math benchmark
SciCode is a research coding benchmark curated by scientists that challenges language models to code solutions for scientific problems. It contains 338 subproblems decomposed from 80 challenging main problems across 16 natural science sub-fields including mathematics, physics, chemistry, biology, and materials science. Problems require knowledge recall, reasoning, and code synthesis skills.
Updated Aug 11, 2026
Higher score ranks better on this benchmark.
| 01 | BY | 59.8% | 100.0% | 19 | C | |
| 02 | GO | 59.0% | 94.4% | 19 | C | |
| 03 | BY | 57.8% | 88.9% | 19 | C | |
| 04 | AC | 53.5% | 83.3% | 19 | C | |
| 05 | MA | 52.2% | 77.8% | 19 | C | |
| 06 | AC | 51.3% | 72.2% | 19 | C | |
| 07 | MA | 48.7% | 66.7% | 19 | C | |
| 08 | MA | 44.8% | 61.1% | 19 | C | |
| 09 | NV | 44.6% | 55.6% | 19 | C | |
| 10 | ME | 43.6% | 50.0% | 19 | C | |
| 11 | NV | 42.0% | 44.4% | 19 | C | |
| 12 | ZA | 41.7% | 38.9% | 19 | C | |
| 13 | MI | 39.0% | 33.3% | 19 | C | |
| 14 | CO | 38.2% | 27.8% | 19 | C | |
| 15 | CO | 38.0% | 22.2% | 19 | C | |
| 16 | IN | 38.0% | 16.7% | 19 | C | |
| 17 | ZA | 37.3% | 11.1% | 19 | C | |
| 18 | MI | 36.0% | 5.6% | 19 | C | |
| 19 | NV | 33.3% | 0.0% | 19 | C |
A closer view of the leading scores on this benchmark.
The leading models and scores on this benchmark.
What SciCode measures and how its scores work.
SciCode is a research coding benchmark curated by scientists that challenges language models to code solutions for scientific problems. It contains 338 subproblems decomposed from 80 challenging main problems across 16 natural science sub-fields including mathematics, physics, chemistry, biology, and materials science. Problems require knowledge recall, reasoning, and code synthesis skills.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about SciCode.
Seed 2.1 Pro is currently ranked first with 59.8%.
SciCode is a research coding benchmark curated by scientists that challenges language models to code solutions for scientific problems. It contains 338 subproblems decomposed from 80 challenging main problems across 16 natural science sub-fields including mathematics, physics, chemistry, biology, and materials science. Problems require knowledge recall, reasoning, and code synthesis skills.
Yes. Higher values rank better for this benchmark.
19 model results are currently shown.
Yes. This benchmark can contribute to the current LLMBoard capability score.