Can AI translation tools really replace human translators? Not quite, but they can still save you tons of time and effort.
At Lokalise, we tested the top ten AI translation tools to find out which ones are worth your attention. Whether you need quick document translations, website localization, or real-time multilingual support, we’ve got you covered.
✅ We tested the best AI translation tools for 2026
This guide compares the best AI translation tools for 2026. We tested top options from general-purpose LLMs (like ChatGPT and Claude) to specialized translation engines (like DeepL and Google Translate) and localization platforms built for repeatable workflows. You’ll see what each tool is actually good at and what to pick based on the job.
Let’s first share more about our scoring system and the way we evaluated AI translation tools.
| Scoring explained | |
| Critical (1) | Accuracy is very poor (translations may have significant impact and cause severe implications) |
| Major (2) | Issues with accuracy can cause a considerable impact and may confuse or mislead the reader |
| Minor (3) | Slight issues with accuracy (does not cause loss of meaning nor confuse the reader) |
| Neutral (4) | Translations are rather accurate, but there are some changes needed (e.g., preferred stylistic changes) |
| Kudos (5) | Exceptional work, near human-like translations |
Lokalise is the best proprietary AI orchestration for context-aware localization because it uses a RAG-powered architecture to ground LLM output in your translation memory, glossary, and style rules, ensuring consistent, governed translations across every release.
Lokalise’s AI capabilities automate context-aware localization by translating in bulk inside the platform. It is the best AI translation tool that applies your translation memory, glossary, and style rules to keep output consistent across releases.
To use Lokalise AI orchestration, you need to:
Once you do that, you’ll be able to generate translations in bulk.
Under the hood, Lokalise uses a RAG approach to retrieve your translation memory, glossary, and style guidance at runtime and feed it into the AI. This is how output stays consistent across releases.
| Pros | Cons |
| - One of the most accurate AI translation tools - Supports 30 languages and their variations - Translates in bulk in as many languages as you need - Option to add context: style guide, content placement, tone of voice - One-click shorten, rephrase, and SEO optimization features - Style guide, translation memory, and glossary available - Built into a TMS (translation system) for seamless localization management - Translation volume discounts to accommodate expanding requirements - 55 integrations with popular CMS tools, Github, Figma, and more | - You need to sign up for the TMS to use Lokalise AI (great option if you also need a TMS) - Only powered by one large language model, OpenAI (more coming soon for even more accurate translation between language pairs) |
🤖 What is Lokalise CLI v2?
Lokalise CLI v2 is a command-line interface that connects your codebase to Lokalise, allowing developers to push source files and pull translated content programmatically. It enables AI-assisted translation and localization workflows to run inside CI/CD instead of manual copy-paste.
Think of the CLI as the bridge between your codebase and Lokalise’s AI orchestration + governance layer: developers automate content exchange, while Lokalise applies context (TM, glossary, style rules) and workflows (review/QA) before anything ships.
Watch this tour to see how Lokalise AI orchestration works.
ChatGPT generates fast, generic translations from a prompt, but it doesn’t enforce TM, glossary, or workflow controls. This is how consistency debt builds across releases.
ChatGPT performs generic text translation by turning pasted input into a target language quickly. It doesn’t enforce localization context like translation memory (TM), which can create inconsistency debt across teams and releases.
To use ChatGPT for AI translation, paste the text you want to translate, specify the target locale (and tone/register), and ask for 1-2 alternatives if you need options.
For repeatable results, use a Custom GPT with fixed instructions, just remember this still won’t behave like a TM-backed localization workflow unless you connect it to one.
🧠 Good to know
A GPT is a large language model that can translate text because it’s trained to predict and generate natural language. It can rewrite your sentence in another language when you tell it what you want.
A Custom GPT is a version of ChatGPT that you configure with fixed instructions and reference materials, so it follows the same translation rules (tone, terminology preferences, formatting guidance) every time instead of relying on a new prompt for each task.
For example, a company could create a GPT that specializes in translating e-commerce product descriptions from English to French, keeping the brand voice consistent.
