# OrcaCyber-Nano-1.0 benchmark scores

Other · model · https://benchgap.net/model/orcacyber-nano-1-0 · data retrieved 2026-10-29

As of 2026-10-29, OrcaCyber-Nano-1.0 (Other) has measured scores on 6 benchmarks and estimated scores on 27 more.

| Benchmark | Score | Source |
|---|---|---|
| [AA-GPQA Diamond](https://benchgap.net/b/aa-gpqa-diamond/current.md) | 87.6% | estimated ± 4.4 pp, high confidence |
| [AA-HLE](https://benchgap.net/b/aa-hle/current.md) | 31.0% | estimated ± 3.8 pp, high confidence |
| [AIME26](https://benchgap.net/b/aime2026/current.md) | 95.6% | estimated ± 4.6 pp, medium confidence |
| [CritPt](https://benchgap.net/b/critpt/current.md) | 7.8% | estimated ± 4.6 pp, high confidence |
| [GPQA Diamond](https://benchgap.net/b/gpqa-diamond/current.md) | 88.3% | measured |
| [HLE](https://benchgap.net/b/hle/current.md) | 32.8% | estimated ± 7.3 pp, medium confidence |
| [IFBench](https://benchgap.net/b/ifbench/current.md) | 56.3% | measured |
| [Terminal-Bench 2.1](https://benchgap.net/b/terminalbench21/current.md) | 72.5% | measured |
| [IFEval](https://benchgap.net/b/ifeval/current.md) | 82.8% | measured |
| [MMLU-Pro](https://benchgap.net/b/mmlu-pro/current.md) | 86.6% | estimated ± 5.5 pp, medium confidence |
| [AA-IFBench](https://benchgap.net/b/aa-if-bench/current.md) | 56.7% | estimated ± 7.4 pp, medium confidence |
| [AIME 2024](https://benchgap.net/b/aime2024/current.md) | 95.1% | estimated ± 2.6 pp, medium confidence |
| [Artificial Analysis Intelligence Index](https://benchgap.net/b/artificialanalysis/current.md) | 25.1% | estimated ± 10.6 pp, low confidence |
| [GPQA](https://benchgap.net/b/gpqa/current.md) | 88.2% | estimated ± 1.2 pp, high confidence |
| [AA-Omniscience Accuracy](https://benchgap.net/b/omniscienceaccuracy/current.md) | 30.3% | estimated ± 8.7 pp, medium confidence |
| [MATH-500](https://benchgap.net/b/math-500/current.md) | 98.6% | measured |
| [MMMLU](https://benchgap.net/b/mmmlu/current.md) | 89.2% | estimated ± 1.1 pp, medium confidence |
| [AIME 2025](https://benchgap.net/b/aime2025/current.md) | 96.2% | measured |
| [AA Terminal-Bench 2.1](https://benchgap.net/b/aa-terminal-bench21/current.md) | 75.9% | estimated ± 6.8 pp, medium confidence |
| [ARC-AGI-1](https://benchgap.net/b/arcagi1/current.md) | 86.3% | estimated ± 2.7 pp, high confidence |
| [ARC-AGI-2](https://benchgap.net/b/arc-agi-2/current.md) | 49.3% | estimated ± 10.1 pp, low confidence |
| [Terminal-Bench 3.0](https://benchgap.net/b/terminal-bench-3/current.md) | 9.1% | estimated ± 9.1 pp, low confidence |
| [Vals GPQA Diamond](https://benchgap.net/b/vals-gpqa-diamond/current.md) | 87.5% | estimated ± 2.0 pp, high confidence |
| [Vals MMLU-Pro](https://benchgap.net/b/vals-mmlu-pro/current.md) | 85.7% | estimated ± 1.6 pp, high confidence |
| [Terminal-Bench 2.1 (Vals AI)](https://benchgap.net/b/vals-terminal-bench21/current.md) | 62.0% | estimated ± 7.4 pp, medium confidence |
| [AA Terminal-Bench 4.0](https://benchgap.net/b/aa-terminal-bench4/current.md) | 10.6% | estimated ± 11.6 pp, low confidence |
| [HLE w/o tools](https://benchgap.net/b/hlenotools/current.md) | 29.9% | estimated ± 4.3 pp, high confidence |
| [ARC-AGI-3](https://benchgap.net/b/arcagi3/current.md) | 0.0% | estimated ± 3.6 pp, medium confidence |
| [HMMT Feb 2025](https://benchgap.net/b/hmmtfeb2025/current.md) | 96.9% | estimated ± 3.1 pp, medium confidence |
| [MMLU-Redux](https://benchgap.net/b/mmluredux/current.md) | 92.3% | estimated ± 2.8 pp, high confidence |
| [SuperGPQA](https://benchgap.net/b/supergpqa/current.md) | 73.6% | estimated ± 13.9 pp, low confidence |
| [MedXpertQA (Text)](https://benchgap.net/b/medxpertqatext/current.md) | 49.0% | estimated ± 4.5 pp, low confidence |
| [MMLU-Pro (Arcee)](https://benchgap.net/b/mmlu-pro-arcee/current.md) | 86.3% | estimated ± 3.4 pp, medium confidence |