AI for Translation: Are Models Like DeepSeek, ChatGPT, and Gemini the Best for Localization?

Boardrooms, LinkedIn comments and webinars are still abuzz with discussion of the AI revolution, and how it’s impacting translation and localization. Though not expressly designed for translation, LLMs are increasingly being used for creating multilingual content – however, the debate continues about their effectiveness for these tasks.

But what do the experts say?

• How do AI models compare to the specialized Neural Machine Translation (NMT) engines?
• Are they viable for large-scale localization or better suited for creative and niche tasks? 
• What ethical, regulatory, and data security challenges do they present? 
• Do open-weight AI models like DeepSeek offer a real alternative to proprietary solutions?

Let’s take a look, with the help of some expert opinions, at the evolving AI translation landscape and what it means for the future of multilingual content.

AI Translation: How Do LLMs Compare?

While LLMs aren’t necessarily built for translation, many industry professionals and organizations have been testing how they perform against established Neural Machine Translation (NMT) systems.

Some models, such as Deepseek’s latest V3 and R1 models, have been praised for their reasoning capabilities, while users report great results for fluency and creativity with models like GPT4 and Claude.

• DeepSeek: Some experts suggest its Chinese-English translation quality surpasses that of other models. It's also particularly strong as mathematical reasoning tasks. However, some early tests show it may struggle with contextual nuance compared to some other models.
• ChatGPT & Claude: These models are preferred by many for high-quality, nuanced, and creative translations, often for marketing or creative content. They can also be useful for stages within localization workflows. In one recent study, Claude Sonnet was evalutated by experts as the best translation model in several language pairs for general translation tasks.
• Gemini & LLaMa: Google’s Gemini models are integrating multimodal capabilities, improving contextual understanding across different types of content, while Meta’s LLaMa focuses on efficiency and adaptability for various AI tasks.

Industry Insight:

Nimdzi industry expert Renato Beninatto, who outlined his thoughts on the major trends to shape the Language and Content Industry in 2025 in our exclusive ebook, carried out an experiment to test a translation error and discovered an interesting difference between GPT-4o and DeepSeek V3 when translating a particularly trick Spanish phrase.

He posted on LinkedIn: “This experiment reveals a significant gap in reasoning capabilities between these AI models. While DeepSeek showed strong grammatical analysis, it struggled with the broader context. ChatGPT demonstrated superior reasoning by understanding the relationship between the content's premise (four words) and the translation.”

Cost-Effective AI Translation? A New Era

Cost is a major consideration for many leaders looking to push the adoption of AI models in content creation and localization. While many of the big AI players have similar token prices, one of the major disruptors in terms of LLM cost has been the arrival of DeepSeek.

Said to have been trained for a fraction of the cost of its rivals, DeepSeek’s cost per token is generally far less than its rivals. This lower cost is likely to mean harnessing the power of an LLM becomes far more financially viable for smaller businesses and other organizations.

LLM costs are likely to trend downwards. But for many businesses that have already invested in building a term base or translation memory with neural machine translation,  or for high-volume use cases, it might remain more cost-effective for now to continue to use NMT – especially since NMT can process large volumes of content more quickly.

Industry Insight 

“There’s no one-size-fits-all all approach when it comes to choosing to use an LLM or NMT for translation. The most cost-effective solution depends on the type of content, the target audience, content volume, and many more factors. That's why Language Service Providers – with their expertise in implementing both NMT and AI solutions - are uniquely positioned to help find the right balance.”

Pavel Soukenik, Head of Global Solutions, Acolad

Customization & Open-Weight Models: A Localization Revolution?

Perhaps the most important difference between DeepSeek and its major LLM rivals is that the model is available through an open weight model. While some might describe it as open source, many experts insist that an AI model that makes its trained parameters publicly accessible while keeping other aspects of the model private should be referred to as Open Weight.

Either way, unlike most other LLM rivals, DeepSeek is available for customization, with the ability to download, modify, and deploy models on-premises enabling businesses to fine-tune the AI specifically - for example, to suit language pairs and industry-specific terminology.

This could be particularly groundbreaking for the development of new models designed especially to handle work in low-resource languages that may be underserved by proprietary models. Beyond this, organizations could securely train the model on their own translation memories, terminology databases and brand guidelines - all without exposing sensitive data to third parties.

Open weight models have the potential to really democratize advanced language technology, while allowing organizations to maintain their competitive linguistic advantages. Many more businesses could be able to run customized applications of the models, without the huge initial costs of training the model.

AI Translation & Compliance: The Data Security Dilemma

As AI adoption grows, concerns over data security, compliance, and ethics aren’t going away.

For example, authorities in Australia, the USA, Italy, Taiwan and South Korea have already moved to enact restrictions on DeepSeek’s use, citing privacy and data concerns. Italy initially took a similar course when ChatGPT launched.

Data protection, regulatory frameworks and other compliance concerns remain a major obstacle to AI adoption for businesses in many areas, particularly regulated industries.

Key questions for business leaders to consider include:

• Can AI models ensure data protection and compliance with local regulations when used for translation?

• Is it feasible for companies to opt for locally-hosted AI models to safeguard data security?

• What will be the impact of diverging approaches to AI regulation between Europe, the USA, and Asia?

While these questions do not have simple answers, organizations must carefully evaluate whether AI translation tools can meet their compliance obligations, consider the viability of locally-hosted models, and prepare for the implications of divergent regulatory approaches between major global regions.

What’s Next for AI-Powered Translation?

As we've seen, the landscape continues to shift with open-weight models challenging traditional proprietary systems, while cost-effectiveness and customization capabilities are becoming key differentiators in the market.

The future perhaps points toward more democratized access to AI technology, with smaller LSPs and businesses gaining the ability to deploy and customize their own AI solutions. The trend of local deployment and model customization will likely accelerate, particularly for organizations working with low-resource languages and specialized industry terminology.

However, this evolution must navigate complex regulatory waters, as various countries implement different approaches to AI governance. The industry will need to balance innovation with compliance, particularly as regional differences in AI regulation continue to emerge.

Key Takeaways for Businesses & Localization Professionals:

✔ AI models are improving, but in some cases, like high content volumes, traditional NMT engines may still be the best choice.

✔ Open-weight AI models may drive industry innovation, but adoption depends on regulatory clarity.

✔ AI translation should be used strategically, for example, creative content and automated post-editing.

✔ Security and data compliance concerns remain critical factors in AI tool selection.

Industry Insight

“The future of AI in translation and localization isn’t just about technology - it’s about how we balance security, compliance, and innovation. AI is reshaping the language industry, but the true power comes from adapting these technologies to specific contexts."

Pavel Soukenik, Head of Global Solutions, Acolad