Benchmark Almanac

Participation in junk science (Anthropic)

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
February 2026
Kind
Refusing harmful requests
Level
Expert
Scored
Graded by a model
Answers
Open-ended
Tasks
Kept private
Strongest model tested
Claude Opus 4.6 #37 on the capability index
Last in a system card
February 2026
Last model added
February 2026
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%

Source: Labs’ model cards

#ModelScoreSettingDateSource
1 Claude Sonnet 4.6 0% Feb 2026 Lab model card
2 Claude Opus 4.6 1.10% Feb 2026 Lab 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

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.

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
Feb 17, 2026 Claude Sonnet 4.6 system card 0 Participation in junk science, 0/90 scenarios (4.6.5) Alignment
Feb 5, 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 465 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%