---
pretty_name: HumanNumberEval
task_categories:
- text-classification
language:
- en
license: apache-2.0
tags:
- benchmark
- binary-classification
- numbers
- rng-detection
size_categories:
- n<1K
configs:
- config_name: default
  data_files:
  - split: test
    path: numbers.csv
- config_name: results
  data_files:
  - split: test
    path: results.csv
---


🚨 We have found out 93% of the Human numbers are 4 digits and 97% of the RNG ones are 3. This Benchmark Version is Not Recommended anymore and we may fix it later.

# HumanNumberEval

HumanNumberEval is a compact benchmark for testing whether AI systems can distinguish human-chosen numbers from numbers produced by a random number generator.

The benchmark contains 200 labeled examples:

- 100 `human` numbers
- 100 `rng` numbers

Each example is a single number with a binary label. The model sees one number at a time and must answer with exactly `human` or `rng`.

## Dataset Files

This upload contains only the files needed to use and inspect the benchmark:

- `numbers.csv`
- `results.csv`: model prediction/evaluation records
- `model_scores.png`: model score visualization
- `README.md`: dataset card
- `LICENSE`: Apache License 2.0

## Dataset Schema

`numbers.csv` has two columns:

| Column | Type | Description |
| --- | --- | --- |
| `number` | string/integer | The number to classify. Treat as a string if preserving formatting matters. |
| `label` | string | Either `human` or `rng`. |

Example rows:

```csv
number,label
37,human
73,human
832,human
181,rng
585,rng
```

## Task

Given a number, predict one of:

- `human`: the number was chosen by a human
- `rng`: the number was produced by a random number generator

This is a binary classification benchmark. Random guessing is expected to score around 50% accuracy because the dataset is balanced.

## Submit Your Own Numbers

If you want to submit your own numbers for future versions of this benchmark, use this form:

https://docs.google.com/forms/d/e/1FAIpQLSclPrTaO5c59JIuOkb8XGygF2R2bbmdPhy5-6hpDF4NJ7b0zw/viewform

## Prompt

The benchmark prompt used for model evaluation was:

```text
Is this number human or RNG The number is {number} you need to just reply with "human" or "rng" nothing else
```

`{number}` is replaced with the value from the `number` column.

## Loading With `datasets`

```python
from datasets import load_dataset

dataset = load_dataset(
    "csv",
    data_files={"test": "numbers.csv"},
)

print(dataset["test"][0])
```

If you upload this repository as a Hugging Face dataset, it can be loaded by replacing the local path with the dataset repository name:

```python
from datasets import load_dataset

dataset = load_dataset("YOUR_USERNAME/HumanNumberEval")

print(dataset["test"][0])
```

To load the model evaluation records instead:

```python
from datasets import load_dataset

results = load_dataset("YOUR_USERNAME/HumanNumberEval", "results")

print(results["test"][0])
```

## Evaluation

Recommended metric:

- Accuracy

Because the label distribution is balanced, random guessing should score around 50% accuracy. The included `results.csv` file contains reference model outputs evaluated with the prompt above.

### Model Results

The error columns show directional mistakes: `RNG predicted human` counts random numbers labeled as human, and `Human predicted RNG` counts human numbers labeled as random. `Predicted human/RNG` shows each model's valid-label prediction split, while `Other/error` counts invalid outputs or failed calls.

A number of models are close to 50% accuracy because they show a strong label bias rather than balanced discrimination. For example, some models predict almost everything as `human`, while others predict almost everything as `rng`.

