diff --git a/packages/cli/src/commands/advisor/recommend.ts b/packages/cli/src/commands/advisor/recommend.ts
index 46ee0a8..121c15a 100644
--- a/packages/cli/src/commands/advisor/recommend.ts
+++ b/packages/cli/src/commands/advisor/recommend.ts
@@ -29,41 +29,41 @@ function formatContextWindow(tokens: number): string {
 }
 
 const MODALITY_LABELS: Record<string, string> = {
-  Text: "文本",
-  Image: "图片",
-  Video: "视频",
-  Audio: "音频",
+  Text: "Text",
+  Image: "Image",
+  Video: "Video",
+  Audio: "Audio",
 };
 const CAPABILITY_LABELS: Record<string, string> = {
-  TG: "文本生成",
-  VU: "视觉理解",
-  IG: "图像生成",
-  VG: "视频生成",
-  TTS: "语音合成",
-  ASR: "语音识别",
-  Reasoning: "推理",
+  TG: "Text Gen",
+  VU: "Vision",
+  IG: "Image Gen",
+  VG: "Video Gen",
+  TTS: "Text-to-Speech",
+  ASR: "Speech-to-Text",
+  Reasoning: "Reasoning",
 };
 const BUDGET_LABELS: Record<string, string> = {
-  low: "低成本优先",
-  medium: "适中",
-  high: "高投入",
+  low: "Cost-Effective",
+  medium: "Balanced",
+  high: "High Investment",
 };
 const QUALITY_LABELS: Record<string, string> = {
-  flagship: "旗舰优先",
-  balanced: "均衡",
-  "cost-optimized": "性价比优先",
+  flagship: "Flagship",
+  balanced: "Balanced",
+  "cost-optimized": "Value",
 };
 const PREFERENCE_MODE_LABELS: Record<string, string> = {
-  scoped: "限定范围",
-  comparison: "对比评估",
-  alternative: "替代推荐",
+  scoped: "Scoped",
+  comparison: "Comparison",
+  alternative: "Alternative",
 };
 
 function formatIntentSummary(intent: IntentProfile, noColor: boolean): string {
   const colorize = noColor ? new Chalk({ level: 0 }) : chalk;
 
   const lines: string[] = [];
-  lines.push(colorize.cyan.bold("需求理解"));
+  lines.push(colorize.cyan.bold("Intent Analysis"));
 
   if (intent.taskSummary) {
     lines.push("");
@@ -72,7 +72,7 @@ function formatIntentSummary(intent: IntentProfile, noColor: boolean): string {
 
   if (intent.scenarioHints.length) {
     lines.push("");
-    lines.push(`${colorize.dim("场景特征")}  ${intent.scenarioHints.join(" · ")}`);
+    lines.push(`${colorize.dim("Scenario")}  ${intent.scenarioHints.join(" · ")}`);
   }
 
   const inputLabels = intent.inputModality.map((mod) => MODALITY_LABELS[mod] ?? mod);
@@ -80,40 +80,40 @@ function formatIntentSummary(intent: IntentProfile, noColor: boolean): string {
   if (inputLabels.length || outputLabels.length) {
     lines.push("");
     const parts: string[] = [];
-    if (inputLabels.length) parts.push(`${colorize.dim("输入")} ${inputLabels.join(", ")}`);
-    if (outputLabels.length) parts.push(`${colorize.dim("输出")} ${outputLabels.join(", ")}`);
+    if (inputLabels.length) parts.push(`${colorize.dim("Input")} ${inputLabels.join(", ")}`);
+    if (outputLabels.length) parts.push(`${colorize.dim("Output")} ${outputLabels.join(", ")}`);
     lines.push(parts.join("    "));
   }
 
   const capLabels = intent.requiredCapabilities.map((cap) => CAPABILITY_LABELS[cap] ?? cap);
   if (capLabels.length) {
-    lines.push(`${colorize.dim("所需能力")}  ${capLabels.join(", ")}`);
+    lines.push(`${colorize.dim("Capabilities")}  ${capLabels.join(", ")}`);
   }
 
