STEM & Reasoning Benchmark

SciCode (subtask)

SciCode (subtask) - scientific code generation benchmark, subtask-level scoring.

Category

STEM & Reasoning

Score type

percent

Scale

100%

higher is better

Models tested

9

What is SciCode (subtask)?

SciCode (subtask) is a benchmark for hard reasoning in science, technology, engineering and math. The official description says: "SciCode (subtask) - scientific code generation benchmark, subtask-level scoring". Below we describe in simple words what benchmarks of this type generally examine.

What does it test?

SciCode (subtask) measures hard reasoning in science, technology, engineering and math. In detail, it checks:

  • graduate- and expert-level questions in science and math
  • multi-step derivations where each step must be correct
  • problems written to defeat memorisation (novel or recent exam problems)
  • correct use of formulas, units and logical inference

How to read the score

The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better.

Official methodology note: SciCode (subtask) - scientific code generation benchmark, subtask-level scoring.

Frequently asked questions

What is the SciCode (subtask) benchmark?

SciCode (subtask) is a benchmark for hard reasoning in science, technology, engineering and math. The official description says: 'SciCode (subtask) - scientific code generation benchmark, subtask-level scoring'. Below we describe in simple words what benchmarks of this type generally examine.

What does SciCode (subtask) test?

SciCode (subtask) mainly measures hard reasoning in science, technology, engineering and math. Concretely, it checks: graduate- and expert-level questions in science and math; multi-step derivations where each step must be correct; problems written to defeat memorisation (novel or recent exam problems).

How is the SciCode (subtask) score calculated?

The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better. The declared maximum score of this benchmark is 100.0.

Where do I get SciCode (subtask) data and scores?

The official benchmark page/reference is linked on this page. Every score on Open LLM Radar is also available as open data: download the scores JSON (http://open-llm-radar-api:8000/api/v1/benchmarks/scicode-subtask/scores) or pull it directly from the Open LLM Radar API with any HTTP client.

Model scores 9 models tested

Reset
# Model Score Date Source Verified
1 Kimi K3
Moonshot AI
58.7%
18.08.2026 Report Verified
2 Kimi K2.6
Moonshot AI
52%
04.06.2026 Report Unverified
3 DeepSeek-V4-Pro
DeepSeek
50.5%
04.06.2026 Report Unverified
4 Kimi K2.5
Moonshot AI
48.7%
23.08.2026 Report Verified
5 DeepSeek-V4-Flash
DeepSeek
48.2%
04.06.2026 Report Unverified
6 NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16
NVIDIA
44.6%
04.06.2026 Report Verified
7 Muse-Glimmer-30B
Meta Superintelligence Lab
43.6%
01.08.2026 Meta Superintelligence Lab (official model card) Verified
8 Gemma4-31B-it
Google
43.4%
01.08.2026 Meta Superintelligence Lab (official model card) Unverified
9 Qwen3.6-27B
Qwen Team (Alibaba Cloud)
39.8%
01.08.2026 Meta Superintelligence Lab (official model card) Unverified

What now?

You've seen how models score on SciCode (subtask) — here's what to do next.