LiT (Lost in Translation) - STEM Abstracts: leaderboard
Metric: MQM>=80 rate (%): share of round-trip translations whose back-translation to English scores at least 80 on an MQM error scale (minor -1, major -5, critical -25) from a Grok 4.1 Fast judge comparing it with the English source; each passage is translated serially through a four-language sequence and back, averaged over eight sequences (high, medium and low-resource languages); STEM paper abstracts; higher is better. Source: arxiv.org. Saturation forecast: Around January 2027. 22 models tracked.
Top models
| # | Model | Score |
|---|---|---|
| 1 | Qwen 3.5 397B A17B | 88.1 |
| 2 | Gemma 4 31B (IT) | 76.2 |
| 3 | Gemma 4 31B (IT) (Thinking) | 76.2 |
| 4 | GLM-5 | 74.4 |
| 5 | Qwen 3.5 397B A17B (Non-reasoning) | 69.4 |
| 6 | GLM-4.7 | 60.6 |
| 7 | Kimi K2 (Thinking) | 58.1 |
| 8 | Qwen 3.5 35B A3B | 52.5 |
| 9 | Qwen 3 235B A22B 2507 (Thinking) | 51.9 |
| 10 | GLM-5 (Non-reasoning) | 45 |
| 11 | Kimi K2 | 43.3 |
| 12 | DeepSeek V3.2 Exp (Non-reasoning) | 42.5 |
| 13 | GPT-OSS-120B (High) | 41.2 |
| 14 | Qwen 3.5 35B A3B (Non-reasoning) | 36.9 |
| 15 | DeepSeek V3.2 Exp (Thinking) | 36.9 |
Interactive version: theaggregate.ai/benchmark?slug=lit-lost-in-translation-stem-abstracts · How It Works · Data refreshed daily, snapshot 2026-10-07.