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batch
stringclasses
8 values
as_of
stringdate
2026-09-22 00:00:00
2026-10-14 00:00:00
product
stringclasses
5 values
slug
stringclasses
5 values
status
stringclasses
4 values
rate_pct
float64
0
67
⌀
ci95_low_pct
float64
0
45
⌀
ci95_high_pct
float64
25
94
⌀
scored_tasks
float64
90
167
⌀
runs
float64
0
637
⌀
same_tasks_for_all
bool
2 classes
note
stringclasses
6 values
october
2026-10-11
Instinct
instinct
usable
40
20
64
null
15
true
null
october
2026-10-11
Muse
muse
usable
58
36
77
null
19
true
null
october
2026-10-11
Town
town
usable
15
5
36
null
20
true
null
october
2026-10-11
Pine
pine
no_data
null
null
null
null
0
true
No data: almost none of its submissions reached the exam, so the column was set aside rather than scored as zero.
october-other-run-1014b-main
2026-10-14
Instinct
instinct
usable
67
21
94
null
3
true
null
october-other-run-1014b-main
2026-10-14
Muse
muse
usable
33
10
70
null
6
true
null
october-other-run-1014b-main
2026-10-14
Town
town
usable
57
25
84
null
7
true
null
october-other-run-1014b-main
2026-10-14
Pine
pine
no_data
null
null
null
null
0
true
No data: almost none of its submissions reached the exam, so the column was set aside rather than scored as zero.
october-other-run-1014-main
2026-10-14
Instinct
instinct
usable
0
0
66
null
2
true
null
october-other-run-1014-main
2026-10-14
Muse
muse
usable
33
10
70
null
6
true
null
october-other-run-1014-main
2026-10-14
Town
town
no_data
null
null
null
null
0
true
No data: almost none of its submissions reached the exam, so the column was set aside rather than scored as zero.
october-other-run-1014-main
2026-10-14
Pine
pine
no_data
null
null
null
null
0
true
No data: almost none of its submissions reached the exam, so the column was set aside rather than scored as zero.
october-other-run-1012-main
2026-10-12
Instinct
instinct
usable
19
9
38
null
26
true
null
october-other-run-1012-main
2026-10-12
Muse
muse
usable
21
11
36
null
39
true
null
october-other-run-1012-main
2026-10-12
Town
town
no_data
null
null
null
null
0
true
No data: almost none of its submissions reached the exam, so the column was set aside rather than scored as zero.
october-other-run-1012-main
2026-10-12
Pine
pine
no_data
null
null
null
null
0
true
No data: almost none of its submissions reached the exam, so the column was set aside rather than scored as zero.
october-other-takeover-1011-pilot-paid-rerun
2026-10-11
Instinct
instinct
usable
41
22
64
null
17
true
null
october-other-takeover-1011-pilot-paid-rerun
2026-10-11
Muse
muse
usable
33
14
61
null
12
true
null
october-other-takeover-1011-pilot-paid-rerun
2026-10-11
Town
town
usable
26
12
49
null
19
true
null
october-other-takeover-1011-pilot-paid-rerun
2026-10-11
Pine
pine
no_data
null
null
null
null
0
true
No data: almost none of its submissions reached the exam, so the column was set aside rather than scored as zero.
october-other-pilot-1011-batch-v2
2026-10-11
Instinct
instinct
usable
50
30
70
null
20
true
null
october-other-pilot-1011-batch-v2
2026-10-11
Muse
muse
usable
37
19
59
null
19
true
null
october-other-pilot-1011-batch-v2
2026-10-11
Town
town
no_data
null
null
null
null
0
true
No data: almost none of its submissions reached the exam, so the column was set aside rather than scored as zero.
october-other-pilot-1011-batch-v2
2026-10-11
Pine
pine
usable
25
11
47
null
20
true
null
first-batch
2026-09-22
Instinct
instinct
usable
43
36
50
159
null
false
159 scored tasks; Pine has the largest published sample at 167. Marginal intervals alone do not establish a significant lead.
first-batch
2026-09-22
Pine
pine
usable
20
14
25
167
null
false
More tasks scored than any other product. The September report said some tasks were being re-run; October status is unverified.
first-batch
2026-09-22
Muse
muse
usable
19
13
25
90
null
false
This is the figure after we fixed our own adapter. The September report said a further 83 tasks were being re-run; October status is unverified.
first-batch
2026-09-22
Town
town
withheld
null
null
null
null
null
false
Withheld. The figure we were given does not reconcile with the only records we can check: five batches for this product survive in the bench repository's history and all five were refused for incomplete domain coverage, giving roughly 13% across 30 scorable tasks. We are not publishing a number while our own evidence d...
first-batch
2026-09-22
Grok
grok
pending
null
null
null
null
null
false
In the exam, no published figure yet. We have not taken this one apart at the runtime level either, so it has scores coming but no teardown.
same-tasks
2026-09-22
Instinct
instinct
usable
52
45
61
null
380
true
null
same-tasks
2026-09-22
Muse
muse
usable
40
33
46
null
637
true
null
same-tasks
2026-09-22
Town
town
usable
37
30
44
null
513
true
null
same-tasks
2026-09-22
Pine
pine
usable
30
23
36
null
546
true
null

