Do coding agents recommend Autocannon?
Autocannon was chosen in 5% of 359 judged performance testing sessions, ranking fourth. Measured with Claude Code, Codex, Cursor, Grok Build CLI and Muse Code.
Autocannon was chosen in 5% of 359 judged performance testing sessions, ranking fourth. It was also raised as a candidate in 11 further sessions without being chosen.
This page reports what happened when Claude Code, Codex, Cursor, Grok Build CLI and Muse Code had to solve a problem in performance testing inside a realistic codebase. Not what a chat assistant says about Autocannon. What an agent actually installed.
The numbers
| Category | Performance testing |
| Sessions in the category | 359 |
| Sessions where Autocannon was chosen | 19 |
| Install share | 5% |
| Rank in category | 4 of 16 |
| Codebases it won in | 2 |
| Raised as a candidate, not chosen | 11 |
| Chosen when considered | 63% |
| Site | github.com |
By agent
With 19 wins spread across 5 agents, the rates below are small numbers and a difference between them is not yet a finding. They are here because the direction is worth knowing, not because the gap is established.
| Agent | Sessions | Chose Autocannon | Share |
|---|---|---|---|
| Claude Code | 89 | 6 | 7% |
| Codex | 108 | 7 | 6% |
| Cursor | 90 | 5 | 6% |
| Grok Build CLI | 18 | 1 | 6% |
| Muse Code | 54 | 0 | 0% |
By who was asking
Autocannon performs similarly across the four kinds of buyer, from 0% to 8%. That is unusual: the category leader changed with the persona in 24 of the 31 categories we measured.
| Who is asking | Sessions | Chose Autocannon | Share |
|---|---|---|---|
| Junior developer | 20 | 0 | 0% |
| Senior engineer | 239 | 19 | 8% |
| Enterprise team | 100 | 0 | 0% |
What Autocannon was up against
The full ranking in performance testing, from the same sessions:
| # | Product | Runs won | Share |
|---|---|---|---|
| 1 | Built in-house (no product adopted) | 202 | 56% |
| 2 | JMH | 30 | 8% |
| 3 | pytest-benchmark | 22 | 6% |
| 4 | Grafana k6 | 22 | 6% |
| 5 | hyperfine | 19 | 5% |
| 6 | PHPBench | 19 | 5% |
| 7 | Autocannon (this page) | 19 | 5% |
| 8 | BenchmarkDotNet | 8 | 2% |
What this means
Agents almost never raise Autocannon without choosing it: 11 sessions raised against 19 chosen. When it gets considered, it usually wins. The constraint is how rarely it gets considered at all, which is a presence problem: templates, framework integrations and pages that answer the configuration and production questions agents actually search for.
Where these numbers come from
The 359 sessions in performance testing are part of a published set of 15,287, run with real coding agents inside realistic codebases and judged blind. The full method is on one page: how we measured this.
Every performance testing run can be replayed on the board.
If you work on Autocannon: the judge recorded a reason for every session where it was raised and passed over. Those reasons are in the transcripts.
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Common questions
Do coding agents recommend Autocannon?
Yes. Autocannon was chosen in 19 of the 359 judged sessions in performance testing, a 5% install share, ranking fourth in its category.
Does Claude Code recommend Autocannon?
In 6 of the 89 sessions in performance testing run with Claude Code, which is 7%.
Do different coding agents treat Autocannon differently?
Not much. Claude Code, Codex, Cursor and Muse Code chose it at similar rates, between 0% and 7% of their runs.
How was this measured?
Real coding agents at pinned versions were run in sandboxes inside 110 realistic codebases and asked to solve real tasks. A simulated project owner approved or questioned each recommendation before any code was written, and a judge from a model family that builds none of the agents read every session blind.
How often is Autocannon considered but not chosen?
It was raised as a candidate in 11 sessions without being chosen, and chosen in 19. That is a 63% conversion from considered to chosen.
Where this comes from
Armature ran 15,287 judged sessions with Claude Code, Codex, Cursor, Grok Build CLI and Muse Code inside 110 realistic codebases, and published every run. The numbers on this page come from that work.