AIEvals

Benchmarks

AutomationBench (public split)

Tool-assisted completion of business workflows on AutomationBench’s public split.

Business workflowsZapier, reported by Reflection AIVendor-reported onlyReported by Reflection AI

How it is run

Reflection AI explicitly labels this row AutomationBench public and reports six models, with Beam at 37.0%.

What it cannot tell you

The public split is separate from private-set publisher results and AutomationBench-AA. The checked announcement does not identify the version or partial-credit versus full-pass scoring rule.

Why there is no independent table

No independent suite in this dataset runs AutomationBench (public split). The numbers below are the ones model publishers report about their own models, which is a different kind of evidence: each publisher chooses its own harness and effort level, so a row is readable on its own and not against the other rows.

Read this with

Other versions of the same evaluation. Versions are not comparable: the task set changes between them.

As reported by each publisher

Kept separate per publisher, because the settings are not shared.

Reflection AI

Introducing Beam: model capability comparisons · 5 October 2026

RankModelScore
5Beam37.0%
6GLM 5.226.2%
2GLM 5.348.2%
3Kimi K346.7%
4Qwen3.8 Max39.8%
1DeepSeek V4.1 Flash54.8%

Ranks compare reported scores in the same published row; ties share a rank.

AutomationBench public. Public split only; the announcement does not state the score variant or version.

Reflection AI’s four capability tables, read 6 October 2026. All 21 rows and eight model columns are transcribed; NR means not reported and remains blank. The source’s Qwen 3.8 Max column uses this catalogue’s Qwen3.8 Max spelling. Beam results are publisher reports; Reflection AI cites Artificial Analysis and Datacurve for comparison-model evaluations. The announcement does not specify a common harness, effort or trial count for every row. Ranks are derived within each published row from reported values only, with ties sharing a rank; they do not establish an independent or overall model ranking. AutomationBench uses the public split; AA-Omniscience is an index on the public split only, calculated by Reflection AI. BrowseComp and DeepSearchQA use context management. AA-LCR has no stated version. These settings remain separate from other source fields.

reflection.ai/blog/introducing-beam

Sources

Every page used for this benchmark, read on 17 October 2026.