Scientist-authored coding problems require translating domain knowledge into numerical implementations.
246 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 percentage points. Configuration and source differences contribute to this spread; it is not measured run-to-run uncertainty.
#7 of 246 in this displayed comparison; 5.09 pp below the leader.
Published observations span 0 percentage points. Configuration and source differences contribute to this spread; it is not measured run-to-run uncertainty.
#16 of 246 in this displayed comparison; 6.6 pp below the leader.
Published observations span 0 percentage points. Configuration and source differences contribute to this spread; it is not measured run-to-run uncertainty.
#32 of 246 in this displayed comparison; 8.45 pp below the leader.
Published observations span 0 percentage points. Configuration and source differences contribute to this spread; it is not measured run-to-run uncertainty.
#45 of 246 in this displayed comparison; 10.3 pp below the leader.
Publisher-selected recent-model cohort from the public evaluation page. Model labels preserve effort and fallback configurations. Values are a dated snapshot, not a live feed. Consult the publisher’s methodology for the harness, task split and judging; do not merge this cohort with vendor-run results.
SciCode · Score · % · 2026-10-26
Epoch AI public data snapshot, CC BY 4.0. Externally collected result; original evaluation provider is linked. CSV: scicode_external.csv; score column: Score; fraction scale: 1. Model version is retained in configuration. Original source authors retain credit and any additional licensing terms.
Earlier-2026 baseline cohort from the official model card. Asterisks identify competitor scores re-evaluated by Kimi rather than cited from their reports. Coding scores average ten runs; vision averages three runs. Tool-enabled and no-tools results, F1 and exact accuracy, pass@3 and pass^3 remain separate rows. MCPMark here predates the Verified default. APEX uses 452 public tasks.
TRACE THE SCORE · Per-model notes use published breakdowns where available.
Score vs. release date
Epoch AI · 226 observations
SciCode · 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 (226)
SciCode · SciCode · Score · %. Published measurements in their original score units.
These model reports include this benchmark. Their harnesses, task subsets, scoring policies, and resource budgets can differ; follow each report’s evaluation notes before joining the numbers.
Scientist-authored coding problems require translating domain knowledge into numerical implementations.
How to read the result
A useful bridge between scientific understanding and code that produces the required computation.
Limits and comparison traps
Report main-problem and subproblem performance separately. Artificial Analysis currently marks its implementation under review.
Benchmark reference notes
80 scientist-curated research coding problems decomposed into 288 subproblems across 16 natural-science disciplines.
Epoch data and evaluation setting
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.
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
A correct multiple-choice answer and a supported scientific contribution demand different evidence. Check whether the score rewards a proposal, an executed experiment, or a completed research target.
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
Use the exact implementation, version, task subset, tool access, attempt budget, and judge described by the source.
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.