Here’s the prompt we used to translate an email into each language:
You are a native [Language] speaker and translator. Translate this into [Target language]: [add copy here].
Once you’ve got the hang of it, ChatGPT is easy to use and incredibly quick.
| Pros | Cons |
| - Covers 50+ languages - Super quick translationsAPI available - Easy to use | - Average for accuracy - No integrations - No option to upload a glossary or style guide with one click - Data used to train models if not using API - Manual process of copying and pasting copy to be translated if not using API |
ChatGPT can be used inside Lokalise through its AI orchestration layer. Instead of prompting it manually, Lokalise routes content to the best-performing model (like ChatGPT), applies your translation memory, glossary, and style rules, and runs QA checks before translations are approved and shipped.
Claude translates from prompts quickly and is great for rewrites, but without TM/glossary enforcement it can’t maintain consistent terminology at scale.
Claude performs generic text translation by generating a target-language version of your input from a prompt. It’s useful for fast drafts and rewrites, but it doesn’t natively enforce translation memory (TM), glossary rules, or localization workflows.
💡 Pro tip
If you need translations fast without breaking the bank, Claude is definitely worth a look, but make sure you always have a machine translation post-editing (MTPE) process in place.
To use Claude for AI translations, follow these steps:
Lokalise can “call” Claude through its AI orchestration layer, so you don’t have to run translations in a separate chat. Claude generates the first pass, then Lokalise applies TM/glossary/style rules and moves the output through QA and review before it ships.
Taia combines AI translation with optional human editing, but it’s more manual and lacks automation for continuous localization.
Taia is a simple AI translation tool that lets teams translate documents and web content, with optional human review/editing when higher accuracy and polish are required.
To use Taia for AI translations, create a new project inside the platform. Then upload your content (or paste text), choose the target languages, select the service level (AI-only or AI + human editing), and submit. Taia’s setup wizard guides you through the required steps in the browser.
You can only upload files or copy and paste text, but there’s no option to integrate your CMS or repository to automate content exchange. However, there is an API.
Taia supports 68 different file formats and you can tell the AI what type of content you need to translate for better translation quality. There are five types of translation you can choose from, depending on the quality you need. For this article comparing tools without human assistance, we used the AI-only option.Pros & cons of Taia as an AI translation tool
| Pros | Cons |
| - Machine-assisted + professional editing option is also available - Covers 97 languages - Instant rate estimates - Long-term project support | - No integrations - No generative AI capabilities - No option to add a glossary and style guide - Slow turnaround times - Manual process of copying and pasting text - Scored one of the worst in terms of accuracy |
📚 Further reading
Check out our guide to the best translation software on the market.
TextUnited pairs machine translation with translation memory and terminology tools to improve consistency over time.
TextUnited is a translation and localization platform that pairs machine translation with built-in translation memory and terminology management, helping teams keep multilingual content more consistent over time.
To use TextUnited for AI translation, you should:
You will receive a notification in the platform (and email) once translations are ready.
| Pros | Cons |
| - One of the most accurate - Free trial for 14 days - Integrations available - Fast translation turnaround times - Option to deploy as a headless translation system via an API | - The translation is often too formal - No generative AI capabilities - No option to add a style guide for on-brand translations |
Want to learn more? Read this jargon-free guide on how AI translation works.
Copy.ai can generate translated content through prompts and templates, but it doesn’t provide TM enforcement or structured localization governance.
Copy.ai is a generative AI platform built for creating and optimizing marketing copy that’s sometimes used for translations. While AI translation isn’t its core focus, it can still generate target-language versions of content through its prompt-based workflows and templates.
While AI translation isn’t its core focus, it can still generate target-language versions of content through its prompt-based workflows and templates.
To use Copy.ai as a translation tool, enter a prompt requesting translation, specify your target locales (including regional variants like Brazilian Portuguese or Chilean Spanish), add brand voice guidance if you have it, then generate translations and review the output before reusing the same instructions for the next batch.
