> ## Documentation Index
> Fetch the complete documentation index at: https://arize-ax.mintlify.site/docs/llms.txt
> Use this file to discover all available pages before exploring further.
# Compare experiments
The **Compare Experiments** feature helps you identify meaningful improvements in performance across experiments so you can decide which experiment to move forward to. This enables:
* **Faster iteration** : Quickly spot where performance diverges without manual guesswork.
* **Evidence-based decisions:** Confirm if improvements are real, significant, or just noise.
* **Understand trade-offs:** See if gains come with costs to balance accuracy, speed, & token usage.
# 1. Select Experiments to Compare
Select the experiments you want to analyze and click **Compare Experiments** to view runs side by side.
* See outputs, evaluator results, and metadata together for direct comparison.
* Choose only the columns that matter most and hide the rest.
* Use **Table View** for detailed text results or **Charting View** to visualize evaluator outputs across runs.
* For classification tasks, you can also enable [binary classification metrics](/ax/develop/datasets-and-experiments/experiment-classification-metrics) to compute F1, Accuracy, Precision, and Recall across experiments.
# 2. Enable Diff Mode
Turn on **Diff Mode** to quickly see where runs improved or regressed against a baseline.
* Select a baseline experiment to measure other runs against.
* Differences in evaluator results are highlighted, making improvements and failures easy to spot.
* Aggregated metrics at the top show how each evaluation measure changed relative to the baseline.
This makes it faster to identify meaningful changes without scanning through every result.
# 3. Enable Diff Output Mode
Enable **Diff Output Mode** to visually compare the textual outputs of experiments.
* Highlights insertions, deletions, and changes compared to a baseline experiment.
* Makes it easy to see how model responses evolve across runs.
* Useful for spotting subtle but important output differences that metrics alone may miss.
This helps you understand not just whether results changed, but exactly **how** they changed.