   const budgetLabel = BUDGET_LABELS[intent.budget] ?? intent.budget;
   const qualityLabel = QUALITY_LABELS[intent.qualityPreference] ?? intent.qualityPreference;
   lines.push("");
   lines.push(
-    `${colorize.dim("预算倾向")} ${budgetLabel}    ${colorize.dim("质量偏好")} ${qualityLabel}`,
+    `${colorize.dim("Budget")} ${budgetLabel}    ${colorize.dim("Quality")} ${qualityLabel}`,
   );
 
   const preference = intent.modelPreference;
   if (preference && preference.mode !== "unconstrained") {
     lines.push("");
     const modeLabel = PREFERENCE_MODE_LABELS[preference.mode] ?? preference.mode;
-    const prefParts = [colorize.dim("推荐模式") + ` ${colorize.yellow(modeLabel)}`];
+    const prefParts = [colorize.dim("Mode") + ` ${colorize.yellow(modeLabel)}`];
     if (preference.targets?.length) {
-      prefParts.push(colorize.dim("目标") + ` ${preference.targets.join(", ")}`);
+      prefParts.push(colorize.dim("Targets") + ` ${preference.targets.join(", ")}`);
     }
     if (preference.excludes?.length) {
-      prefParts.push(colorize.dim("排除") + ` ${preference.excludes.join(", ")}`);
+      prefParts.push(colorize.dim("Excludes") + ` ${preference.excludes.join(", ")}`);
     }
     lines.push(prefParts.join("    "));
   }
 
   if (intent.segments?.length) {
     lines.push("");
-    lines.push(colorize.dim("任务拆解"));
+    lines.push(colorize.dim("Pipeline"));
     for (const [idx, segment] of intent.segments.entries()) {
       const outMods = segment.outputModality.map((mod) => MODALITY_LABELS[mod] ?? mod).join(", ");
       lines.push(
@@ -131,19 +131,19 @@ function formatIntentSummary(intent: IntentProfile, noColor: boolean): string {
   });
 }
 
-const RECOMMEND_LABELS = ["最佳推荐", "次优选择", "备选参考"];
+const RECOMMEND_LABELS = ["Best Pick", "Runner-Up", "Alternative"];
 
 function renderCard(rec: RecommendedModel, index: number, colorize: ChalkInstance): string {
   const labelColors = [colorize.green.bold, colorize.blue.bold, colorize.magenta.bold];
   const colorFn = labelColors[index] ?? colorize.white.bold;
-  const label = RECOMMEND_LABELS[index] ?? `推荐 #${index + 1}`;
+  const label = RECOMMEND_LABELS[index] ?? `#${index + 1}`;
 
   const lines: string[] = [];
-  lines.push(colorFn(`⬢ 推荐 #${index + 1} — ${label}`));
+  lines.push(colorFn(`⬢  #${index + 1} — ${label}`));
   lines.push("");
   lines.push(`${colorize.bold(rec.name)}  ${colorize.dim(`(${rec.model})`)}`);
   lines.push("");
-  lines.push(`${colorize.cyan("推荐理由")}  ${rec.reason}`);
+  lines.push(`${colorize.cyan("Why")}  ${rec.reason}`);
 
   if (rec.highlights.length) {
     lines.push("");
@@ -153,8 +153,8 @@ function renderCard(rec: RecommendedModel, index: number, colorize: ChalkInstanc
   }
 
   const meta: string[] = [];
-  if (rec.contextWindow) meta.push(`上下文 ${formatContextWindow(rec.contextWindow)}`);
-  if (rec.maxOutputTokens) meta.push(`最大输出 ${formatContextWindow(rec.maxOutputTokens)}`);
+  if (rec.contextWindow) meta.push(`Context ${formatContextWindow(rec.contextWindow)}`);
+  if (rec.maxOutputTokens) meta.push(`Max Output ${formatContextWindow(rec.maxOutputTokens)}`);
   if (meta.length) {
     lines.push("");
     lines.push(colorize.dim(meta.join("  ·  ")));
@@ -163,7 +163,7 @@ function renderCard(rec: RecommendedModel, index: number, colorize: ChalkInstanc
   const docLink = buildDocLink(rec.docUrl);
   if (docLink) {
     lines.push("");
-    lines.push(colorize.dim(`文档  ${docLink}`));
+    lines.push(colorize.dim(`Docs  ${docLink}`));
   }
 
