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.
As of July 6, 2026, Sakana Fugu leads the SciCode leaderboard with 60.1% , followed by Sakana Fugu-Ultra (58.7%) and Qwen3.7 Max (53.5%).
Sakana Fugu
Sakana AI
Sakana Fugu-Ultra
Sakana AI
Qwen3.7 Max
Alibaba
According to BenchLM.ai, Sakana Fugu leads the SciCode benchmark with a score of 60.1%, followed by Sakana Fugu-Ultra (58.7%) and Qwen3.7 Max (53.5%). The scores show moderate spread, with meaningful differences between the top tier and mid-tier models.
13 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.
Year
2024
Tasks
80
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.
Sakana Fugu by Sakana AI currently leads with a score of 60.1% on SciCode.
13 AI models have been evaluated on SciCode on BenchLM.
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