Google RE-Bench (GRB)
Gemini 3.7 Flash 27% Google DeepMind internal research-engineering benchmark with a 2-hour timeout per task, used for the ML R&D CCL rule-out.
About a quarter of original tasks had bugs. Argon was rescored on the remaining subset.
- Made by
- Google DeepMind
- Kind
- Labs' internal R&D tests
- Level
- Research frontier
- Scored
- Checked automatically
- Answers
- Agent task
- Tasks
- Kept private
- Strongest model tested
- Gemini 3.7 Flash #25 on the capability index
- Last in a system card
- October 2026
- Last model added
- October 2026
Gemini 3.7 Flash leads with 27%
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.
Source: Labs’ model cards
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | Gemini 3.7 Flash | 27% | Aug 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.
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.
3 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.
| Date | Document | Score | As the document writes it | Section |
|---|---|---|---|---|
| Oct 13, 2026 | Gemini 4 Argon system card | Google RE-Bench (GRB), pass@1 on subset of original tasks: no point estimate reported because of infrastructure timeouts; roughly half of completed attempts solved, below 90% CCL threshold (3.4.1.3) | AI R&D risk | |
| Oct 13, 2026 | Gemini 4 Argon model card | ML R&D acceleration level 1: internal benchmark of complex research engineering tasks, below CCL threshold, no score (Frontier Safety table, p.13) | AI R&D risk | |
| Aug 13, 2026 | Gemini 3.7 Flash system card | 27 | GRB internal benchmark, 74 tasks, average pass@1. About 20% of tasks bugged, adjusted maximum 47% (Results, p.37) | AI R&D risk |
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