CULTURE-MT: leaderboard
Social media platforms enable large-scale cross-lingual communication, but translating user-generated content (UGC) remains challenging due to its informal style, cultural references, and interaction-based expressions. While recent LLMs have improved translation quality, existing benchmarks and metrics often fail to capture whether translations convey intended meaning and cultural resonance in rea
Metric: Ineff. (%) (self-reported). Source: benchmarklist.com. Saturation forecast: Around January 2027. 15 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | GPT-5 | 9.38 | #98 |
| 2 | GLM-4.6V | 16.07 | #316 |
| 3 | DeepSeek V3.2 | 16.57 | #205 |
| 4 | Qwen 3 235B A22B | 30.24 | #313 |
| 5 | Qwen 3 32B | 30.74 | #443 |
| 6 | Qwen 3 14B | 41.32 | #537 |
| 7 | Qwen 3 8B | 49.6 | #674 |
| 8 | Qwen 3 4B | 60.98 | #829 |
| 9 | Qwen 3 1.7B | 83.53 | #1196 |
| 10 | Qwen 3 0.6B | 95.41 | #1459 |
No result here: #3 Claude Opus 5.5, #6 GPT-6 Astra, #7 GPT-6.1 Sol.
Interactive version: theaggregate.ai/benchmark?slug=culture-mt · How It Works · Data refreshed daily, snapshot 2026-10-20.