As large language models rapidly enter the translation field, enterprises now have access to more AI translation capabilities than ever before. Domestic mainstream models such as DeepSeek, GLM, Kimi, Qwen, Doubao, Hunyuan, and ERNIE are increasingly being applied to text understanding, multilingual processing, and content generation scenarios. However, for enterprise translation, model capabilities cannot be evaluated through a single metric. Different types of content place different requirements on AI translation: long-form translation focuses on contextual consistency, professional document translation emphasizes terminology accuracy, while localization requires stronger language expression and market adaptation capabilities. Therefore, this analysis evaluates different LLMs based on common enterprise translation scenarios, focusing on five dimensions: translation quality, terminology consistency, long-text processing, professional domain adaptation, and localization performance.

Comparison of Translation Capabilities Among Mainstream LLMs

Currently, domestic large language models have developed a relatively complete ecosystem, with different models showing different strengths based on their technical approaches and capability focuses.

It should be noted that the advantages of different models are mainly reflected in specific tasks, and there is no single model that is universally suitable for every translation scenario. In practical enterprise applications, businesses need to select more suitable AI capabilities based on document type, professional domain, and quality requirements.

1. Content Localization Translation Evaluation: Moving Beyond Translation to Reaching Target Markets

In the content localization scenario, LanTMS selected a typical enterprise marketing text as the test sample:

Chinese Source Text: LanTMS 致力于通过先进的人工智能翻译技术，帮助企业打破语言壁垒，让优质内容跨越国界，连接全球市场。

Although this type of content appears simple, the real challenge lies in whether the translated content sounds natural, matches the expression habits of overseas audiences, and effectively communicates in a market-oriented context. Based on the same source text, different models showed noticeable differences in LanTMS testing. For English localization, DeepSeek tends to preserve the original logic and generate structured, semantically complete translations; Kimi focuses more on fluency and natural tone; Doubao demonstrates stronger marketing-oriented rewriting capabilities; while ERNIE tends to generate more communication-focused expressions for phrases such as “connecting global markets,” making it more suitable for brand-oriented content.

In localization tasks, being technically correct is not enough. The key question is whether the translation sounds like something local audiences would naturally read and accept.

For example, the same content may show different expression styles across models:

Based on the LanTMS evaluation matrix, the different models show the following characteristics in localization tasks:

The evaluation shows that if enterprises prioritize more natural and marketing-oriented localization content, models such as Kimi, Doubao, and ERNIE may be more suitable for consideration. For organizations seeking a balance between natural expression and information stability, GLM, Qwen, and Hunyuan are better suited for general international content scenarios.

2. Professional Document Translation Evaluation: Professional Translation Requires More Than Understanding

For professional document translation, LanTMS selected a typical pharmaceutical instruction text as the test sample:

Chinese Source Text: This product is used for blood glucose control in adults with type 2 diabetes mellitus and should be used on the basis of diet control and exercise. Common adverse reactions include nausea, diarrhea, and decreased appetite.

This type of content places significantly higher requirements on AI models than general text because it involves standardized medical terminology and domain-specific expressions. Terms such as “type 2 diabetes mellitus,” “glycemic control,” and “adverse reactions” need to be translated according to professional conventions rather than simply converted based on general language meaning. In this evaluation, LanTMS not only compared model outputs but also used terminology highlighting and terminology matching to examine whether models could consistently apply standard expressions such as type 2 diabetes mellitus, glycemic control, and adverse reaction.

The evaluation shows that DeepSeek performs strongly in sentence-level semantic completeness, with structured and rigorous outputs suitable for highly technical and professional documents. GLM demonstrates stable terminology accuracy and standardized expression, making it suitable for technical documentation and patent-related materials. Qwen shows balanced performance in regulatory and standard documents, maintaining stable terminology and logical relationships. Although Doubao is relatively more focused on readability rather than strict terminology control, its output is easier to understand and may be suitable for internal training materials or documents targeting non-specialist audiences. ERNIE demonstrates stronger formal expression capabilities, making it suitable for regulatory and government-related documents.

The following table shows typical differences in translation styles generated by different models using the same test content:

Based on the LanTMS evaluation matrix, the performance characteristics in professional document scenarios are summarized below:

This evaluation also shows that there is no single model that can handle every professional translation scenario. For enterprises, a more practical approach is to select models based on document type, business purpose, and available terminology resources.

3. Long-form Translation Evaluation: Translating a Section Is Different from Translating an Entire Document

Compared with the previous two scenarios, long-form translation places greater emphasis on a model’s ability to continuously understand context. In this evaluation, LanTMS selected a chapter-based document related to content globalization and localization, focusing on how models handle chapter structures, paragraph logic, and terminology consistency. The test content included repeatedly used and interconnected concepts such as “content globalization,” “localization,” “target market culture,” and “user habits and values.” If these concepts are translated differently across sections, the professionalism and readability of the entire document can be significantly affected.

The evaluation shows that Kimi demonstrates advantages in long-form continuous content processing and is suitable for documents with multiple chapters and strong contextual relationships. DeepSeek maintains stable performance in complex logical understanding and is suitable for more technical long-form materials. Qwen provides a balanced performance between contextual consistency and long-distance semantic understanding, while Hunyuan performs well in terminology stability and is suitable for enterprise documents that require consistent expressions throughout the content. In this scenario, the value of LanTMS is not simply sending content to a model for one-time translation, but helping enterprises understand which models fit different document types, where terminology resources should be applied, and which sections require human review.

The following table summarizes the capability focus of different models in long-form translation scenarios:

When applied to real enterprise scenarios, the significance of these results becomes clear. For companies translating product manuals, industry reports, training materials, and white papers, the key question is not simply whether a model can translate. What truly affects delivery quality is whether the model can maintain logical consistency across chapters, use terminology consistently, and reduce the amount of manual revision required afterward.

4. From Model Selection to Model Management: The Value of LanTMS Lies in Applying Different Models Where They Work Best

The three evaluation scenarios demonstrate that different large language models have different strengths across translation tasks. Localization focuses more on natural expression and market communication, professional documents emphasize terminology accuracy and standardized expression, while long-form translation relies heavily on contextual consistency and terminology stability. Therefore, enterprises do not necessarily need to find one “best” model. Instead, they need the ability to flexibly apply different models according to content types and integrate them into a unified translation workflow.

The role of LanTMS is not simply to connect multiple models, but to help enterprises complete multi-model evaluation, scenario-based selection, and workflow integration within a unified platform. Enterprises can select different models according to project requirements and combine them with terminology management, translation memory, CAT editing, and quality assurance capabilities. This transforms AI model usage from temporary experimentation into a reusable, manageable, and continuously optimized translation workflow. For organizations that process multilingual content continuously, this capability is more valuable than pursuing short-term performance from a single model.