Do coding agents recommend Intl API?
Coding agents raised Intl API in 68 of 392 judged internationalization sessions and chose it in none of them.
Coding agents raised Intl API in 68 of 392 judged internationalization sessions and chose it in none of them.
This page reports what happened when Claude Code, Codex, Cursor, Grok Build CLI and Muse Code had to solve a problem in internationalization inside a realistic codebase. Not what a chat assistant says about Intl API. What an agent actually installed.
The numbers
| Category | Internationalization |
| Sessions in the category | 392 |
| Sessions where Intl API was chosen | 0 |
| Install share | 0% |
| Raised as a candidate, not chosen | 68 |
| Chosen when considered | 0% |
| Site | developer.mozilla.org |
Which agents raised Intl API
Claude Code, Codex, Cursor and Grok Build CLI considered it and none adopted it. Cursor raised it most often, in 26% of its internationalization sessions.
| Agent | Sessions | Raised Intl API | Chose it |
|---|---|---|---|
| Claude Code | 113 | 27 (24%) | 0 |
| Codex | 114 | 9 (8%) | 0 |
| Cursor | 103 | 27 (26%) | 0 |
| Grok Build CLI | 26 | 5 (19%) | 0 |
| Muse Code | 36 | 0 (0%) | 0 |
Which buyers it came up for
It surfaced most for requests written as vibe coder, in 48% of those sessions. Knowing which buyer already has the product in mind tells you which pages to fix first.
| Who is asking | Sessions | Raised it |
|---|---|---|
| Vibe coder | 84 | 40 (48%) |
| Junior developer | 97 | 13 (13%) |
| Senior engineer | 86 | 12 (14%) |
| Enterprise team | 125 | 3 (2%) |
What Intl API was up against
The full ranking in internationalization, from the same sessions:
| # | Product | Runs won | Share |
|---|---|---|---|
| 1 | next-intl | 92 | 23% |
| 2 | Django translation framework | 78 | 20% |
| 3 | Symfony Translation | 32 | 8% |
| 4 | Paraglide JS | 32 | 8% |
| 5 | Rails I18n | 30 | 8% |
| 6 | Built in-house (no product adopted) | 26 | 7% |
| 7 | i18next | 22 | 6% |
| 8 | Vue I18n | 20 | 5% |
What this means
Raised 68 times and chosen none is a specific, diagnosable result, and a better starting position than being unknown. The agent has the product in mind. It reached the shortlist 68 times. Something then lost every one of those sessions.
In our data the causes, in the order they occur:
- The quickstart does not run when pasted, so the agent abandoned it mid-integration.
- The documentation describes an interface that changed, so the generated code failed.
- The product name and the package name differ, so the install step went wrong.
- The fit was genuinely wrong for the repository, which is fine and worth knowing.
All but the last are fixable in days, and the reason is written down in the session transcript.
Where these numbers come from
The 392 sessions in internationalization are part of a published set of 11,878, run with real coding agents inside realistic codebases and judged blind. The full method is on one page: how we measured this.
Every internationalization run can be replayed on the board.
If you work on Intl API: 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 Intl API?
They raise it but do not choose it. Across 68 sessions where Intl API came up as a candidate, agents chose something else every time.
Does Claude Code recommend Intl API?
In 0 of the 113 sessions in internationalization run with Claude Code, which is 0%.
Do different coding agents treat Intl API differently?
Not much. Claude Code, Codex, Cursor and Muse Code chose it at similar rates, between 0% and 0% of their runs.
How was this measured?
Real coding agents at pinned versions were run in sandboxes inside 91 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.
Where this comes from
Armature ran 11,878 judged sessions with Claude Code, Codex, Cursor, Grok Build CLI and Muse Code inside 91 realistic codebases, and published every run. The numbers on this page come from that work.