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Benchmarking AI
on Government

Government work is highly specialized and hard. Can frontier AI finish the work government depends on? CivicArena tests how AI agents handle government and regulated work.

7
Models
41
Tasks per model
287
Single attempts
Fail rate on government and regulated work
Average across frontier models

September results · Updated September 29, 2026. Earlier Sol attempts informed task revisions.

Most models failed more tasks than they passed.

  • Federal accounting
  • Contracts and funding
  • FOIA requests
  • Tax review
  • Relocation travel
  • Benefits
  • Grant eligibility
  • Federal personnel
  • Insurance programs
  • Military pay
  • Pay and retirement

Astra passed 28 of 39 scored tasks. GPT-6.1 Sol passed 25 of 38. The other 5 models failed a majority of their scored assignments. These results measure this development set, not government-work accuracy as a whole.

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Task completion by model

A pass requires every required check.
Pass rates exclude unscored attempts.

Government work is a chain of decisions, calculations, and documents. Getting individual steps right isn’t enough if an error carries through to the final result. We count a task as passed only when the submission meets every required check. The breakdown below shows where models fell short, including downstream checks blocked by earlier errors.

Models

7 models · 41 tasks · 287 attempts

Model / evaluation dateShare of scored tasks passedPassed / scored
GPT-6 AstraOpenAI · 2026-09-16
71.8%28 / 39 · 2 unscored
GPT-6.1 SolOpenAI · 2026-09-29
65.8%25 / 38 · 3 unscored
Claude Opus 5.5Anthropic · 2026-09-25
42.5%17 / 40 · 1 unscored
GPT-5.6 SolOpenAI · 2026-09-16
41.5%17 / 41
Claude Fable 5.1Anthropic · 2026-09-16
40.0%16 / 40 · 1 unscored
Claude Opus 5Anthropic · 2026-09-16
35.9%14 / 39 · 2 unscored
Claude Sonnet 5.5Anthropic · 2026-09-29
32.4%12 / 37 · 4 unscored
0%25%50%75%100%

High reasoning. A 60-minute work limit per attempt. Each model made one attempt per task. Different unscored counts produce different denominators. Choose “Common scored tasks only” to compare the same tasks.

Counts for the selected models and tasks
ModelPassedFailedUnscoredPass rate
GPT-6 Astra2811271.8% 28/39
GPT-6.1 Sol2513365.8% 25/38
Claude Opus 5.51723142.5% 17/40
GPT-5.6 Sol1724041.5% 17/41
Claude Fable 5.11624140.0% 16/40
Claude Opus 51425235.9% 14/39
Claude Sonnet 5.51225432.4% 12/37
Download this comparison, with filters and denominators.

Which tasks did models pass?

Only tasks with a score for every
selected model enter this chart.

The overall ranking doesn't tell you whether models fail the same assignments. Each bar counts tasks by how many selected models passed. We leave out a task if any selected model lacks a score.

31 comparable tasks. This chart excludes 10 tasks with at least one unscored attempt.

Failures by type of work

Failed / scored tasks.

Government work demands more than a simple answer. A single assignment can require reconciling conflicting rules, carrying accurate information through multiple files and steps, and producing a result that holds up to review. These results show where frontier models fall short and why Statecraft is building AI specifically for government work.

Work familyAstraSol 6.1Opus 5.5Sol 5.6Fable 5.1Opus 5Sonnet 5.5
Federal accounting10 tasks3/10
3/10
6/10
4/10
4/9
1 unscored
4/10
5/9
1 unscored
Benefits3 tasks0/3
0/3
1/3
1/3
0/3
2/3
1/1
2 unscored
Grant eligibility1 task0/1
0/1
0/1
0/1
0/1
0/1
0/1
Federal personnel2 tasks0/2
0/2
0/2
1/2
0/2
0/1
1 unscored
0/2
Insurance programs2 tasks0/2
0/2
1/2
0/2
1/2
1/2
1/2
Military pay1 task0/1
0/1
0/1
0/1
0/1
0/1
0/1
Pay and retirement3 tasks0/3
2/3
1/3
3/3
2/3
1/2
1 unscored
2/2
1 unscored
Contracts, funds and FOIA14 tasks5/12
2 unscored
5/12
2 unscored
10/14
12/14
13/14
12/14
11/14
Tax review4 tasks2/4
3/4
4/4
2/4
3/4
4/4
4/4
Relocation travel1 task1/1
No score
1 unscored
No score
1 unscored
1/1
1/1
1/1
1/1

Some families contain one task. These results do not estimate performance across an entire profession.

Task results

Click a cell for check counts
or the reason we left it unscored.

Each cell shows passed checks / required checks. Green means every check passed; coral means at least one failed. Grey marks an unscored attempt. Select a cell for details.

41 matching tasks
PassFailUnscored
TaskAstraSol 6.1Opus 5.5Sol 5.6Fable 5.1Opus 5Sonnet 5.5
gov-acct-002
gov-acct-003
gov-acct-004
gov-acct-005
gov-acct-006
gov-acct-007
gov-acct-008
gov-acct-009
gov-acct-010
gov-acct-011
gov-ben-001
gov-ben-002
gov-ben-003
gov-civ-001
gov-hr-001
gov-hr-002
gov-ins-001
gov-ins-002
gov-mil-001
gov-pay-001
gov-pay-002
gov-pay-003
gov-syn-001
gov-syn-002
gov-syn-003
gov-syn-004
gov-syn-005
gov-syn-006
gov-syn-007
gov-syn-008
gov-syn-009
gov-syn-010
gov-syn-011
gov-syn-012
gov-syn-013
gov-syn-014
gov-tax-001
gov-tax-002
gov-tax-003
gov-tax-004
gov-trv-001

Task-search filters affect this matrix only. The comparison controls above apply to every chart and export.

Why this matters

A foundation for government-specific AI

Statecraft builds these assignments around the work government teams actually handle. We use them to identify where general-purpose models fall short and guide the AI we are building specifically for government.

Judgment across rules and records

Government work rarely arrives as one tidy question. Models must reconcile information across files, apply the relevant rules and carry those decisions into a final submission.

Accuracy through the whole assignment

One wrong decision can affect everything that follows. A task passes only when the final submission meets every required check. Partial success helps us understand a failure, but does not turn it into completed work.

Completed work, not plausible answers

An explanation can sound convincing while the underlying work is wrong. These assignments require models to produce a checkable result, such as a payment calculation, benefit determination or records response.

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