Understand how today’s LLMs perform across multiple tasks and languages
- How leading LLMs perform across high- and low-resource languages
- Which models excel at specific synthetic data generation tasks
- Where LLMs outperform humans
- How to choose the right LLM for global AI applications
Download TrainAI’s multilingual LLM synthetic data generation study 2.0
How does your preferred LLM stack up against the rest?
25,600
76,800
>211,200
120
Here are 6 key takeaways from our study:
Overall: top performers across all tasks and languages
Overall: top performers across all tasks and languages
Here are the top performers overall across all 4 data generation tasks and 8 languages from our study.
| Rank | Model | Average overall score (1–5 range, higher is better) |
| 1 | Gemini 2.5 Pro | 4.73 |
| 2 | Claude Sonnet 4.5 | 4.61 |
| 3 | DeepSeek V3.1 | 4.51 |
Multilingual proficiency: the language gap is closing fast
Multilingual proficiency: the language gap is closing fast
The disparity between LLM performance on well-supported vs. underrepresented languages has narrowed dramatically. Gemini 2.5 Pro achieved scores above 4.5 out of 5 across multiple tasks in Kinyarwanda – a language in which previous model generations could barely produce coherent text.
GPT-5 and Claude Sonnet 4.5 also showed meaningful improvements in long-tail languages. While challenges remain, the current generation of models signals that synthetic data generation and translation are becoming viable for a far broader range of languages than ever before.
Task alignment: one model doesn’t fit all
Task alignment: one model doesn’t fit all
No single model dominated across all tasks and languages. GPT-5 excelled at text normalization and translation but struggled with content generation, particularly in Chinese and Polish.
Gemini 2.5 Pro led in conversation generation and translation but was matched by Claude Sonnet 4.5 on domain-specific paragraph generation. Smaller models like Mistral Small 3.2 performed surprisingly well in certain languages while failing in others. Practitioners should expect to evaluate models against their specific use cases rather than relying on overall rankings.
Humans vs. machines: LLMs surpass single-pass human quality
Humans vs. machines: LLMs surpass single-pass human quality
When human creators worked under realistic constraints – limited time, no secondary review, and no extensive research – the top LLMs consistently outscored them across most languages.
Humans achieved steady results around 4.5 but did not rank first in any language. This doesn't render human expertise obsolete; rather, it suggests a shift in workflows where LLMs produce strong first drafts and humans add value through review, refinement, and specialized judgment.
Cost: thinking harder costs more – tokenizer efficiency matters again
Cost: thinking harder costs more – tokenizer efficiency matters again
Reasoning models generate up to 10x more tokens during their "thinking" process, amplifying the cost impact of tokenizer efficiency. Gemini 2.5 Pro now leads with 3.67 characters per token – roughly 10% more efficient than GPT-5 and up to 3.5 times more efficient than Claude Sonnet 4.5 on non-Latin scripts.
For high-volume or reasoning-intensive workloads, these differences translate directly into significant cost variation, making tokenizer efficiency a renewed consideration when selecting models.
Benchmark drift: today’s upgrade might be tomorrow’s downgrade
Benchmark drift: today’s upgrade might be tomorrow’s downgrade