   return boxen(lines.join("\n"), {
@@ -183,7 +183,7 @@ function formatSingleResult(results: RecommendedModel[], noColor: boolean): stri
 function formatPipelineResult(summary: string, steps: PipelineStep[], noColor: boolean): string {
   const colorize = noColor ? new Chalk({ level: 0 }) : chalk;
   const lines: string[] = [];
-  lines.push(`  ${colorize.yellow.bold("⚡ 组合方案")}  ${summary}`);
+  lines.push(`  ${colorize.yellow.bold("⚡ Pipeline")}  ${summary}`);
 
   for (const [stepIdx, { step, recommendations, warnings }] of steps.entries()) {
     lines.push("");
@@ -247,14 +247,14 @@ export default defineCommand({
 
     if (!userInput.trim()) {
       if (isInteractive({ nonInteractive: config.nonInteractive })) {
-        const hint = await promptText({ message: "描述你的需求:" });
+        const hint = await promptText({ message: "Describe your requirement:" });
         if (!hint) {
-          process.stderr.write("已取消。\n");
+          process.stderr.write("Cancelled.\n");
           process.exit(1);
         }
         userInput = hint;
       } else {
-        failIfMissing("message", 'bl advisor recommend "你的需求"');
+        failIfMissing("message", 'bl advisor recommend "your requirement"');
       }
     }
 
@@ -262,16 +262,16 @@ export default defineCommand({
     const format = detectOutputFormat(config.output);
 
     const modelsOptions: GetModelsOptions = {
-      onPrepareStart: () => process.stderr.write("初始化中...\n"),
+      onPrepareStart: () => process.stderr.write("Initializing model data...\n"),
     };
-    process.stderr.write("正在分析需求...\n");
+    process.stderr.write("Analyzing your request...\n");
     const [allModels, intent] = await Promise.all([
       getModels(config, modelsOptions),
       analyzeIntent(config, userInput),
     ]);
 
     if (intent.confidence === 0) {
-      process.stderr.write("需求分析超时，使用默认参数继续...\n");
+      process.stderr.write("Intent analysis timed out, using defaults...\n");
     } else {
       process.stderr.write("\n");
     }
@@ -297,7 +297,7 @@ export default defineCommand({
     }
 
     // Stage 3: LLM Ranking
-    const spinner = createSpinner("正在推荐最佳模型...");
+    const spinner = createSpinner("Recommending best models...");
     spinner.start();
 
     const result = await rankModels(config, candidates, intent, userInput, top);
@@ -305,12 +305,31 @@ export default defineCommand({
     spinner.stop();
 
     if (isEmptyResult(result)) {
-      emitBare("暂无满足该需求的模型。");
+      emitBare("No suitable models found for this request.");
       return;
     }
 
     if (format !== "text") {
-      emitResult(result, format);
+      emitResult(
+        {
+          intent: {
+            taskSummary: intent.taskSummary,
+            scenarioHints: intent.scenarioHints,
+            complexity: intent.complexity,
+            inputModality: intent.inputModality,
+            outputModality: intent.outputModality,
+            requiredCapabilities: intent.requiredCapabilities,
+            budget: intent.budget,
+            qualityPreference: intent.qualityPreference,
+            modelPreference:
+              intent.modelPreference?.mode !== "unconstrained" ? intent.modelPreference : undefined,
+            segments: intent.segments,
+          },
+          result,
+          candidates: candidates.length,
+        },
+        format,
+      );
       return;
     }
 
