diff --git a/bash/case/api_test_config.env.example b/bash/case/api_test_config.env.example
new file mode 100644
index 0000000..dcb76bc
--- /dev/null
+++ b/bash/case/api_test_config.env.example
@@ -0,0 +1,24 @@
+# 复制为 api_test_config.env 后填入真实 key，再 source api_test_config.env
+
+# API 认证
+export EVAL_API_KEY="sk-xxxx"
+export EVAL_API_URL="https://api.vectron.meta-stone.com/v1"
+
+# 模型名（以 API 服务商的 model id 为准）
+export EVAL_MODEL="DeepSeek/DeepSeek-V4-Flash"
+
+# 评测数据集，逗号分隔，想测什么改这里
+# 可用参考：gsm8k,aime24,aime25,aime26,hmmt26,imo_answerbench,competition_math,bbh,drop
+#            gpqa_diamond,mmlu_pro,simple_qa,mmlu,cmmlu,arc,hellaswag,trivia_qa,winogrande
+#            longbench_v2,openai_mrcr,general_fc,bfcl_v3,bigcodebench,humaneval,live_code_bench
+export EVAL_DATASETS="gsm8k,aime24,arc"
+
+# 输出目录标识
+export EVAL_FOLDER_NAME="API-Test"
+
+# 评测配置
+export EVAL_CONFIG="config/dpv4-int8_nothinking.yaml"
+export EVAL_BATCH_SIZE=4
+export EVAL_LIMIT="none"
+export EVAL_SEED=42
+export EVAL_THINKING="false"
diff --git a/bash/case/api_test_runner.sh b/bash/case/api_test_runner.sh
new file mode 100755
index 0000000..467cc96
--- /dev/null
+++ b/bash/case/api_test_runner.sh
@@ -0,0 +1,121 @@
+#!/bin/bash
+# ============================================================
+# 通用 API 评测脚本
+# 用法:
+#   # 方式 1：通过环境变量配置
+#   export EVAL_API_KEY="sk-xxxx"
+#   export EVAL_API_URL="https://api.example.com/v1"
+#   export EVAL_MODEL="gpt-4o"
+#   export EVAL_DATASETS="gsm8k,aime24,arc"
+#   bash bash/case/api_test_runner.sh
+#
+#   # 方式 2：命令行参数覆盖
+#   bash bash/case/api_test_runner.sh \
+#       --api-key sk-xxxx \
+#       --api-url https://api.example.com/v1 \
+#       --model gpt-4o \
+#       --datasets gsm8k,aime24,arc
+#
+# 修改 datasets 只需改 EVAL_DATASETS 或 --datasets。
+# ============================================================
+
+set -euo pipefail
+
+ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/../.." && pwd)"
+cd "$ROOT_DIR"
+
+# --------------------------------------------------
+# 默认值（可通过环境变量或命令行覆盖）
+# --------------------------------------------------
+API_KEY="${EVAL_API_KEY:-}"
+API_URL="${EVAL_API_URL:-https://api.vectron.meta-stone.com/v1}"
+MODEL="${EVAL_MODEL:-DeepSeek/DeepSeek-V4-Flash}"
+DATASETS="${EVAL_DATASETS:-gsm8k,aime24,arc}"
+FOLDER_NAME="${EVAL_FOLDER_NAME:-API-Test}"
+CONFIG="${EVAL_CONFIG:-config/dpv4-int8_nothinking.yaml}"
+BATCH_SIZE="${EVAL_BATCH_SIZE:-4}"
+LIMIT="${EVAL_LIMIT:-none}"
+SEED="${EVAL_SEED:-42}"
+THINKING="${EVAL_THINKING:-false}"
+DATASET_DIR="${EVAL_DATASET_DIR:-$ROOT_DIR}"
+OUTPUT_DIR="${EVAL_OUTPUT_DIR:-$ROOT_DIR/output}"
+
+# --------------------------------------------------
+# 解析命令行参数
+# --------------------------------------------------
+while [[ $# -gt 0 ]]; do
+    case "$1" in
+        --api-key) API_KEY="$2"; shift 2 ;;
+        --api-url) API_URL="$2"; shift 2 ;;
+        --model) MODEL="$2"; shift 2 ;;
+        --datasets) DATASETS="$2"; shift 2 ;;
+        --folder-name) FOLDER_NAME="$2"; shift 2 ;;
+        --config) CONFIG="$2"; shift 2 ;;
+        --batch-size) BATCH_SIZE="$2"; shift 2 ;;
+        --limit) LIMIT="$2"; shift 2 ;;
+        --seed) SEED="$2"; shift 2 ;;
+        --thinking) THINKING="true"; shift ;;
+        --no-thinking) THINKING="false"; shift ;;
+        --dataset-dir) DATASET_DIR="$2"; shift 2 ;;
+        --output-dir) OUTPUT_DIR="$2"; shift 2 ;;
+        -h|--help)
+            grep '^# ' "$0" | sed 's/^# //'
+            exit 0
+            ;;
+        *) echo "未知参数: $1"; exit 1 ;;
+    esac
+done
+
+if [[ -z "$API_KEY" ]]; then
+    echo "ERROR: 请设置 EVAL_API_KEY 环境变量或传入 --api-key"
+    exit 1
+fi
+
+export EVALSCOPE_API_KEY="$API_KEY"
