A collection of software performance optimization challenges which test models’ ability to modify a program’s code to significantly increase its performance.
34 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.
Published observations span 0.03 percentage points. Configuration and source differences contribute to this spread; it is not measured run-to-run uncertainty.
#14 of 34 in this displayed comparison; 61.75 pp below the leader.
Published observations span 0.01 percentage points. Configuration and source differences contribute to this spread; it is not measured run-to-run uncertainty.
#18 of 34 in this displayed comparison; 73.53 pp below the leader.
Published observations span 0.03 percentage points. Configuration and source differences contribute to this spread; it is not measured run-to-run uncertainty.
#19 of 34 in this displayed comparison; 74.52 pp below the leader.
Published observations span 0.04 percentage points. Configuration and source differences contribute to this spread; it is not measured run-to-run uncertainty.
#22 of 34 in this displayed comparison; 81.36 pp below the leader.
Shetty, Manish, Jain, Naman, Liu, Jinjian, Kethanaboyina, Vijay, Sen, Koushik, Stoica, Ion · 2025
Dates & evaluation details
GSO-Bench · Score OPT@1 · % · 2026-10-26
Epoch AI public data snapshot, CC BY 4.0. Externally collected result; original evaluation provider is linked. CSV: gso_external.csv; score column: Score OPT@1; fraction scale: 1. Model version is retained in configuration. Original source authors retain credit and any additional licensing terms.
Epoch AI public data snapshot, CC BY 4.0. Externally collected result; original evaluation provider is linked. CSV: gso_external.csv; score column: Score OPT@1; fraction scale: 1. Model version is retained in configuration. Original source authors retain credit and any additional licensing terms.
Shetty, Manish, Jain, Naman, Liu, Jinjian, Kethanaboyina, Vijay, Sen, Koushik, Stoica, Ion · 2025
Dates & evaluation details
GSO-Bench · Score OPT@1 · % · 2026-10-26
Epoch AI public data snapshot, CC BY 4.0. Externally collected result; original evaluation provider is linked. CSV: gso_external.csv; score column: Score OPT@1; fraction scale: 1. Model version is retained in configuration. Original source authors retain credit and any additional licensing terms.
TRACE THE SCORE · Per-model notes use published breakdowns where available.
Score vs. release date
Epoch AI · 42 observations
GSO-Bench · Score OPT@1 · % · 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 (42)
GSO · GSO-Bench · Score OPT@1 · %. 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.
Where this fits in an evaluation suite
The agent wrapper is part of the system. Pin tools, dependencies, timeouts, tests, and the repository revision. Compare contest coding separately from repository maintenance.
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.