diff --git a/packages/cli/src/commands/quota/check.ts b/packages/cli/src/commands/quota/check.ts
index 5c52fa8..7a17509 100644
--- a/packages/cli/src/commands/quota/check.ts
+++ b/packages/cli/src/commands/quota/check.ts
@@ -64,9 +64,9 @@ function formatRatio(usage: number, limit: number): string {
 function getStatus(usage: number, limit: number): string {
   if (limit <= 0) return "-";
   const pct = (usage / limit) * 100;
-  if (pct >= 100) return "已限流";
-  if (pct >= 80) return "接近限流";
-  return "正常";
+  if (pct >= 100) return "Throttled";
+  if (pct >= 80) return "Near Limit";
+  return "Normal";
 }
 
 function getNestedRecord(
@@ -193,7 +193,6 @@ function printTable(rows: CheckRow[], noColor: boolean): void {
   const yellow = noColor ? (t: string) => t : (t: string) => `\x1b[33m${t}\x1b[0m`;
   const red = noColor ? (t: string) => t : (t: string) => `\x1b[31m${t}\x1b[0m`;
 
-  const headersCn = ["模型", "RPM 用量/限额", "TPM 用量/限额", "状态"];
   const headersEn = ["Model", "RPM Usage/Limit", "TPM Usage/Limit", "Status"];
 
   const tableRows = rows.map((r) => {
@@ -215,36 +214,30 @@ function printTable(rows: CheckRow[], noColor: boolean): void {
     return;
   }
 
-  const widths = headersCn.map((label, col) =>
-    Math.max(
-      displayWidth(label),
-      displayWidth(headersEn[col]),
-      ...tableRows.map((r) => displayWidth(r.cells[col])),
-    ),
+  const widths = headersEn.map((label, col) =>
+    Math.max(displayWidth(label), ...tableRows.map((r) => displayWidth(r.cells[col]))),
   );
 
-  const cnLine = headersCn.map((label, col) => bold(padEnd(label, widths[col]))).join("  ");
-  const enLine = headersEn.map((label, col) => dim(padEnd(label, widths[col]))).join("  ");
+  const headerLine = headersEn.map((label, col) => bold(padEnd(label, widths[col]))).join("  ");
   const separator = widths.map((w) => dim("─".repeat(w))).join("──");
 
-  process.stdout.write(cnLine + "\n");
-  process.stdout.write(enLine + "\n");
+  process.stdout.write(headerLine + "\n");
   process.stdout.write(separator + "\n");
 
   const statusCol = 3;
   for (const r of tableRows) {
     const cells = r.cells.map((cell, col) => {
       if (col === statusCol) {
-        if (cell === "已限流") return red(padEnd(cell, widths[col]));
-        if (cell === "接近限流") return yellow(padEnd(cell, widths[col]));
-        if (cell === "正常") return green(padEnd(cell, widths[col]));
+        if (cell === "Throttled") return red(padEnd(cell, widths[col]));
+        if (cell === "Near Limit") return yellow(padEnd(cell, widths[col]));
+        if (cell === "Normal") return green(padEnd(cell, widths[col]));
       }
       return padEnd(cell, widths[col]);
     });
     process.stdout.write(cells.join("  ") + "\n");
   }
 
-  process.stdout.write(dim(`\n共 ${rows.length} 个模型 (Total: ${rows.length})`) + "\n");
+  process.stdout.write(dim(`\nTotal: ${rows.length} models`) + "\n");
 }
 
 export default defineCommand({
diff --git a/packages/cli/src/commands/quota/history.ts b/packages/cli/src/commands/quota/history.ts
index 99dd510..a5f2081 100644
--- a/packages/cli/src/commands/quota/history.ts
+++ b/packages/cli/src/commands/quota/history.ts
@@ -65,7 +65,6 @@ function printTable(records: LimitApplicationItem[], noColor: boolean, total: nu
   const bold = noColor ? (t: string) => t : (t: string) => `\x1b[1m${t}\x1b[0m`;
   const dim = noColor ? (t: string) => t : (t: string) => `\x1b[2m${t}\x1b[0m`;
 
-  const headersCn = ["模型", "Token 账号限流", "申请时间"];
   const headersEn = ["Model", "Token Limit", "Applied At"];
 
   const rows = records.map((r) => [
@@ -74,27 +73,21 @@ function printTable(records: LimitApplicationItem[], noColor: boolean, total: nu
     formatDateTime(r.gmtCreate),
   ]);
 
