diff --git a/home_assistant_datasets/tool/leaderboard/build.py b/home_assistant_datasets/tool/leaderboard/build.py
index 5f214bc7..dd243b78 100644
--- a/home_assistant_datasets/tool/leaderboard/build.py
+++ b/home_assistant_datasets/tool/leaderboard/build.py
@@ -155,7 +155,7 @@ def create_leaderboard_table(
         for dataset, best_record in dataset_scores.items():
             if best_record.good_percent_value() != 0:
                 ci = 1.96 * best_record.stddev*100
-                row.append(f"{best_record.good_percent_value()*100:0.1f}% <span style=\"font-size:0.5em;\">CI:&nbsp;{ci:0.1f}%&nbsp;{best_record.dataset_label}</span>")
+                row.append(f"{best_record.good_percent_value()*100:0.1f}% (CI:&nbsp;{ci:0.1f}%, {best_record.dataset_label})")
             else:
                 row.append("")
         rows.append(row)
diff --git a/reports/README.md b/reports/README.md
index 548c17f6..7ddae542 100644
--- a/reports/README.md
+++ b/reports/README.md
@@ -1,18 +1,18 @@
 # Home LLM Leaderboard
 | Model | assist (n=80) | assist-mini (n=49) | intents (n=165) |
 | --- | --- | --- | --- |
-| gemini-1.5-flash | 91.2% <span style="font-size:0.5em;">CI:&nbsp;6.2%&nbsp;2024.6.3</span> | 98.0% <span style="font-size:0.5em;">CI:&nbsp;4.0%&nbsp;2024.8.0dev</span> | 63.0% <span style="font-size:0.5em;">CI:&nbsp;7.4%&nbsp;2024.8.0b</span> |
-| gpt-4o-mini | 90.0% <span style="font-size:0.5em;">CI:&nbsp;6.6%&nbsp;2024.8.0b</span> | 98.0% <span style="font-size:0.5em;">CI:&nbsp;4.0%&nbsp;2024.8.0dev</span> | 63.6% <span style="font-size:0.5em;">CI:&nbsp;7.3%&nbsp;2024.8.0b</span> |
-| gpt-4o | 87.5% <span style="font-size:0.5em;">CI:&nbsp;7.2%&nbsp;2024.6.3</span> |  | 81.2% <span style="font-size:0.5em;">CI:&nbsp;6.0%&nbsp;2024.6.3</span> |
-| gpt-3.5 | 75.0% <span style="font-size:0.5em;">CI:&nbsp;9.5%&nbsp;2024.6.3</span> |  | 67.9% <span style="font-size:0.5em;">CI:&nbsp;7.1%&nbsp;2024.6.3</span> |
-| functionary-small-v2.5 | 56.2% <span style="font-size:0.5em;">CI:&nbsp;10.9%&nbsp;2024.7.0</span> | 63.3% <span style="font-size:0.5em;">CI:&nbsp;13.5%&nbsp;2024.8.0dev</span> | 37.6% <span style="font-size:0.5em;">CI:&nbsp;7.4%&nbsp;2024.6.3</span> |
-| llama3.1 | 45.6% <span style="font-size:0.5em;">CI:&nbsp;11.0%&nbsp;2024.8.0b</span> | 83.7% <span style="font-size:0.5em;">CI:&nbsp;10.3%&nbsp;2024.8.0b0</span> | 22.6% <span style="font-size:0.5em;">CI:&nbsp;6.4%&nbsp;2024.8.0b</span> |
