A benchmark measuring how well AI agents can complete real, economically valuable remote freelance projects end-to-end.
35 model results
One row per model and compatible score measure. Mean = equal-weight arithmetic average of imported reports. Distinct configurations reported for the same model in a source table remain separate. Reports can overlap; the range is not a confidence interval. Unbundle to inspect each observation.
15.83% · Scale Labs · Score · Source-displayed model and agent configuration [2]
Published observations span 0.27 percentage points. Configuration and source differences contribute to this spread; it is not measured run-to-run uncertainty.
#7 of 35 in this displayed comparison; 5.29 pp below the leader.
Epoch AI public data snapshot, CC BY 4.0. Externally collected result; original evaluation provider is linked. CSV: rli_external.csv; score column: Score; fraction scale: 1. Model version is retained in configuration. Original source authors retain credit and any additional licensing terms.
Public source leaderboard captured 26 October 2026. Model names, configurations, native score units and source-reported uncertainty are retained. The displayed agent scaffold and reasoning setting belong to the result. These are published observations, not evaluations run by RLTasks.
TRACE THE SCORE · Per-model notes use published breakdowns where available.
Score vs. release date
Epoch AI · 17 observations
Remote Labor Index · Score · % · higher is better
Dates are model release dates recorded by Epoch AI, not evaluation dates. Each point retains its published score and configuration. Separate releases are not a matched longitudinal experiment. Missing dates are omitted. Explore all reported results ↓
The dashed line traces the best observed score through the model release dates. It describes this selection of reports, with their original configurations. It does not estimate unmeasured models or costs.
View plotted values and sources (17)
Remote Labor Index · Remote Labor Index · Score · %. Published measurements in their original score units.
Epoch AI publishes this benchmark in its capability collection. Imported rows retain the published score column and model configuration. Original authors and evaluation providers remain credited in the result references.
How to read the result
Compare the named task version, tools and scoring rule. Epoch-run evaluations and externally collected results have different provenance. ECI inclusion does not make a benchmark score an ECI value.
Epoch attribution and score coverage
Data is attributed to Epoch AI under CC BY 4.0, with original evaluation providers credited in the linked source snapshots. Scores are imported only where a published mapping identifies the field and its scale; missing or incompatible records are omitted rather than assigned zero.
Versions and evaluation settings
Scale and Epoch report the same Remote Labor Index family. Keep each observation and its evaluation metadata.
Where this fits in an evaluation suite
A transcript that says “done” is weaker evidence than the verified final state. Track policy violations, recoveries, and invalid attempts alongside success.
Start with a capability hypothesis, then choose a second test with a different failure mode. Keep the checkpoint, reasoning effort, agent tools, sampling policy, and scoring procedure fixed when measuring a training intervention. Record failures and missing outputs in the denominator.
A reproducible comparison
Retain Epoch’s task version, source score column, model version, tool setting and sampling budget. External observations retain their original publication URL.
Publish the exact run configuration, item coverage, score units, and available uncertainty. If a source supplies only an aggregate or a single run, preserve that limit. A new model name or a rounded score alone does not establish a capability improvement.