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.
Benchmark data & downloads
Where to get, inspect and reuse the data behind this benchmark.
Benchmark dataset
The question/task data itself — download and evaluate locally
Benchmark metadata (JSON)
This page's benchmark record from the Open LLM Radar API
All model scores (JSON download)
Full score table as JSON; paginate with limit/offset params
Open LLM Radar API docs
Swagger UI to explore and reuse all radar API endpoints
Model scores 9 models tested
| # | 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 |