Personal Agent Bench

Task completion rates for personal AI agents, published with 95% intervals and sample sizes, plus the runtime teardown behind each product. This is the data from personalagentbench.com; the pages there explain every figure.

There is no combined score and no ranking. A rate without its interval and the number of tasks it rests on is not a result, so every row carries both.

Files

  • results.csv: one row per product per batch. Columns: batch, as_of, product, slug, status, rate_pct, ci95_low_pct, ci95_high_pct, scored_tasks, runs, same_tasks_for_all, note.

  • results.json: the same figures with each batch's description and caveat. Read the caveat before comparing batches; the September and October batches used different tasks, scoring rules and exam versions.

  • scorecard.json: what we found when we took each runtime apart, on these questions:

  • execution: Where it runs. Does the model run near your machine or far from it, and what can it touch?

  • tools: How tools are exposed. When does a tool definition enter the context, and who decides which ones?

  • memory: Where memory lives. Files you can open, or a server-side store you can only reach through the product?

  • control: What you get to approve. What does it ask before acting, and can you take back what it knows?

  • channel: How you reach it. What is the default surface, and what happens on the ones that are not?

  • unknown: What we could not establish. Stated plainly, because a bench that only reports wins is not a bench.

Batches

Batches are listed separately and never added together. Each batch's note in results.json says what it can and cannot be compared with.

Batch Label As of Same tasks for every product
october October batch (11 October) 2026-10-11 yes, 20
october-other-run-1014b-main Other October batch (14 October, second run) 2026-10-14 yes, 7
october-other-run-1014-main Other October batch (14 October, first run) 2026-10-14 yes, 7
october-other-run-1012-main Other October batch (12 October, large batch) 2026-10-12 yes, 72
october-other-takeover-1011-pilot-paid-rerun Other October batch (11 October, rerun) 2026-10-11 yes, 20
october-other-pilot-1011-batch-v2 Other October batch (11 October, pilot) 2026-10-11 yes, 20
first-batch First batch 2026-09-22 no
same-tasks The 09-21 to 09-22 records, filtered 2026-09-22 yes, 114

Method and provenance

Every figure is a share of tasks completed, with a 95% interval and the number of scored tasks it rests on. There is no combined score and no ranking, because a rate without its interval and its sample size is not a result. These September rates describe adapted runtimes using the exam model, on differing scored subsets; they are not consumer-product end-to-end performance.

On the September batches: these figures come from the team running the exam. The bench repository deliberately stopped tracking generated batch records in September, on the grounds that it holds the inputs a run needs rather than the outputs a run produces, so there is no published record behind these numbers for us or anyone else to check them against. We are printing them anyway, with that said plainly, because the alternative is printing nothing and pretending we have no numbers. If the underlying batches are ever published we will reconcile against them and say what changed. The October batches differ: the note on each says whether its figures were checked against the records behind them.

How the exam works: https://personalagentbench.com/how-the-exam-works/. How scoring works: https://personalagentbench.com/how-scoring-works/. Changes to the exam: https://personalagentbench.com/how-the-exam-works/changes/.

The exam tasks themselves are not in this dataset, so that they stay usable as a test.

Who runs it

Published by Personal Agent Bench, run by the Zentor team. Zentor builds agent software of its own, which is a conflict of interest, so its products are not on the scorecard and will not be added.

Corrections

Every corrected claim is logged at https://personalagentbench.com/corrections/ (machine-readable: https://personalagentbench.com/corrections.json). This copy was exported on 2026-10-22; the latest batch in it is from 2026-10-14. The website is the current version.

Licence and citation

CC BY 4.0. Please credit Personal Agent Bench and link to https://personalagentbench.com/results/.

@misc{personalagentbench,
  title  = {Personal Agent Bench},
  author = {{Personal Agent Bench}},
  year   = {2026},
  url    = {https://personalagentbench.com/results/}
}
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