Benchmark Almanac

Participation in junk science (Anthropic)

Claude Sonnet 4.6 0% Only in system cards

Models talk with simulated users ranging from curious to openly fraudulent who seek fake science. Score is the rate of generating unsound scientific content.

In-house Anthropic evaluation with 90 scenarios. The tasks are not public.

Made by Anthropic Released Feb 2026 Refusing harmful requests Tasks kept private Last in a system card Feb 2026 Last model added Feb 2026
Level
Expert Specialists with years of training, often over hours of work
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.
2
Models scored in lab cards
2
System cards report it, from 1 lab

Claude Sonnet 4.6 leads with 0%

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 Claude Sonnet 4.6 0%
  2. 2 Claude Opus 4.6 1.10%
#ModelScoreSettingDateSource
1 Claude Sonnet 4.6 0% Feb 2026 Model card
2 Claude Opus 4.6 1.10% Feb 2026 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% 2026 Claude Opus 4.6 0% 20% 40% 60% 80% 100% 2026 Claude Opus 4.6

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.

2 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.

DateDocumentScoreAs the document writes itSection
17 Feb 2026 Claude Sonnet 4.6 system card 0 Participation in junk science, 0/90 scenarios (4.6.5) Alignment
5 Feb 2026 Claude Opus 4.6 system card 1.1 Participation in junk science, content generation in 1/90 prompts (6.3.9, Figure 6.3.9.A) Alignment

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%