-| home-llm | 45.0% <span style="font-size:0.5em;">CI:&nbsp;10.9%&nbsp;2024.6.3</span> | 34.7% <span style="font-size:0.5em;">CI:&nbsp;13.3%&nbsp;2024.8.0dev</span> | 25.5% <span style="font-size:0.5em;">CI:&nbsp;6.6%&nbsp;2024.6.3</span> |
-| assistant | 37.5% <span style="font-size:0.5em;">CI:&nbsp;10.6%&nbsp;2024.6.3</span> | 63.3% <span style="font-size:0.5em;">CI:&nbsp;13.5%&nbsp;2024.8.0dev</span> | 98.8% <span style="font-size:0.5em;">CI:&nbsp;1.7%&nbsp;2024.6.3</span> |
-| xlam-7b | 25.0% <span style="font-size:0.5em;">CI:&nbsp;9.5%&nbsp;2024.8.0b</span> | 85.7% <span style="font-size:0.5em;">CI:&nbsp;9.8%&nbsp;2024.8.0b0</span> |  |
-| llama3-groq-tool-use | 20.0% <span style="font-size:0.5em;">CI:&nbsp;8.8%&nbsp;2024.8.0b</span> | 51.0% <span style="font-size:0.5em;">CI:&nbsp;14.0%&nbsp;2024.8.0b0</span> | 11.5% <span style="font-size:0.5em;">CI:&nbsp;4.9%&nbsp;2024.8.0b</span> |
-| mistral-v3 | 3.8% <span style="font-size:0.5em;">CI:&nbsp;4.2%&nbsp;2024.8.0b</span> | 2.0% <span style="font-size:0.5em;">CI:&nbsp;4.0%&nbsp;2024.8.0dev</span> | 10.3% <span style="font-size:0.5em;">CI:&nbsp;4.6%&nbsp;2024.8.0b</span> |
-| xlam-1b |  | 27.1% <span style="font-size:0.5em;">CI:&nbsp;12.6%&nbsp;2024.8.0b0</span> |  |
+| gemini-1.5-flash | 91.2% (CI:&nbsp;6.2%, 2024.6.3) | 98.0% (CI:&nbsp;4.0%, 2024.8.0dev) | 63.0% (CI:&nbsp;7.4%, 2024.8.0b) |
+| gpt-4o-mini | 90.0% (CI:&nbsp;6.6%, 2024.8.0b) | 98.0% (CI:&nbsp;4.0%, 2024.8.0dev) | 63.6% (CI:&nbsp;7.3%, 2024.8.0b) |
+| gpt-4o | 87.5% (CI:&nbsp;7.2%, 2024.6.3) |  | 81.2% (CI:&nbsp;6.0%, 2024.6.3) |
+| gpt-3.5 | 75.0% (CI:&nbsp;9.5%, 2024.6.3) |  | 67.9% (CI:&nbsp;7.1%, 2024.6.3) |
+| functionary-small-v2.5 | 56.2% (CI:&nbsp;10.9%, 2024.7.0) | 63.3% (CI:&nbsp;13.5%, 2024.8.0dev) | 37.6% (CI:&nbsp;7.4%, 2024.6.3) |
+| llama3.1 | 45.6% (CI:&nbsp;11.0%, 2024.8.0b) | 83.7% (CI:&nbsp;10.3%, 2024.8.0b0) | 22.6% (CI:&nbsp;6.4%, 2024.8.0b) |
+| home-llm | 45.0% (CI:&nbsp;10.9%, 2024.6.3) | 34.7% (CI:&nbsp;13.3%, 2024.8.0dev) | 25.5% (CI:&nbsp;6.6%, 2024.6.3) |
+| assistant | 37.5% (CI:&nbsp;10.6%, 2024.6.3) | 63.3% (CI:&nbsp;13.5%, 2024.8.0dev) | 98.8% (CI:&nbsp;1.7%, 2024.6.3) |
+| xlam-7b | 25.0% (CI:&nbsp;9.5%, 2024.8.0b) | 85.7% (CI:&nbsp;9.8%, 2024.8.0b0) |  |
+| llama3-groq-tool-use | 20.0% (CI:&nbsp;8.8%, 2024.8.0b) | 51.0% (CI:&nbsp;14.0%, 2024.8.0b0) | 11.5% (CI:&nbsp;4.9%, 2024.8.0b) |
+| mistral-v3 | 3.8% (CI:&nbsp;4.2%, 2024.8.0b) | 2.0% (CI:&nbsp;4.0%, 2024.8.0dev) | 10.3% (CI:&nbsp;4.6%, 2024.8.0b) |
+| xlam-1b |  | 27.1% (CI:&nbsp;12.6%, 2024.8.0b0) |  |
 
 Implementation notes:
 - CI is large given small number of samples in the datasets.