Which AI agents give consistent results with Clay?
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Clay AI agent benchmark: consistent results across runs
With Clay, every one of seven AI coding agents found exactly the same number of work emails in all three of its runs, because Clay returned the same answers for the same contacts each time. Across all five tasks in our benchmark, Grok CLI with Grok 4.6 was the most consistent agent: its score never moved more than 8 points between runs. When an agent used the same Clay method in every run, its score and its credit spend held steady.
By The Prompting Company · Snapshot November 5, 2026
How did we measure consistency?
Each agent ran each of five Clay tasks three times. For every agent and task we measured the spread of the primary score, out of 100: the best run minus the worst. The primary scores were emails correct, the average of the four company fields (domain, HQ, employee range, funding), CMO name correct, people marked correctly as stayed or left, and companies meeting all three target list rules.
Which agents gave the most consistent results?
Spread between the best and worst of three runs, in points. Lower is more consistent.
| Agent | Work emails | Enrich companies | CMO | Job changes | Target list | Largest spread | Sum of spreads |
|---|---|---|---|---|---|---|---|
| Grok CLI · Grok 4.6 | 0 | 3.25 | 6 | 8 | 2 | 8 | 19.25 |
| Claude Code · Sonnet 5 | 0 | 11.75 | 11 | 2 | 11 | 11.75 | 35.75 |
| Codex · GPT-6 Sol | 0 | 5.5 | 15 | 10 | 6 | 15 | 36.5 |
| Factory · Kimi K3 | 0 | 13.25 | 9 | 3 | 12 | 13.25 | 37.25 |
| Pi · GPT-5.6 Luna | 0 | 0 | 13 | 12 | 60 | 60 | 85 |
| Claude Code · Opus 5.5 | 0 | 3.25 | 8 | 94* | 3 | 94* | 108.25 |
| OpenCode · Kimi K3 | 0 | 91.5* | 73* | 2 | 22 | 91.5* | 188.5 |
*Includes a run that reached the 60-minute limit and counts as 0.
Grok's scores stayed within 8 points on every task. Claude Code with Opus 5.5 was even steadier when it finished: leave out its one timed-out run and its largest spread on any task is 8 points, and its sum of spreads is 14.25, the lowest of any agent.
Why was the work email task identical every run?
Every agent repeated its work email score exactly: six agents found 93 of 100 in all three runs, and Opus found 86 in all three. Clay returned the same addresses for the same contacts each time, and every agent that searched all 100 contacts found the same 93, as described in "How accurate is Clay for AI agents finding work emails?".
The cost repeated too: Pi with GPT-5.6 Luna spent 337.5 Clay credits in each of its three runs, and Grok 337.5 to 340.5. For a team that refreshes the same list every week, that predictability matters. With Clay's Work Email waterfall, you only pay credits for the provider that finds a match.
What kept scores steady?
The steadiest results came from the same Clay methods that scored highest.
- One enrichment per profile on job changes. Claude Code with Sonnet 5's job change scores moved 2 points across its three runs and OpenCode with Kimi K3 (OpenCode Kimi) also 2, with every run enriching each LinkedIn profile once.
- Checking the candidates in Clay's company search. Codex with GPT-6 Sol checked each company match in every run and scored 98 or 99 on domains.
- Free people search, then one Work Email. Opus used this CMO workflow in all three runs and spent 285, 288 and 293 credits.
Naming the method in the prompt is the simplest way to get the same result run after run.
Is the Clay credit spend consistent too?
When an agent used the same workflow every run, the spend barely moved:
| Agent and task | Clay credits by run |
|---|---|
| Opus, find the CMO | 285 · 288 · 293 |
| OpenCode Kimi, job changes | 1,404 · 1,404 · 1,406 |
| Pi, find work emails | 337.5 · 337.5 · 337.5 |
A stable workflow gives stable Clay costs; see "What is the cheapest AI agent to run with Clay?" for the full cost picture.
How we tested
Seven agents ran each task three times: Claude Code (Opus 5.5 and Sonnet 5), Codex (GPT-6 Sol), Pi (GPT-5.6 Luna), Grok CLI (Grok 4.6), OpenCode (Kimi K3) and Factory (Kimi K3). Each run had a fresh sandbox, the same Clay key and the same 19 saved Clay routines, and a 60-minute limit; a timeout counts as 0. Agents ran one at a time, so Clay credits are measured as the change in the Clay workspace credit balance. Platform failures were rerun and are not counted. Tasks, prompts and graders are at github.com/promptingcompany/clay-bench.
What this doesn't tell you. Three runs show each agent's range, not a precise average.
Try it yourself
The Clay Agent Plugin ships Clay's skills to your agent up front, including the habits that keep credit use down; setup is one prompt (about 15 minutes), and there is no additional cost to use it. Install the Clay Agent Plugin or give your agent a Clay API key and the Clay Public API docs. Run the same list twice and compare the outputs row by row. If the results differ, check that the agent used the same Clay method in both runs. The benchmark tasks are in the clay-bench repo.
Common questions
Which AI agents give consistent results with Clay? Grok CLI with Grok 4.6: its score moved at most 8 points between three runs on each of five Clay tasks, and Opus did as well whenever it finished.
Does Clay return the same answer every time? In our work email task, yes. Every agent repeated its exact score in all three runs, and six of seven found the same 93 addresses each time.
What keeps an agent's results steady? Using the same Clay method every run: Opus's CMO runs cost 285 to 293 credits, and OpenCode Kimi's job change runs 1,404 to 1,406.