+export OPENAI_API_KEY="$API_KEY"
+
+# --------------------------------------------------
+# API key 连通性校验
+# --------------------------------------------------
+HEALTH=$(curl -s -o /dev/null -w "%{http_code}" \
+    -H "Authorization: Bearer ${API_KEY}" \
+    "${API_URL}/models")
+if [[ "$HEALTH" != "200" ]]; then
+    echo "ERROR: API key 校验失败，${API_URL}/models 返回 HTTP $HEALTH"
+    exit 1
+fi
+echo "API key 校验通过 (${API_URL})"
+
+# --------------------------------------------------
+# 组装 run.py 参数
+# --------------------------------------------------
+ARGS=(
+    --model "$MODEL"
+    --api-url "$API_URL"
+    --dataset-dir "$DATASET_DIR"
+    --output-dir "$OUTPUT_DIR"
+    --folder-name "$FOLDER_NAME"
+    --config "$CONFIG"
+    --batch-size "$BATCH_SIZE"
+    --seed "$SEED"
+    --limit "$LIMIT"
+    --datasets "$DATASETS"
+)
+
+if [[ "$THINKING" == "true" ]]; then
+    ARGS+=(--thinking)
+fi
+
+echo "============================================================"
+echo "API 评测启动"
+echo "Model:        $MODEL"
+echo "API URL:      $API_URL"
+echo "Datasets:     $DATASETS"
+echo "Folder:       $FOLDER_NAME"
+echo "Config:       $CONFIG"
+echo "Batch size:   $BATCH_SIZE"
+echo "Limit:        $LIMIT"
+echo "Thinking:     $THINKING"
+echo "============================================================"
+
+python bash/run.py "${ARGS[@]}"
diff --git a/bash/case/calc_glm52_cost.sh b/bash/case/calc_glm52_cost.sh
new file mode 100755
index 0000000..b205a50
--- /dev/null
+++ b/bash/case/calc_glm52_cost.sh
@@ -0,0 +1,28 @@
+#!/bin/bash
+# ============================================================
+# GLM5.2 API 成本计算脚本
+# 用法: bash bash/case/calc_glm52_cost.sh [结果CSV/Excel]
+# 默认读取最新的 DS4-Flash-INT8 NO-Thinking 2.0 FULL 结果
+# ============================================================
+
+set -euo pipefail
+
+ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/../.." && pwd)"
+cd "$ROOT_DIR"
+
+INPUT="${1:-$ROOT_DIR/P800模型能力评测结果 - DS4-Flash-INT8-NO-Thinking-2.0-FULL.csv}"
+OUTPUT="${2:-$ROOT_DIR/results/P800_benchmark_cost_GLM52.csv}"
+
+# GLM5.2 报价（示例）：输入 8 元/百万 tokens，输出 28 元/百万 tokens，折扣 0.65
+INPUT_PRICE=8
+OUTPUT_PRICE=28
+DISCOUNT=0.65
+MODEL_NAME="GLM-5.2"
+
+python3 tools/calculate_cost.py \
+    --input "$INPUT" \
+    --input-price "$INPUT_PRICE" \
+    --output-price "$OUTPUT_PRICE" \
+    --discount "$DISCOUNT" \
+    --model-name "$MODEL_NAME" \
+    --output "$OUTPUT"
diff --git a/tools/calculate_cost.py b/tools/calculate_cost.py
new file mode 100644
index 0000000..e34ebcc
--- /dev/null
+++ b/tools/calculate_cost.py
@@ -0,0 +1,137 @@
+#!/usr/bin/env python3
+"""
+根据 benchmark 结果计算 API 调用成本。
+
+支持输入：
+  - CSV（如 P800模型能力评测结果 - DS4-Flash-INT8-NO-Thinking-2.0-FULL.csv）
+  - Excel（如 P800模型能力评测结果_统一格式_filled.xlsx）
+
+计价公式：
+  input_cost  = n_samples * input_tokens_mean / 1_000_000 * input_price * discount
+  output_cost = n_samples * output_tokens_mean / 1_000_000 * output_price * discount
+
+价格单位：元 / 百万 tokens（按国内 API 常见报价）。
+"""
+
+import argparse
+import sys
+from pathlib import Path
+
+import pandas as pd
+
+
+def load_input(path: str, sheet: str = None) -> pd.DataFrame:
+    p = Path(path)
+    if not p.exists():
+        raise FileNotFoundError(path)
+
+    if p.suffix.lower() in ('.xlsx', '.xls'):
+        if sheet:
+            return pd.read_excel(path, sheet_name=sheet)
+        xl = pd.ExcelFile(path)
+        print(f"可用 sheet: {xl.sheet_names}")
+        return pd.read_excel(path, sheet_name=xl.sheet_names[0])
+    else:
+        return pd.read_csv(path)
+
+
+def compute_cost(
+    df: pd.DataFrame,
+    input_price: float,
+    output_price: float,
+    discount: float,
+    model_name: str,
+) -> pd.DataFrame:
