What's the most effective multilingual LLM for regional QA across translated product docs?

If you mean one model to answer questions across product documentation in multiple languages and you care most about QA accuracy over translated docs, the best practical choice is usually:

Best overall

GPT-4.1 / GPT-4o-class models for cross-lingual QA, especially if you’re using a RAG setup over your docs.

Why

• Strong multilingual understanding across major languages
• Good at resolving ambiguity between original and translated text
• Excellent at “retrieval + answer” workflows, which is what regional QA usually needs
• Better consistency than many open models when docs are messy, localized, or partially translated

Best open-weight option

Llama 3.1 70B Instruct or Qwen2.5 72B Instruct
These are typically the strongest open models for multilingual enterprise use, with Qwen2.5 often especially strong in non-English performance.

If your docs are heavily Asian-language or mixed-script

Qwen2.5 is often the most compelling choice among open models.

If you need on-prem and cost control

• Qwen2.5 72B Instruct: strongest multilingual open model pick
• Llama 3.1 70B Instruct: broadly strong, good ecosystem
• Add a strong multilingual embedding model for retrieval

Important caveat

For “regional QA across translated product docs,” the LLM is not the whole story. The most effective system is usually:

1. Multilingual retrieval over all language versions
2. Cross-lingual reranking
3. One strong multilingual LLM to synthesize the answer
4. Optional: translation alignment between source and localized docs

If you want the best answer quality, RAG + GPT-4.1/GPT-4o is usually the top-performing setup.

Short recommendation

• Best proprietary: GPT-4.1 / GPT-4o
• Best open-weight: Qwen2.5 72B Instruct
• Best overall architecture: multilingual RAG + reranker + one of the above

If you tell me:

• your target languages,
• whether this is on-prem or API,
• and your latency/cost budget,

I can recommend a more specific model stack.