You will see it has a similar interface to ChatGPT, which makes it very easy to use.
| Pros | Cons |
| - One of the most accurateTranslates into 95+ languages - Option to upload brand voice guide - User-friendly, GPT-style interface - Workflow builder to connect your tools | - No option to add a glossary - No translation memory - No human translation option built-in - No direct integrations (you can use Zapier) |
DeepL delivers natural-sounding machine translations, but it runs mainly as a standalone engine without end-to-end localization workflow control.
DeepL is a neural machine translation engine that generates fluent, natural-sounding translations for text and documents. It operates primarily as a standalone translator without built-in translation memory governance or end-to-end localization workflow controls.
Compared to Google Translate, DeepL is often preferred because it goes beyond word-for-word translations. It captures the context and nuances of the original text, making translations feel more natural and fluent.
With a free plan, a familiar interface, seamless integrations with Windows and iOS, and a Chrome plugin, DeepL is accessible to everyone. There’s the option to add a glossary, allowing you to set rules for how words and phrases are translated. However, the number of glossaries and entries is limited on the free plan.
DeepL also has an easy-to-use, real-time online document translator. You can translate your PDFs, Word Docs, PPTs, and more with a single click while preserving their original format.
| Pros | Cons |
| - Lifetime free plan - API and Chrome plugins are available - Supports translation in 31 languages - Maintains formatting of the original document | - Scored one of the lowest in terms of accuracy - No integrations with tools - No option to upload your style guide or brand voice - No translation memory - Manual process of copying and pasting text to be translated |
Lokalise can use DeepL as a translation engine inside your localization workflow. You send content from your repo/CMS to Lokalise, run DeepL for the first pass, then review and ship with TM, glossary, and QA checks applied in one place.
Google Translate provides instant neural machine translation across many languages, but it doesn’t preserve terminology or enforce long-term consisten
Google Translate is a neural machine translation engine that delivers instant translations across 100+ languages, making it useful for quick, low-risk text conversion. However, it does not include built-in translation memory, glossary governance, or structured localization workflows for maintaining long-term consistency.
A while ago, we did extensive research on Google Translate accuracy, if you want to check it out.
It’s very simple to use Google Translate. Type in the text in your source language, select the target language, and the tool will immediately translate the content. You can also upload a document or translate entire websites if you want to.
| Pros | Cons |
| - Easy to use - Fast translations - Good for low-visibility and non-critical translations - Free to use - Verified translations | - No integrations - No generative AI capabilities - No option to iterate on translations using the tool |
Lokalise can use Google Translate as a machine translation engine for quick first-pass translations. Content flows into Lokalise from your tools, translations are generated with Google Translate, and then you refine and approve them with Lokalise’s glossary/TM and review workflow before publishing.
Gemini can process very large inputs thanks to its extended context window, but it doesn’t include TM-backed localization governance by default.
Gemini (Google’s successor to Bard) is a multimodal large language model capable of translating text, documents, images, and even entire codebases. Its standout capability is an extended context window (2M+ tokens in newer versions), allowing it to process very large files or repositories in a single pass.
However, while Gemini can handle massive inputs, it does not natively include translation memory (TM) or structured localization governance. This means consistency across releases depends on prompt discipline rather than stored assets.
To use Gemini for translation, paste or upload your content, specify the target locale and tone, and generate translations in a single prompt. Gemini can also process image-based text (OCR) and large documents thanks to its extended context window.
| Pros | Cons |
| - Large context window (up to 2M+ depending on model/availability) for translating long documents or codebases - Multimodal (text + image translation) - Fast generation - Free access tier available | - No built-in translation memory - No glossary enforcement by default - No localization workflow or release management - Consistency depends on manual prompt control |
Microsoft Translator offers fast neural machine translation, but it doesn’t store translations for reuse or support structured localization workflows.
Microsoft Bing Translator provides instant neural machine translation for text, documents, and websites across many languages, making it a practical option for quick, low-risk translations.
