OJBench
OJBench - competitive programming and reasoning benchmark.
Category
STEM & Reasoning
Score type
percent
Scale
relative
higher is better
Models tested
18
What is OJBench?
OJBench evaluates competitive programming and reasoning on olympiad-informatics style problems. The model has to write full programs that pass hidden judge tests — correctness and runtime limits both count.
What does it test?
OJBench measures hard reasoning in science, technology, engineering and math. In detail, it checks:
- algorithmic problem solving with strict test sets
- runtime and memory limits, as in a real judge
- complete program implementations
- reasoning about edge cases and complexity
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.
Frequently asked questions
What is the OJBench benchmark?
OJBench evaluates competitive programming and reasoning on olympiad-informatics style problems. The model has to write full programs that pass hidden judge tests — correctness and runtime limits both count.
What does OJBench test?
OJBench mainly measures hard reasoning in science, technology, engineering and math. Concretely, it checks: algorithmic problem solving with strict test sets; runtime and memory limits, as in a real judge; complete program implementations.
How is the OJBench score calculated?
The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better.
Where do I get OJBench 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/ojbench/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 18 models tested
| # | Model | Score | Date | Source | Verified |
|---|---|---|---|---|---|
| 1 |
Kimi K2.6
Moonshot AI
|
60.6%
|
23.08.2026 | Report | Verified |
| 2 |
Kimi K2.5
Moonshot AI
|
54.7%
|
23.08.2026 | Report | Verified |
| 3 |
Qwen3.5-27B
Qwen Team (Alibaba Cloud)
|
40.1%
|
01.02.2026 | Report | Unverified |
| 4 |
Qwen3.5-122B-A10B
Qwen Team (Alibaba Cloud)
|
39.5%
|
01.02.2026 | Report | Verified |
| 5 |
Qwen3.5-35B-A3B
Qwen Team (Alibaba Cloud)
|
36%
|
01.02.2026 | Report | Unverified |
| 6 |
Qwen3-235B-A22B
Qwen Team (Alibaba Cloud)
|
32.7%
|
01.02.2026 | Report | Unverified |
| 7 |
MiniCPM5-2B
OpenBMB
|
32.5%
|
— | Report | Verified |
| 8 |
Qwen3-Next-80B-A3B-Thinking
Qwen
|
29.7%
|
10.09.2025 | Report | Verified |
| 9 |
Qwen3-30B-A3B-Thinking-2507
Qwen
|
25.1%
|
31.07.2025 | Report | Verified |
| 10 |
Qwen3.5-4B
Qwen
|
24.1%
|
01.02.2026 | Report | Verified |
| 11 |
granite-4.2-3B
IBM
|
21.8%
|
— | Report | Verified |
| 12 |
Qwen3-30B-A3B
Qwen
|
20.7%
|
29.04.2025 | Report | Verified |
| 13 |
Nemotron-3-Nano-4B
NVIDIA
|
20%
|
— | Report | Verified |
| 14 |
Gemma4-E4B
Google
|
19%
|
— | Report | Verified |
| 15 |
Gemma4-E2B
Google
|
11.6%
|
— | Report | Verified |
| 16 |
LFM2.5-2.6B
LiquidAI
|
11.2%
|
— | Report | Verified |
| 17 |
LFM2.5-8B-A1B
LiquidAI
|
8.2%
|
— | Report | Verified |
| 18 |
Qwen3.5-2B
Qwen Team (Alibaba Cloud)
|
2.6%
|
— | Report | Verified |