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# Leaderboards

<span class="viewport-desktop">Choose a leaderboard from the menu on the left to see the
results.</span><span class="viewport-mobile">👆 Choose a leaderboard from the top
left menu to see the results.</span>

## 🏷️ Types of Leaderboards

Each language has two leaderboards:

- **Generative Leaderboard**: This leaderboard shows the performance of models that can
  generate text. These models have been evaluated on _all_ [tasks](/tasks), both NLU and
  NLG.
- **NLU Leaderboard**: This leaderboard shows the performance of models that can
  understand text, which includes both generative and non-generative models.

## 📊 How to Read the Leaderboards

The main score column is the `Rank score`, showing the
[mean rank score](/methodology) of the model across all the tasks in the leaderboard.
The lower the score, the better the model. The `Rank` column to the left is a dense
ordinal ranking derived from the rank score (see the methodology page for how ties
are decided).

The columns that follow the rank columns are metadata about the model:

- `Type`: The type of model:
  - 🔍 indicates that it is an encoder model (e.g., BERT)
  - 🧠 indicates that it is a base generative model (e.g., GPT-2)
  - 📝 indicates that it is an instruction-tuned model (e.g., ChatGPT)
  - 🤔 indicates that it is a reasoning model (e.g., o1)
- `Parameters`: The total number of parameters in the model, in millions.
- `Vocabulary`: The size of the model's vocabulary, in thousands.
- `Context`: The maximum number of tokens that the model can process at a time.
- `Commercial`: Whether the model can be used for commercial purposes. See [here](/faq)
  for more information.
- `Merge`: Whether the model is a merge of other models.

After these metadata columns, the individual scores for each dataset is shown. Each
dataset has a primary and secondary score - see what these are on the [task
page](/tasks). Lastly, the final columns show the EuroEval version used to benchmark
the given model on each of the datasets.

To read more about the individual datasets, see the [datasets](/datasets) page. If
you're interested in the methodology behind the benchmark, see the
[methodology](/methodology) page.
