OptimBench
Muse Spark 1.3 64.2% Only in system cards Agents solve optimization engineering tasks. Part of Meta's recursive-improvement suite and graded programmatically.
In-house Meta evaluation. The tasks are not public.
Made by Meta Machine learning engineering Tasks kept private Last in a system card Oct 2026
Last model added Oct 2026
- Level
- Research frontier Researchers, on open problems or tasks that take days
- Scored
- Checked automatically A program checks the result against an answer key, unit tests or the expected final state.
- Answers
- Agent task Many steps in a terminal, browser, desktop, app, game or robot.
1
Model scored in lab cards
1
System card reports it, from 1 lab
Muse Spark 1.3 leads with 64.2%
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 Muse Spark 1.3 64.2%
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | Muse Spark 1.3 | 64.2% | Oct 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
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.
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.
| Date | Document | Score | As the document writes it | Section |
|---|---|---|---|---|
| 23 Oct 2026 | Muse Spark 1.3 system card | 64.2 | OptimBench, Muse Spark 1.3 (Helpful) (Table 1, Table 5) | AI R&D risk |
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