-  const widths = headersCn.map((label, col) =>
-    Math.max(
-      displayWidth(label),
-      displayWidth(headersEn[col]),
-      ...rows.map((row) => displayWidth(row[col])),
-    ),
+  const widths = headersEn.map((label, col) =>
+    Math.max(displayWidth(label), ...rows.map((row) => displayWidth(row[col]))),
   );
 
-  const cnLine = headersCn.map((label, col) => bold(padEnd(label, widths[col]))).join("  ");
-  const enLine = headersEn.map((label, col) => dim(padEnd(label, widths[col]))).join("  ");
+  const headerLine = headersEn.map((label, col) => bold(padEnd(label, widths[col]))).join("  ");
   const separator = widths.map((w) => dim("─".repeat(w))).join("──");
 
-  process.stdout.write(cnLine + "\n");
-  process.stdout.write(enLine + "\n");
+  process.stdout.write(headerLine + "\n");
   process.stdout.write(separator + "\n");
 
   for (const row of rows) {
     process.stdout.write(row.map((cell, col) => padEnd(cell, widths[col])).join("  ") + "\n");
   }
 
-  process.stdout.write(dim(`\n共 ${total} 条记录 (Total: ${total})`) + "\n");
+  process.stdout.write(dim(`\nTotal: ${total} records`) + "\n");
 }
 
 export default defineCommand({
@@ -161,11 +154,6 @@ export default defineCommand({
       throw err;
     }
 
-    if (format === "json") {
-      emitResult(result, format);
-      return;
-    }
-
     const resp = extractResponseData(result as Record<string, unknown>);
     let records = (resp.records as LimitApplicationItem[]) ?? [];
     const total = (resp.items as number) ?? records.length;
@@ -174,6 +162,16 @@ export default defineCommand({
       records = records.filter((r) => r.deployedModel === modelFilter);
     }
 
+    if (format === "json") {
+      const items = records.map((r) => ({
+        model: r.deployedModel,
+        tokenLimit: r.usageLimit,
+        appliedAt: formatDateTime(r.gmtCreate),
+      }));
+      emitResult({ records: items, total: modelFilter ? records.length : total }, format);
+      return;
+    }
+
     if (records.length === 0) {
       process.stdout.write("No quota change history found.\n");
       return;
diff --git a/packages/cli/src/commands/quota/list.ts b/packages/cli/src/commands/quota/list.ts
index 3002757..9b2054d 100644
--- a/packages/cli/src/commands/quota/list.ts
+++ b/packages/cli/src/commands/quota/list.ts
@@ -108,7 +108,6 @@ function printTable(models: ModelWithQpm[], noColor: boolean): void {
   const bold = noColor ? (t: string) => t : (t: string) => `\x1b[1m${t}\x1b[0m`;
   const dim = noColor ? (t: string) => t : (t: string) => `\x1b[2m${t}\x1b[0m`;
 
-  const headersCn = ["模型", "RPM", "TPM", "可设上限 TPM"];
   const headersEn = ["Model", "Req/min", "Token/min", "Max TPM"];
 
   const rows = models.map((m) => {
@@ -135,27 +134,21 @@ function printTable(models: ModelWithQpm[], noColor: boolean): void {
     return;
   }
 
-  const widths = headersCn.map((label, col) =>
-    Math.max(
-      displayWidth(label),
-      displayWidth(headersEn[col]),
-      ...rows.map((row) => displayWidth(row[col])),
-    ),
+  const widths = headersEn.map((label, col) =>
+    Math.max(displayWidth(label), ...rows.map((row) => displayWidth(row[col]))),
   );
 
-  const cnLine = headersCn.map((label, col) => bold(padEnd(label, widths[col]))).join("  ");
-  const enLine = headersEn.map((label, col) => dim(padEnd(label, widths[col]))).join("  ");
+  const headerLine = headersEn.map((label, col) => bold(padEnd(label, widths[col]))).join("  ");
   const separator = widths.map((w) => dim("─".repeat(w))).join("──");
 
