math benchmark
A competitive programming benchmark using problems from the CodeForces platform. The benchmark evaluates code generation capabilities of LLMs on algorithmic problems with difficulty ratings ranging from 800 to 2400. Problems cover diverse algorithmic categories including dynamic programming, graph algorithms, data structures, and mathematical problems with standardized evaluation through direct platform submission.
Updated Aug 11, 2026
Higher normalized rating ranks better on this benchmark.
| 01 | DE | 100.0% | 100.0% | 16 | C | |
| 02 | DE | 100.0% | 93.3% | 16 | C | |
| 03 | DE | 90.0% | 86.7% | 16 | C | |
| 04 | AC | 85.1% | 80.0% | 16 | C | |
| 05 | AC | 82.2% | 73.3% | 16 | C | |
| 06 | OP | 82.1% | 66.7% | 16 | C | |
| 07 | AC | 80.7% | 60.0% | 16 | C | |
| 08 | DE | 79.5% | 53.3% | 16 | C | |
| 09 | DE | 79.5% | 46.7% | 16 | C | |
| 10 | OP | 74.3% | 40.0% | 16 | C | |
| 11 | DE | 70.7% | 33.3% | 16 | C | |
| 12 | DE | 69.7% | 26.7% | 16 | C | |
| 13 | AC | 65.9% | 20.0% | 16 | C | |
| 14 | DE | 64.3% | 13.3% | 16 | C | |
| 15 | GO | 55.3% | 6.7% | 16 | C | |
| 16 | GO | 47.6% | 0.0% | 16 | C |
A closer view of the leading scores on this benchmark.
The leading models and scores on this benchmark.
What CodeForces measures and how its scores work.
A competitive programming benchmark using problems from the CodeForces platform. The benchmark evaluates code generation capabilities of LLMs on algorithmic problems with difficulty ratings ranging from 800 to 2400. Problems cover diverse algorithmic categories including dynamic programming, graph algorithms, data structures, and mathematical problems with standardized evaluation through direct platform submission.
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 CodeForces.
DeepSeek-V4-Flash-Max is currently ranked first with 100.0%.
A competitive programming benchmark using problems from the CodeForces platform. The benchmark evaluates code generation capabilities of LLMs on algorithmic problems with difficulty ratings ranging from 800 to 2400. Problems cover diverse algorithmic categories including dynamic programming, graph algorithms, data structures, and mathematical problems with standardized evaluation through direct platform submission.
Yes. Higher values rank better for this benchmark.
16 model results are currently shown.
Yes. This benchmark can contribute to the current LLMBoard capability score.