Benchmark Almanac

DeepSeek in-house safety benchmark

DeepSeek-R1 25.2% Only in system cards

Models answer 1,120 Chinese and English questions across 28 safety subcategories. GPT-4o labels each answer as unsafe, safe or rejection.

In-house DeepSeek evaluation. The questions are not public. Lower unsafe and rejection ratios are better.

Made by DeepSeek Released Sep 2025 Refusing harmful requests Tasks kept private Last in a system card Jan 2025 Last model added Jan 2025
Level
Everyday Most adults, without training
Scored
Graded by a model A language model scores each response, usually against a rubric.
Answers
Open-ended Free-form text, documents, designs or media judged as a whole.
1
Model scored in lab cards
1
System card reports it, from 1 lab

DeepSeek-R1 leads with 25.2%

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.

  1. 1 DeepSeek-R1 25.2%
#ModelScoreSettingDateSource
1 DeepSeek-R1 25.2% Jan 2025 Model card

Scores over time

All the scores we have, one dot each, so a model tested at several settings appears more than once.

Reported by the lab in its model card
0% 20% 40% 60% 80% 100% 2025 DeepSeek-R1 0% 20% 40% 60% 80% 100% 2025 DeepSeek-R1

Labs report it only in system cards

We read the headline results of 479 launch posts from 51 labs, and none of them list this benchmark. Some system cards do, below.

One system card reports it

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.

DateDocumentScoreAs the document writes itSection
20 Jan 2025 DeepSeek-R1 technical report 25.2 In-house safety benchmark, Overall Unsafe ratio (lower is better), rejection 5.6. With risk control system: Unsafe 8.5, rejection 27.3 (Table 10) Safety

More in Safety and alignment

All 444 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%
  • AgentDojo In use Agents do tool-use tasks while tools and data carry prompt injections. Attack success rate counts hijacked runs. Grok 4 Fast 0%
  • MASK In use Measures whether a model contradicts its own elicited beliefs under pressure to lie. Honesty separated from accuracy. Muse Spark Open 98.1%
  • 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. GPT-6 Astra 8.50%
  • Agentic misalignment suite (Anthropic) In use Agents in simulated company settings face threats or goal conflicts that invite blackmail, leaking or sabotage. Score is the misaligned action rate. Claude Sonnet 4.5 0%