| Rank | Model | Accuracy | Correct | RNG predicted human | Human predicted RNG | Predicted human/RNG | Other/error |
| --- | --- | ---: | ---: | ---: | ---: | ---: | ---: |
| 1 | `z-ai/glm-5.1` | 67.0% | 134/200 | 37 | 25 | 110/86 | 4 |
| 2 | `moonshotai/kimi-k2.6` | 64.5% | 129/200 | 24 | 47 | 77/123 | 0 |
| 3 | `z-ai/glm-5.2` | 57.5% | 115/200 | 44 | 38 | 105/92 | 3 |
| 4 | `google/gemini-2.5-pro` | 55.5% | 111/200 | 59 | 30 | 129/71 | 0 |
| 5 | `openai/gpt-oss-120b` | 51.5% | 103/200 | 80 | 16 | 164/35 | 1 |
| 6 | `openai/gpt-5.5` | 51.0% | 102/200 | 56 | 42 | 114/86 | 0 |
| 7 | `anthropic/claude-opus-4.8` | 50.5% | 101/200 | 4 | 93 | 10/188 | 2 |
| 8 | `deepseek/deepseek-v4-pro` | 50.5% | 101/200 | 92 | 4 | 187/10 | 3 |
| 9 | `google/gemma-4-26b-a4b-it` | 50.5% | 101/200 | 11 | 88 | 23/177 | 0 |
| 10 | `google/gemma-4-31b-it` | 50.5% | 101/200 | 1 | 98 | 3/197 | 0 |
| 11 | `mistralai/mistral-medium-3-5` | 50.5% | 101/200 | 0 | 99 | 1/199 | 0 |
| 12 | `anthropic/claude-haiku-4.5` | 50.0% | 100/200 | 100 | 0 | 200/0 | 0 |
| 13 | `anthropic/claude-opus-4.6` | 50.0% | 100/200 | 100 | 0 | 200/0 | 0 |
| 14 | `anthropic/claude-sonnet-4.6` | 50.0% | 100/200 | 100 | 0 | 200/0 | 0 |
| 15 | `google/gemini-3-flash-preview` | 50.0% | 100/200 | 100 | 0 | 200/0 | 0 |
| 16 | `meta-llama/llama-3.3-70b-instruct` | 50.0% | 100/200 | 100 | 0 | 200/0 | 0 |
| 17 | `x-ai/grok-4.3` | 50.0% | 100/200 | 0 | 100 | 0/200 | 0 |
| 18 | `deepseek/deepseek-v4-flash` | 49.0% | 98/200 | 93 | 8 | 184/15 | 1 |
| 19 | `z-ai/glm-5` | 49.0% | 98/200 | 86 | 16 | 170/30 | 0 |
| 20 | `google/gemini-2.5-flash-lite` | 48.5% | 97/200 | 8 | 95 | 13/187 | 0 |
| 21 | `google/gemini-3.5-flash` | 47.0% | 94/200 | 98 | 8 | 190/10 | 0 |
| 22 | `google/gemini-3.1-flash-lite` | 46.5% | 93/200 | 9 | 98 | 11/189 | 0 |
| 23 | `minimax/minimax-m3` | 46.0% | 92/200 | 33 | 72 | 59/138 | 3 |
| 24 | `openai/gpt-3.5-turbo` | 46.0% | 92/200 | 98 | 10 | 188/12 | 0 |
| 25 | `google/gemini-2.5-flash` | 45.5% | 91/200 | 18 | 91 | 27/173 | 0 |
| 26 | `qwen/qwen3.7-plus` | 44.5% | 89/200 | 41 | 70 | 71/129 | 0 |
| 27 | `google/gemini-3.1-pro-preview` | 42.5% | 85/200 | 67 | 43 | 121/74 | 5 |
| 28 | `qwen/qwen3.6-27b` | 38.0% | 76/200 | 91 | 33 | 158/42 | 0 |
| 29 | `anthropic/claude-opus-4.7` | 37.0% | 74/200 | 92 | 34 | 158/42 | 0 |
| 30 | `qwen/qwen3.6-plus` | 36.0% | 72/200 | 71 | 57 | 114/86 | 0 |
| 31 | `openai/gpt-4o` | 34.0% | 68/200 | 51 | 81 | 70/130 | 0 |
| 32 | `openai/gpt-4.1` | 23.0% | 46/200 | 99 | 55 | 144/56 | 0 |

![Model scores](model_scores.png)

## License

This dataset is released under the Apache License 2.0. See `LICENSE` for the full license text.

## Citation

If you use this benchmark, cite the dataset repository:

```bibtex
@dataset{humannumbereval,
  title = {HumanNumberEval},
  description = {A benchmark for classifying human-chosen numbers versus RNG-generated numbers},
  year = {2026}
}
```