\begin{table*}[htp]
\resizebox{\textwidth}{!}{
\begin{tabular}{lrrrrrr}
\toprule
Model/adapter & Correct Answer & # eval & # train & truncated & # prompt tokens & # output tokens \\
\midrule
Llama 3 Instruct (70B) & 0.56 & 100.0 & 5.0 &  & 5154.91 & 89.91 \\
Llama 3 Instruct (8B) & 0.19 & 100.0 & 5.0 &  & 5154.91 & 116.62 \\
Mistral Instruct v0.3 (7B) & 0.41 & 100.0 & 5.0 &  & 7465.93 & 88.7 \\
Mixtral Instruct (8x22B) & 0.49 & 100.0 & 5.0 &  & 7465.93 & 71.7 \\
Mixtral Instruct (8x7B) & 0.47 & 100.0 & 1.8 &  & 3295.33 & 83.56 \\
Qwen2 Instruct (72B) & 0.54 & 100.0 & 5.0 &  & 6221.57 & 59.53 \\
Claude 3 Haiku (20240307) & 0.6 & 100.0 & 5.0 &  & 5654.76 & 125.59 \\
Claude 3 Sonnet (20240229) & 0.64 & 100.0 & 5.0 &  & 5654.76 & 118.5 \\
Claude 3 Opus (20240229) & 0.68 & 100.0 & 5.0 &  & 5654.76 & 104.29 \\
Claude 3.5 Sonnet (20240620) & 0.63 & 100.0 & 5.0 &  & 5654.76 & 89.9 \\
Gemini 1.5 Pro (001) & 0.56 & 100.0 & 5.0 &  & 6790.49 &  \\
Gemini 1.5 Flash (001) & 0.57 & 100.0 & 5.0 &  & 6790.49 &  \\
GPT-3.5 Turbo (0125) & 0.6 & 100.0 & 5.0 &  & 5154.93 & 21.74 \\
GPT-4 Turbo (2024-04-09) & 0.6 & 100.0 & 5.0 &  & 5154.93 & 28.85 \\
GPT-4o (2024-05-13) & 0.65 & 100.0 & 5.0 &  & 5098.93 & 18.38 \\
GPT-4o mini (2024-07-18) & 0.53 & 100.0 & 5.0 &  & 5098.93 & 30.04 \\
\bottomrule
\end{tabular}}
\caption{Results for financebench_ (financebench)}
\label{fig:financebench_ (financebench)}
\end{table*}