| Rank | Model | Score |
|---|---|---|
| 1 | nemotron-3-5-content-safety-4b | 86.1 |
| 2 | gpt-oss-safeguard-20b | 83.9 |
| 3 | shieldstral-1-0 | 70.3 |
| 4 | qwen3guard-8b | 67.3 |
| 5 | llamaguard-4-12b | 43.9 |
| 6 | shieldgemma-9b | 36.7 |
1 phaseActive
Microsoft's multilingual extension of RealToxicityPrompts, spanning 28 languages, prompt-classification task. de Wynter et al., 2024.
Quick answer: RTP-LX Prompt measures multilingual toxicity classification across roughly 28 languages, using Microsoft's human-transcreated (not just translated) extension of the original RealToxicityPrompts benchmark. It's a relative weak spot for Shieldstral, where a larger competing model leads by double digits.
What it tests: Whether a classifier correctly flags toxic prompts across a broad set of languages, including many outside the typical high-resource set covered by other multilingual safety benchmarks. Why it matters: RTP-LX uses human "transcreation" (culturally adapted translation, not literal machine translation) so toxicity patterns reflect real linguistic and cultural nuance rather than translation artifacts. Known limitations: Per-language subset sizes vary; some included languages have comparatively little training data available for classifiers to learn from, which can depress aggregate scores.
RTP-LX extends the RealToxicityPrompts methodology to roughly 28 languages via human transcreation and annotation, rather than relying purely on machine translation. This task scores whether a classifier's prompt-toxicity judgment holds up across that broader, culturally-adapted multilingual distribution.
| Field | Value |
|---|---|
| Task category | Safety / multilingual toxicity |
| Metric | F1 score (%) |
| Languages | ~28 |
| Created by | Adrian de Wynter, Ishaan Watts, Nektaria Potha, and colleagues (Microsoft) |
| Paper | RTP-LX (arXiv 2404.14397) |
| GitHub | microsoft/RTP-LX |
| License | Research use (see repository terms) |
Each prompt's ground-truth toxicity label is compared against a classifier's prediction and scored as F1, aggregated across the covered languages.
Scores from the Shieldstral model card (Mistral AI, August 2026). F1 (%).
| Rank | Model | Score | Source | Date |
|---|---|---|---|---|
| 1 | Nemotron-3.5-Content-Safety-4B | 86.1% | Mistral AI model card | 2026-08 |
| 2 | GPT-OSS-Safeguard-20B | 83.9% | Mistral AI model card | 2026-08 |
| 3 | Shieldstral 1.0 | 70.3% | Mistral AI model card | 2026-08 |
| 4 | Qwen3Guard-8B | 67.3% | Mistral AI model card | 2026-08 |
| 5 | LlamaGuard-4-12B | 43.9% | Mistral AI model card | 2026-08 |
| 6 | ShieldGemma-9B | 36.7% | Mistral AI model card | 2026-08 |
Scores sourced from Mistral AI's published model card, shown for context. Not Benchgen measurements. This is one of the benchmarks where Shieldstral's model card shows a notable gap to the leading model.
No Benchgen results yet — be the first to run RTP-LX Prompt.
| Benchmark | What it tests | Languages | Saturation |
|---|---|---|---|
| RTP-LX Prompt | Multilingual toxicity classification | ~28 | Medium |
| PolyGuard Prompt | Multilingual prompt-harm classification | 17 | Medium |
Benchgen tracks version-controlled, regression-tested guard-model performance — submit your classifier's RTP-LX scores to compare against Shieldstral and other guard models.
Last updated 2026-08-12.