-  process.stdout.write(cnLine + "\n");
-  process.stdout.write(enLine + "\n");
+  process.stdout.write(headerLine + "\n");
   process.stdout.write(separator + "\n");
 
   for (const row of rows) {
     process.stdout.write(row.map((cell, col) => padEnd(cell, widths[col])).join("  ") + "\n");
   }
 
-  process.stdout.write(dim(`\n共 ${models.length} 个模型 (Total: ${models.length})`) + "\n");
+  process.stdout.write(dim(`\nTotal: ${models.length} models`) + "\n");
 }
 
 export default defineCommand({
@@ -221,7 +214,25 @@ export default defineCommand({
     }
 
     if (format === "json") {
-      emitResult(models, format);
+      const items = models.map((m) => {
+        const qpm = m.qpmInfo;
+        const modelDefault = qpm?.["model-default"];
+        const userSpec = qpm?.["user-spec"];
+
+        const defaultRPM = calculateRPM(modelDefault);
+        const defaultTPM = calculateTPM(modelDefault);
+        const currentRPM = calculateRPM(userSpec, modelDefault?.count_limit_period) || defaultRPM;
+        const currentTPM = calculateTPM(userSpec, modelDefault?.usage_limit_period) || defaultTPM;
+        const maxTPM = defaultTPM * 2;
+
+        return {
+          model: m.model,
+          rpm: currentRPM > 0 ? currentRPM : null,
+          tpm: currentTPM > 0 ? currentTPM : null,
+          maxTPM: maxTPM > 0 ? maxTPM : null,
+        };
+      });
+      emitResult(items, format);
       return;
     }
 
diff --git a/packages/cli/src/commands/usage/free.ts b/packages/cli/src/commands/usage/free.ts
index 3dd11a0..986e234 100644
--- a/packages/cli/src/commands/usage/free.ts
+++ b/packages/cli/src/commands/usage/free.ts
@@ -78,7 +78,6 @@ function printTable(
   typeMap: Map<string, string>,
   noColor: boolean,
 ): void {
-  const headersCn = ["模型", "类型", "剩余/总量", "使用率", "过期时间", "用完即停"];
   const headersEn = ["Model", "Type", "Remaining/Total", "Usage", "Expires", "Auto-Stop"];
 
   const rows = quotas.map((quota) => {
@@ -102,12 +101,8 @@ function printTable(
     ];
   });
 
-  const widths = headersCn.map((label, col) =>
-    Math.max(
-      displayWidth(label),
-      displayWidth(headersEn[col]),
-      ...rows.map((row) => displayWidth(row[col])),
-    ),
+  const widths = headersEn.map((label, col) =>
+    Math.max(displayWidth(label), ...rows.map((row) => displayWidth(row[col]))),
   );
 
   const dim = noColor ? (text: string) => text : (text: string) => `\x1b[2m${text}\x1b[0m`;
@@ -115,12 +110,10 @@ function printTable(
   const green = noColor ? (text: string) => text : (text: string) => `\x1b[32m${text}\x1b[0m`;
   const yellow = noColor ? (text: string) => text : (text: string) => `\x1b[33m${text}\x1b[0m`;
 
-  const autoStopCol = headersCn.length - 1;
-  const cnLine = headersCn.map((label, col) => bold(padEnd(label, widths[col]))).join("  ");
-  const enLine = headersEn.map((label, col) => dim(padEnd(label, widths[col]))).join("  ");
+  const autoStopCol = headersEn.length - 1;
+  const enLine = headersEn.map((label, col) => bold(padEnd(label, widths[col]))).join("  ");
   const separator = widths.map((width) => dim("─".repeat(width))).join("──");
 
-  process.stdout.write(cnLine + "\n");
   process.stdout.write(enLine + "\n");
   process.stdout.write(separator + "\n");
 
@@ -296,11 +289,6 @@ export default defineCommand({
       }),
     ]);
 