To use Microsoft Translator, follow the next steps:
Unless you’re uploading documents, you’ll have to copy and paste your text and translate it one by one.
It’s also worth mentioning that you can’t store translations to re-use in the future and there are limited dialects. For example, you only have the option of Spanish and can’t break it down by Spanish-speaking countries.
🧠 Good to know
Microsoft Translator offers a few other cool features like automatic language detection, widely used phrases, and the option to listen to the translation and download the app. You can also choose between three different types of tone for your translation.
| Pros | Cons |
| - Easy to use - Free plan - API available | - Scored one of the worst for accuracy - No integrations |
An AI translation agent is a system that can pull context (TM, glossary, style rules), choose the best model for each task, run QA checks, and route work through review, so localization runs as a managed workflow, not a one-off prompt.
Instead of instant translations, teams want an AI agent that can:
Lokalise’s AI capabilities act as the manager layer for translation. It uses smart routing across multiple LLMs (like ChatGPT or Claude), and automatically selects the best-performing engine for each translation based on language pair and context.
Lokalise’s Custom AI Profiles are built on a RAG-powered architecture. That means they retrieve the right context (TM matches, glossary terms, style rules, examples) and inject it into the generation step. In other words, the model will always translate with your approved language.
Speed and accuracy matter, but in 2026 they’re not the deciding factor. You need to think about maintaining consistency in the long term.
Before you buy anything, check whether the tool can preserve your terminology and style across every release, not just produce a good-looking one-off translation.
To evaluate AI translation tools, pay attention to:
General-purpose models can produce fast translations, but without TM and governance, they create inconsistency debt. These are small variations that compound across teams, products, and markets until localization becomes expensive to fix.
Instead of treating translation as a single model, Lokalise’s AI capabilities act as an orchestration layer inside your localization workflow. Sign up for a 14-day free trial today.
Watch this tour to see how Lokalise AI orchestration works.
1. What are the best AI translation tools in 2026?
Lokalise is the best AI translation tool that combines RAG-powered AI orchestration with translation memory, glossary enforcement, and workflow automation. Other options include a mix of general-purpose LLMs (like ChatGPT, Claude, and Gemini) and neural machine translation engines (like DeepL and Google Translate). The right choice depends on your goal: quick drafts, fluent standalone translations, or governed, repeatable localization workflows.
2. What is the difference between LLM translation and NMT (neural machine translation)?
LLM translation uses large language models (like ChatGPT or Claude) that generate translations through contextual prediction, while NMT (like DeepL or Google Translate) relies on neural networks trained specifically for translation tasks. LLMs are more flexible and adaptable to tone, but NMT engines are often more stable for straightforward, high-volume translations.
3. What are AI translation agents?
AI translation agents are systems that combine large language models with retrieval and workflow automation. The best ones like Lokalise, pull context from translation memory (TM), glossaries, and style guides using a RAG (Retrieval-Augmented Generation) architecture, choose the best model for each task, run QA checks, and route translations through review before delivery.
4. Can you localize with ChatGPT?
Yes, you can localize with ChatGPT by prompting it with specific target locales, tone instructions, and terminology constraints. However, without integration into a translation management system (TMS) that provides translation memory and glossary enforcement, ChatGPT alone cannot guarantee long-term consistency across releases.
5. Are AI translation tools replacing human translators?
AI translation tools are not replacing human translators, but they are changing the workflow. In 2026, AI is commonly used for first-pass translations, while humans handle post-editing, quality assurance, and high-risk content. The most effective approach combines AI speed with human oversight.
Author
Meet Rachel, our Content Manager and Lead Copywriter, who pivoted from advertising to SaaS and has never looked back.
Born and raised in the UK, Rachel has lived in London, Paris, Buenos Aires, and now Brussels. Through city-hopping, traveling, and her studies in French and Journalism, she’s picked up French and Spanish, and is now inventing her own language with help from her three-year-old daughter: Franglospanish!
Outside work, Rachel enjoys making (and eating) fresh pasta, drawing, and spending as much time as possible outside, cycling, hiking, or running.
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