Do coding agents recommend Parallel?
Parallel was chosen in 5% of 277 judged AI search sessions, ranking seventh. Measured with Claude Code, Codex and Cursor.
Parallel was chosen in 5% of 277 judged AI search sessions, ranking seventh. It was also raised as a candidate in 20 further sessions without being chosen.
This page reports what happened when Claude Code, Codex and Cursor had to solve a problem in AI search inside a realistic codebase. Not what a chat assistant says about Parallel. What an agent actually installed.
The numbers
| Category | AI search |
| Sessions in the category | 277 |
| Sessions where Parallel was chosen | 13 |
| Install share | 5% |
| Rank in category | 7 of 27 |
| Codebases it won in | 4 |
| Raised as a candidate, not chosen | 20 |
| Chosen when considered | 39% |
| Site | parallel.ai |
By agent
With 13 wins spread across three 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 Parallel | Share |
|---|---|---|---|
| Claude Code | 92 | 0 | 0% |
| Codex | 96 | 3 | 3% |
| Cursor | 89 | 10 | 11% |
By who was asking
Parallel does much better with one kind of buyer than another. It won 15% of sessions asked as senior engineer and 0% of those asked as vibe coder.
| Who is asking | Sessions | Chose Parallel | Share |
|---|---|---|---|
| Vibe coder | 44 | 0 | 0% |
| Junior developer | 117 | 2 | 2% |
| Senior engineer | 68 | 10 | 15% |
| Enterprise team | 48 | 1 | 2% |
What Parallel was up against
The full ranking in AI search, from the same sessions:
| # | Product | Runs won | Share |
|---|---|---|---|
| 1 | Anthropic web search | 56 | 20% |
| 2 | Exa | 38 | 14% |
| 3 | Brave Search API | 31 | 11% |
| 4 | Tavily | 21 | 8% |
| 5 | OpenAI web search | 16 | 6% |
| 6 | Firecrawl | 14 | 5% |
| 7 | Parallel (this page) | 13 | 5% |
| 8 | Built in-house (no product adopted) | 13 | 5% |
What this means
Parallel was raised in 20 sessions and chosen in 13. That ratio is balanced enough that the ceiling is presence rather than integration: the product converts reasonably when it is on the table, and it is not on the table often enough.
Where these numbers come from
The 277 sessions in AI search are part of a published set of 7,025, run with real coding agents inside realistic codebases and judged blind. The full method is on one page: how we measured this.
Every AI search run can be replayed on the board.
If you work on Parallel: 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 Parallel?
Yes. Parallel was chosen in 13 of the 277 judged sessions in AI search, a 5% install share, ranking seventh in its category.
Does Claude Code recommend Parallel?
In 0 of the 92 sessions in AI search run with Claude Code, which is 0%.
Do different coding agents treat Parallel differently?
Yes, and by a wide margin. Cursor chose it in 11% of its runs and Claude Code in 0%.
How was this measured?
Real coding agents at pinned versions were run in sandboxes inside 77 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 Parallel considered but not chosen?
It was raised as a candidate in 20 sessions without being chosen, and chosen in 13. That is a 39% conversion from considered to chosen.
Where this comes from
Armature ran 7,025 judged sessions with Claude Code, Codex and Cursor inside 77 realistic codebases, and published every run. The numbers on this page come from that work.