-    if (format === "json") {
-      emitResult(quotaResult, format);
-      return;
-    }
-
     const allQuotas = extractQuotas(quotaResult);
     let quotas = modelFlag
       ? allQuotas
@@ -321,14 +309,44 @@ export default defineCommand({
       quotas.sort((a, b) => (a.quotaValidityPeriod ?? 0) - (b.quotaValidityPeriod ?? 0));
     }
 
+    const stopStatuses = extractFreeTierOnlyStatuses(stopResult);
+    const stopMap = new Map(stopStatuses.map((status) => [status.model, status.freeTierOnly]));
+
+    if (format === "json") {
+      const items = quotas.map((quota) => {
+        const hasQuota = quota.quotaInitTotal != null && quota.quotaTotal != null;
+        const used = hasQuota ? quota.quotaInitTotal - quota.quotaTotal : 0;
+        const stopStatus = stopMap.get(quota.model);
+        const autoStop =
+          quota.quotaStatus === "UNKNOWN"
+            ? "unsupported"
+            : stopStatus === true
+              ? true
+              : stopStatus === false
+                ? false
+                : null;
+        return {
+          model: quota.model,
+          type: typeMap.get(quota.model) || null,
+          remaining: hasQuota ? quota.quotaTotal : null,
+          total: hasQuota ? quota.quotaInitTotal : null,
+          usagePercent:
+            hasQuota && quota.quotaInitTotal > 0
+              ? Math.round((used / quota.quotaInitTotal) * 1000) / 10
+              : null,
+          expires: quota.quotaValidityPeriod ? formatDate(quota.quotaValidityPeriod) : null,
+          autoStop,
+        };
+      });
+      emitResult(items, format);
+      return;
+    }
+
     if (quotas.length === 0) {
       process.stdout.write("No free-tier quota found.\n");
       return;
     }
 
-    const stopStatuses = extractFreeTierOnlyStatuses(stopResult);
-    const stopMap = new Map(stopStatuses.map((status) => [status.model, status.freeTierOnly]));
-
     printTable(quotas, stopMap, typeMap, config.noColor);
   },
 });
diff --git a/packages/cli/src/commands/usage/stats.ts b/packages/cli/src/commands/usage/stats.ts
index fb5074b..7efbcd5 100644
--- a/packages/cli/src/commands/usage/stats.ts
+++ b/packages/cli/src/commands/usage/stats.ts
@@ -155,33 +155,32 @@ function resolveUsageMap(item: ModelStatisticItem): Record<string, number> {
 }
 
 interface UsageLabel {
-  cn: string;
   en: string;
   unit?: string;
 }
 
 const USAGE_KEY_LABELS: Record<string, UsageLabel> = {
-  total_token: { cn: "总 Token", en: "Total Tokens", unit: "tokens" },
-  input_token: { cn: "输入 Token", en: "Input Tokens", unit: "tokens" },
-  output_token: { cn: "输出 Token", en: "Output Tokens", unit: "tokens" },
-  input_token_cache: { cn: "缓存 Token", en: "Cached Tokens", unit: "tokens" },
-  input_token_cache_read: { cn: "缓存读取", en: "Cache Read", unit: "tokens" },
-  input_token_cache_creation: { cn: "缓存创建", en: "Cache Creation", unit: "tokens" },
-  thinking_input_token: { cn: "思考输入", en: "Thinking Input", unit: "tokens" },
-  thinking_output_token: { cn: "思考输出", en: "Thinking Output", unit: "tokens" },
-  text_input_token: { cn: "文本输入", en: "Text Input", unit: "tokens" },
-  purein_text_output_token: { cn: "文本输出", en: "Text Output", unit: "tokens" },
-  embedding_token: { cn: "向量", en: "Embedding", unit: "tokens" },
-  image_number: { cn: "图片数", en: "Images", unit: "张" },
-  video_duration: { cn: "视频时长", en: "Video Duration", unit: "秒" },
-  content_duration: { cn: "音频时长", en: "Audio Duration", unit: "秒" },
-  tts_text_number: { cn: "语音合成", en: "TTS Chars", unit: "字符" },
-  total_token_avg: { cn: "平均 Token/次", en: "Avg Tokens/Req" },
+  total_token: { en: "Total Tokens", unit: "tokens" },
+  input_token: { en: "Input Tokens", unit: "tokens" },
+  output_token: { en: "Output Tokens", unit: "tokens" },
+  input_token_cache: { en: "Cached Tokens", unit: "tokens" },
+  input_token_cache_read: { en: "Cache Read", unit: "tokens" },
+  input_token_cache_creation: { en: "Cache Creation", unit: "tokens" },
+  thinking_input_token: { en: "Thinking Input", unit: "tokens" },
+  thinking_output_token: { en: "Thinking Output", unit: "tokens" },
+  text_input_token: { en: "Text Input", unit: "tokens" },
+  purein_text_output_token: { en: "Text Output", unit: "tokens" },
+  embedding_token: { en: "Embedding", unit: "tokens" },
+  image_number: { en: "Images", unit: "images" },
+  video_duration: { en: "Video Duration", unit: "seconds" },
+  content_duration: { en: "Audio Duration", unit: "seconds" },
+  tts_text_number: { en: "TTS Chars", unit: "chars" },
+  total_token_avg: { en: "Avg Tokens/Req" },
 };
 
