Do coding agents recommend Bland AI?
Coding agents raised Bland AI in 86 of 531 judged voice agents sessions and chose it in none of them.
Coding agents raised Bland AI in 86 of 531 judged voice agents 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 voice agents inside a realistic codebase. Not what a chat assistant says about Bland AI. What an agent actually installed.
The numbers
| Category | Voice agents |
| Sessions in the category | 531 |
| Sessions where Bland AI was chosen | 0 |
| Install share | 0% |
| Raised as a candidate, not chosen | 86 |
| Chosen when considered | 0% |
| Site | bland.ai |
Which agents raised Bland AI
Claude Code, Codex, Cursor and Grok Build CLI considered it and none adopted it. Cursor raised it most often, in 35% of its voice agents sessions.
| Agent | Sessions | Raised Bland AI | Chose it |
|---|---|---|---|
| Claude Code | 139 | 25 (18%) | 0 |
| Codex | 155 | 3 (2%) | 0 |
| Cursor | 149 | 52 (35%) | 0 |
| Grok Build CLI | 43 | 6 (14%) | 0 |
| Muse Code | 45 | 0 (0%) | 0 |
Which buyers it came up for
It surfaced most for requests written as vibe coder, in 23% 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 | 107 | 25 (23%) |
| Junior developer | 72 | 11 (15%) |
| Senior engineer | 221 | 39 (18%) |
| Enterprise team | 131 | 11 (8%) |
What Bland AI was up against
The full ranking in voice agents, from the same sessions:
| # | Product | Runs won | Share |
|---|---|---|---|
| 1 | Retell AI | 111 | 21% |
| 2 | Vapi | 104 | 20% |
| 3 | LiveKit Agents | 74 | 14% |
| 4 | ElevenLabs Agents | 49 | 9% |
| 5 | Twilio ConversationRelay | 48 | 9% |
| 6 | OpenAI Realtime API | 41 | 8% |
| 7 | Built in-house (no product adopted) | 27 | 5% |
| 8 | Azure Voice Live API | 17 | 3% |
What this means
Raised 86 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 86 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 531 sessions in voice agents 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 voice agents run can be replayed on the board.
If you work on Bland AI: 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 Bland AI?
They raise it but do not choose it. Across 86 sessions where Bland AI came up as a candidate, agents chose something else every time.
Does Claude Code recommend Bland AI?
In 0 of the 139 sessions in voice agents run with Claude Code, which is 0%.
Do different coding agents treat Bland AI differently?
Not much. Claude Code, Codex, Cursor, Grok Build CLI 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.