Harmful content defect rate (Azure AI Evaluation SDK, Microsoft)
Phi-4-Reasoning-Vision-15B 1.40% leads since March 2026 GPT-4o simulates adversarial conversations with the model, and a judge flags violence, sexual, self-harm and hateful content. Scored as the share of harmful responses, lower is better.
In-house Microsoft evaluation. The tasks are not public. Variants add known jailbreaks, Tier 1 languages, or audio prompts.
- Made by
- Microsoft
- Released
- March 2025
- Kind
- Refusing harmful requests
- Level
- Everyday
- Scored
- Graded by a model
- Answers
- Open-ended
- Tasks
- Kept private
- Last in a system card
- March 2026
- Last model added
- March 2026
Phi-4-Reasoning-Vision-15B leads with 1.40% among models released by March 2026
Best score per model as labs report them in their own cards. Labs pick their own settings, so these compare less cleanly than independent runs.
The newest model with a score here came out in March 2026, and nobody has reported a newer one. Among the 20 highest-ranked models on the capability index, these have no score yet:
- Claude Fable 5.2
-
Gemini 4 Carbon - GPT-6 Astra
- Claude Opus 5.5
- and 16 more
- 1 Phi-4-Reasoning-Vision-15B 1.40%
- 2 Phi-4 Mini 3.75%
- 3 Phi-4-multimodal 4.00%
Source: Labs’ model cards
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | Phi-4-Reasoning-Vision-15B | 1.40% | Mar 2026 | Lab model card | |
| 2 | Phi-4 Mini | 3.75% | Mar 2025 | Lab model card | |
| 3 | Phi-4-multimodal | 4.00% | Mar 2025 | Lab model card |
2 models have held number one in labs’ own numbers since its release in March 2025
Lab claims count from the day the card came out, independent runs from the model’s release.
Source: Labs’ model cards
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Source: Labs’ model cards
Labs report it only in system cards
We read the headline results of 522 launch posts from 52 labs, and none of them list this benchmark. Some system cards do, below.
3 system cards report it, from 1 lab
Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.
| Date | Document | Score | As the document writes it | Section |
|---|---|---|---|---|
| Mar 4, 2026 | Phi-4-Reasoning-Vision-15B technical report | 1.4 | Text to Text Safety, automated content safety defect rate, lower is better. Image to Text Safety 4.5% (Section 7 table) | Safety |
| Mar 3, 2025 | Phi-4-multimodal technical report | 4 | Defect Rate average over Violence 7%, Sexual 6%, Self-Harm 0%, Hateful 3%, lower is better (Table 10). With known jailbreaks 2.25% (Table 11). Tier 1 languages 5.06% (Table 13). Audio prompts 3.25% (Table 14) | Safety |
| Mar 3, 2025 | Phi-4 Mini technical report | 3.75 | Defect Rate average over Violence 6%, Sexual 6%, Self-Harm 0%, Hateful 3%, lower is better (Table 10). With known jailbreaks 1.25% (Table 11). Tier 1 languages average 3.91% (Table 13) | Safety |
More in Safety and alignment
- FORTRESS In use Paired adversarial and benign national-security misuse prompts. Rubric-graded risk score on adversarial and over-refusal score on benign prompts. Claude Opus 5.5 5.78%
- StrongREJECT In use Forbidden-prompt jailbreak benchmark with a rubric grader that scores how useful a non-refusal actually is.
Inkling 98.6% - Gray Swan IPI Arena benchmark In use Indirect prompt-injection attacks from public red-teaming competitions replayed on tool, coding and browser agents. Attack success rate at k attempts. Gemini 4 Carbon 0%
- DistressBench In use Models reply as a chatbot to 718 simulated users in psychological crisis, in single-turn and multi-turn chats. Three judge models score replies against clinician-written rubrics.
Muse Spark 1.2 88.4% - Lakera B3 (Backbone Breaker Benchmark) In use Crowdsourced prompt-injection attacks hit backbone LLMs inside agent threat snapshots at three defense levels. The score is attack resistance, 100 minus the board's vulnerability percentage. Claude Opus 4.5 89%
- IHEval In use Models face conflicts between system, user and tool instructions. Reports a composite score for following the higher-priority instruction. GPT-4o 70%