SciCode evaluates language models on generating code for realistic scientific research problems across 16 subfields of physics, math, chemistry, biology, and material science. Problems decompose into 338 subproblems requiring domain knowledge recall, scientific reasoning, and precise code synthesis. Based on real scripts from published research.
BenchLM is tracking SciCode in the local dataset, but exact-source verification records for these rows are still being attached. To avoid a blank benchmark page, BenchLM shows the current tracked rows below as a display-only reference table.
These tracked rows are useful for inspection and spot-checking, but until exact-source attachments are completed they should not be treated as fully verified public benchmark rows.
BenchLM mirrors the published tracked score view for SciCode. Gemini 3.1 Pro leads the public snapshot at 59% , followed by Claude Mythos Preview (58.7%) and GPT-5.4 Pro (56.2%). BenchLM does not use these results to rank models overall.
Gemini 3.1 Pro
gemini-3-1-pro
Claude Mythos Preview
Anthropic
claude-mythos-preview
GPT-5.4 Pro
OpenAI
gpt-5-4-pro
The published SciCode snapshot is tightly clustered at the top: Gemini 3.1 Pro sits at 59%, while the third row is only 2.8 points behind. The broader top-10 spread is 13.2 points, so the benchmark still separates strong models even when the leaders cluster.
24 models have been evaluated on SciCode. The benchmark falls in the Coding category. This category carries a 20% weight in BenchLM.ai's overall scoring system. Within that category, SciCode contributes 10% of the category score, so strong performance here directly affects a model's overall ranking.
Version
SciCode 2024
Refresh cadence
Annual
Staleness state
Refreshing
Question availability
Public benchmark set
BenchLM uses freshness metadata to decide whether a benchmark should still be treated as a strong differentiator, a benchmark to watch, or a display-only reference. For the full scoring policy, see the BenchLM methodology page.
SciCode evaluates language models on generating code for realistic scientific research problems across 16 subfields of physics, math, chemistry, biology, and material science. Problems decompose into 338 subproblems requiring domain knowledge recall, scientific reasoning, and precise code synthesis. Based on real scripts from published research.
Gemini 3.1 Pro currently leads the published SciCode snapshot with a tracked score of 59%. BenchLM shows this benchmark for display only and does not use it in overall rankings.
24 AI models are included in BenchLM's mirrored SciCode snapshot, based on the public leaderboard captured on April 7, 2026.
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