Benchmark Almanac

Petri Bloom

Only in system cards

An auditor model runs generated scenarios to elicit named misaligned behaviors. Judges score each transcript from 1 to 10.

Bloom generates scenarios and Petri runs the interactions. Meta averages mean scores across 13 shared behaviors.

Released
December 2025
Kind
Deception and alignment
Level
Expert
Scored
Graded by a model
Answers
Agent task
Tasks
All public
Strongest model tested
Muse Spark 1.3 #26 on the capability index
Last in a system card
October 2026
Last model added
October 2026
1
Model scored in lab cards
1
System card reports it, from 1 lab

Muse Spark 1.3 leads with 1.44

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 Muse Spark 1.3 1.44

Source: Labs’ model cards

#ModelScoreSettingDateSource
1 Muse Spark 1.3 1.44 Oct 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
1.44 2027 1.44 2027

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.

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
Oct 23, 2026 Muse Spark 1.3 system card 1.44 Petri Bloom v0.2 aggregate mean behavior score on 1-10, 13 behaviors, Muse Spark 1.3 (API) (Table 2, Table 18). Mean maximum 3.42 Alignment

More in Safety and alignment

All 465 in Safety and alignment
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  • 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%