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What is SciCode?
Last reviewed July 16, 2026
SciCode is a coding benchmark whose problems come from real scientific research workflows: implementing numerical methods, simulations, and analysis routines drawn from fields like physics, mathematics, and biology. Each main problem decomposes into subproblems with verifiable outputs, so scoring is objective. It is deliberately harder and more specialized than contest-style coding tests, and scores run low across the board.
What makes scientific coding different
Research code couples programming with domain knowledge: implementing a solver correctly requires understanding the underlying mathematics or physics, not just the language. Models that do well on general coding benchmarks can fail here by producing plausible code with subtly wrong science, which the verified subproblem outputs catch.
How to read SciCode scores
Scores run low relative to general coding suites, and small absolute differences matter: solving even a few more full problems represents a real capability gap. For research work, weigh SciCode next to the math benchmarks; together they predict scientific-computing usefulness better than either alone.
idapt's coding rankings include a SciCode lane, and code execution in chat runs a model's scientific code as it writes it, so verification is part of the workflow rather than an afterthought.
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