+    df = df.copy()
+
+    # 兼容中英文列名
+    col_n_samples = next((c for c in df.columns if '样本' in c or c.lower() == 'n_samples'), None)
+    col_input = next((c for c in df.columns if '输入tokens' in c or c.lower() == 'input_tokens_mean'), None)
+    col_output = next((c for c in df.columns if '输出tokens' in c or c.lower() == 'output_tokens_mean'), None)
+    col_total = next((c for c in df.columns if '累计总tokens' in c or c.lower() == 'total_tokens'), None)
+    col_score = next((c for c in df.columns if '得分' in c or c.lower() == 'score'), None)
+    col_bench = next((c for c in df.columns if 'benchmark' in c.lower()), None)
+
+    if col_n_samples is None or col_input is None or col_output is None:
+        raise ValueError("输入文件缺少必要列：总样本数 / 输入tokens_mean / 输出tokens_mean")
+
+    price_in = input_price * discount / 1_000_000.0
+    price_out = output_price * discount / 1_000_000.0
+
+    # 数值化，空值填 0
+    n = pd.to_numeric(df[col_n_samples], errors='coerce').fillna(0)
+    in_tok = pd.to_numeric(df[col_input], errors='coerce').fillna(0)
+    out_tok = pd.to_numeric(df[col_output], errors='coerce').fillna(0)
+
+    df['input_tokens_total'] = (n * in_tok).astype('int64')
+    df['output_tokens_total'] = (n * out_tok).astype('int64')
+    df['input_cost_yuan'] = n * in_tok * price_in
+    df['output_cost_yuan'] = n * out_tok * price_out
+    df['total_cost_yuan'] = df['input_cost_yuan'] + df['output_cost_yuan']
+
+    # 元/千 tokens 便于查看
+    df['input_price_per_1k_tokens'] = price_in * 1000
+    df['output_price_per_1k_tokens'] = price_out * 1000
+    df['model_pricing'] = f"{model_name} (in={input_price}*{-discount}, out={output_price}*{discount})"
+
+    return df
+
+
+def save_output(df: pd.DataFrame, path: str):
+    p = Path(path)
+    p.parent.mkdir(parents=True, exist_ok=True)
+    if p.suffix.lower() in ('.xlsx', '.xls'):
+        with pd.ExcelWriter(path, engine='openpyxl') as writer:
+            df.to_excel(writer, sheet_name='cost', index=False)
+    else:
+        df.to_csv(path, index=False, encoding='utf-8-sig')
+    print(f"结果已保存: {path}")
+
+
+def main():
+    parser = argparse.ArgumentParser(description="根据 benchmark 结果计算 API 成本")
+    parser.add_argument('--input', '-i', required=True, help='输入 CSV/Excel 路径')
+    parser.add_argument('--sheet', '-s', default=None, help='Excel sheet 名（默认第一个）')
+    parser.add_argument('--input-price', type=float, default=8.0, help='输入单价（元/百万 tokens）')
+    parser.add_argument('--output-price', type=float, default=28.0, help='输出单价（元/百万 tokens）')
+    parser.add_argument('--discount', '-d', type=float, default=0.65, help='折扣率，如 0.65')
+    parser.add_argument('--model-name', '-m', default='GLM-5.2', help='模型名称（仅用于标注）')
+    parser.add_argument('--output', '-o', required=True, help='输出 CSV/Excel 路径')
+    args = parser.parse_args()
+
+    df = load_input(args.input, args.sheet)
+    df_cost = compute_cost(
+        df,
+        input_price=args.input_price,
+        output_price=args.output_price,
+        discount=args.discount,
+        model_name=args.model_name,
+    )
+
+    # 汇总行（如果原表没有 totals，追加一个）
+    numeric_cols = ['input_cost_yuan', 'output_cost_yuan', 'total_cost_yuan']
+    total_row = {c: df_cost[c].sum() for c in numeric_cols}
+    total_row['Benchmark'] = 'TOTAL'
+    total_row['model_pricing'] = args.model_name
+
+    # 插入或更新 TOTAL 行
+    is_total = df_cost['Benchmark'].astype(str).str.lower().isin(['total', '总计', '合计'])
+    if is_total.any():
+        for c in numeric_cols:
+            df_cost.loc[is_total, c] = total_row[c]
+    else:
+        total_df = pd.DataFrame([total_row])
+        df_cost = pd.concat([df_cost, total_df], ignore_index=True)
+
