Benchmark Almanac

OpenEQA

Agents answer over 1,600 open-vocabulary questions about real indoor scenes from recorded episodes or active exploration. A judge model scores answers against human answers.

Made by Meta FAIR Released Apr 2024 Embodied reasoning All tasks public Last in a launch post May 2024 , dropped before it saturated Last model added Sep 2025
Level
Everyday Most adults, without training
Scored
Graded by a model A language model scores each response, usually against a rubric.
Answers
Short answer A number, word or short string matched to a key.
2
Models scored in lab cards
1
Launch post lists it, from 1 lab
1
System card reports it, from 1 lab

Gemini 1.5 Pro leads with 57.9

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 Gemini 1.5 Pro May 2024 57.9
  2. 2 gemini-robotics-er-1.5-preview 55.0
#ModelScoreSettingDateSource
1 Gemini 1.5 Pro May 2024 57.9 May 2024 Model card
2 gemini-robotics-er-1.5-preview 55.0 Sep 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
55.0 56.0 57.0 58.0 2024 2025 55.0 56.0 57.0 58.0 2024 2025

No launch post has listed it since May 2024

Launch posts from every lab that has listed it, one tick per post. Tall colored ticks include it, and short gray ones leave it out.

Google DeepMind
DateLaunch postScoreAs the post writes it
14 May 2024 Gemini 1.5 Pro 57.9 OpenEQA (val), 0-shot (LLM-judge score)

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
25 Sep 2025 gemini-robotics-er-1.5-preview technical report 55 OpenEQA (Table 19, thinking on). Thinking off 50.5 Capabilities

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