translation post-editing comes in.
MTPE is the step that turns âgood enoughâ into âready to publish.â After the machine translation engine does its job, a human linguist reviews and edits the output to ensure itâs accurate, consistent, and aligned with your brand voice.
Hereâs what MTPE helps fix:
Terminology inconsistencies
Missing or incorrect tags and placeholders
Awkward phrasing or unnatural flow
Tone mismatches across languages
Critical translation errors in legal or technical content
Itâs faster than translating from scratch and far more reliable than using raw MT or ChatGPT alone.
đ§ Did you know?
Localization platforms like Lokalise lets you apply MTPE selectively, assigning post-editors only where it matters most, and skipping it for low-priority or internal content. This is the middle ground between automation and control, and itâs how the best localization teams hit both speed and quality targets.
How Lokalise helps you get the best of both worlds
You donât need to pick a side in the âChatGPT vs.âŻmachine translationâ battle. What you do need is a reliable system that knows which engine to use, when, and how to monitor quality, without giving up control.
Thatâs exactly what Lokalise delivers. Hereâs how it works:
It enriches translation with your own context (translation memories (TM), glossaries, style guides) so the output is onâbrand, accurate and scalable
It monitors quality using translation scoring, flags content that needs human review and automates the rest (this means only ~20âŻ% of content may need postâediting)
It supports every workflow (e.g., mobile apps, webâUI strings, documentation, marketing) all in one centralized hub with enterpriseâgrade security
Youâll end up with better translation quality, faster global launches ,and predictable costs, but without sacrificing brand voice or risking inconsistency. In other words, you get speed and quality.
Mia has 13+ years of experience in content & growth marketing in B2B SaaS. During her career, she has carried out brand awareness campaigns, led product launches and industry-specific campaigns, and conducted and documented demand generation experiments. She spent years working in the localization and translation industry.
In 2021 & 2024, Mia was selected as one of the judges for the INMA Global Media Awards thanks to her experience in native advertising. She also works as a mentor on GrowthMentor, a learning platform that gathers the world's top 3% of startup and marketing mentors.
Earning a Master's Degree in Comparative Literature helped Mia understand stories and humans better, think unconventionally, and become a really good, one-of-a-kind marketer. In her free time, she loves studying art, reading, travelling, and writing. She is currently finding her way in the EdTech industry.
Miaâs work has been published on Adweek, Forbes, The Next Web, What's New in Publishing, Publishing Executive, State of Digital Publishing, Instrumentl, Netokracija, Lokalise, Pleo.io, and other websites.
Mia has 13+ years of experience in content & growth marketing in B2B SaaS. During her career, she has carried out brand awareness campaigns, led product launches and industry-specific campaigns, and conducted and documented demand generation experiments. She spent years working in the localization and translation industry.
In 2021 & 2024, Mia was selected as one of the judges for the INMA Global Media Awards thanks to her experience in native advertising. She also works as a mentor on GrowthMentor, a learning platform that gathers the world's top 3% of startup and marketing mentors.
Earning a Master's Degree in Comparative Literature helped Mia understand stories and humans better, think unconventionally, and become a really good, one-of-a-kind marketer. In her free time, she loves studying art, reading, travelling, and writing. She is currently finding her way in the EdTech industry.
Miaâs work has been published on Adweek, Forbes, The Next Web, What's New in Publishing, Publishing Executive, State of Digital Publishing, Instrumentl, Netokracija, Lokalise, Pleo.io, and other websites.
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