+    save_output(df_cost, args.output)
+
+    # 终端摘要
+    summary = df_cost[['Benchmark', '总样本数', '输入tokens_mean', '输出tokens_mean',
+                       'input_cost_yuan', 'output_cost_yuan', 'total_cost_yuan']].copy()
+    print('\n=== 成本摘要（元）===')
+    print(summary.to_string(index=False))
+    print(f"\n总成本: {total_row['total_cost_yuan']:.4f} 元")
+
+
+if __name__ == '__main__':
+    main()
diff --git a/tools/fill_excel_cost.py b/tools/fill_excel_cost.py
new file mode 100644
index 0000000..59d5eff
--- /dev/null
+++ b/tools/fill_excel_cost.py
@@ -0,0 +1,79 @@
+#!/usr/bin/env python3
+"""
+为 P800模型能力评测结果 Excel 的每个 sheet 追加 API 成本列。
+只处理包含 Benchmark + 总样本数 + 输入/输出 tokens_mean 的 sheet。
+"""
+
+import argparse
+from pathlib import Path
+
+import pandas as pd
+
+
+def try_compute_cost(df: pd.DataFrame, input_price: float, output_price: float,
+                     discount: float, model_name: str):
+    col_n = next((c for c in df.columns if '样本' in c and '数' in c), None)
+    col_in = next((c for c in df.columns if '输入tokens' in c), None)
+    col_out = next((c for c in df.columns if '输出tokens' in c), None)
+    col_bench = next((c for c in df.columns if 'benchmark' in c.lower()), None)
+
+    if col_n is None or col_in is None or col_out is None or col_bench is None:
+        return None
+
+    price_in = input_price * discount / 1_000_000.0
+    price_out = output_price * discount / 1_000_000.0
+
+    n = pd.to_numeric(df[col_n], errors='coerce').fillna(0)
+    in_tok = pd.to_numeric(df[col_in], errors='coerce').fillna(0)
+    out_tok = pd.to_numeric(df[col_out], errors='coerce').fillna(0)
+
+    result = df.copy()
+    result['input_cost_元'] = n * in_tok * price_in
+    result['output_cost_元'] = n * out_tok * price_out
+    result['total_cost_元'] = result['input_cost_元'] + result['output_cost_元']
+    result['price_model'] = model_name
+    return result
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--input', '-i', required=True)
+    parser.add_argument('--output', '-o', required=True)
+    parser.add_argument('--input-price', type=float, default=8.0)
+    parser.add_argument('--output-price', type=float, default=28.0)
+    parser.add_argument('--discount', '-d', type=float, default=0.65)
+    parser.add_argument('--model-name', '-m', default='GLM-5.2')
+    args = parser.parse_args()
+
+    xls = pd.ExcelFile(args.input)
+    out_path = Path(args.output)
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+
+    with pd.ExcelWriter(out_path, engine='openpyxl') as writer:
+        for sheet in xls.sheet_names:
+            df = pd.read_excel(args.input, sheet_name=sheet)
+            new_df = try_compute_cost(
+                df, args.input_price, args.output_price,
+                args.discount, args.model_name,
+            )
+            if new_df is not None:
+                # 汇总
+                total_mask = new_df.iloc[:, 0].astype(str).str.lower().isin(['total', '总计', '合计'])
+                if not total_mask.any():
+                    total_row = {'price_model': args.model_name}
+                    for c in new_df.columns:
+                        if 'cost_元' in c:
+                            total_row[c] = new_df[c].sum()
+                    total_df = pd.DataFrame([total_row])
+                    new_df = pd.concat([new_df, total_df], ignore_index=True)
+                sheet_name = f"{sheet}_cost" if len(sheet) <= 25 else sheet[:25]
+                new_df.to_excel(writer, sheet_name=sheet_name, index=False)
+            else:
+                # 原样复制
+                df.to_excel(writer, sheet_name=sheet, index=False)
+
+    print(f"已保存: {out_path}")
+
+
+if __name__ == '__main__':
+    main()