 function formatLabel(label: UsageLabel): string {
   const unitSuffix = label.unit ? ` [${label.unit}]` : "";
-  return `${label.cn} (${label.en})${unitSuffix}`;
+  return `${label.en}${unitSuffix}`;
 }
 
 function printOverview(
@@ -195,12 +194,12 @@ function printOverview(
   const dim = noColor ? (text: string) => text : (text: string) => `\x1b[2m${text}\x1b[0m`;
 
   process.stdout.write(
-    `${dim("时间范围 Period:")} ${formatDate(startTime)} ~ ${formatDate(endTime)} ${dim(`(${days} 天)`)}\n\n`,
+    `${dim("Period:")} ${formatDate(startTime)} ~ ${formatDate(endTime)} ${dim(`(${days} days)`)}\n\n`,
   );
 
   const rows: [string, string][] = [
-    ["调用模型数 (Models Called)", formatNumber(stat.modelCount ?? 0)],
-    ["调用成功次数 (Successful Calls)", formatNumber(stat.callSuccessCount ?? 0)],
+    ["Models Called", formatNumber(stat.modelCount ?? 0)],
+    ["Successful Calls", formatNumber(stat.callSuccessCount ?? 0)],
   ];
 
   for (const usage of stat.usages ?? []) {
@@ -226,7 +225,7 @@ function printModelTable(
   const dim = noColor ? (text: string) => text : (text: string) => `\x1b[2m${text}\x1b[0m`;
 
   process.stdout.write(
-    `${dim("时间范围 Period:")} ${formatDate(startTime)} ~ ${formatDate(endTime)} ${dim(`(${days} 天)`)}\n\n`,
+    `${dim("Period:")} ${formatDate(startTime)} ~ ${formatDate(endTime)} ${dim(`(${days} days)`)}\n\n`,
   );
 
   if (items.length === 0) {
@@ -257,15 +256,6 @@ function printModelTable(
     return (idxA === -1 ? 999 : idxA) - (idxB === -1 ? 999 : idxB);
   });
 
-  const headersCn = [
-    "模型",
-    "调用次数",
-    ...orderedKeys.map((key) => {
-      const label = USAGE_KEY_LABELS[key];
-      if (!label) return key;
-      return label.unit ? `${label.cn} [${label.unit}]` : label.cn;
-    }),
-  ];
   const headersEn = [
     "Model",
     "Calls",
@@ -280,20 +270,14 @@ function printModelTable(
     }),
   ]);
 
-  const widths = headersCn.map((label, col) =>
-    Math.max(
-      displayWidth(label),
-      displayWidth(headersEn[col]),
-      ...rows.map((row) => displayWidth(row[col])),
-    ),
+  const widths = headersEn.map((label, col) =>
+    Math.max(displayWidth(label), ...rows.map((row) => displayWidth(row[col]))),
   );
 
-  const cnLine = headersCn.map((label, col) => bold(padEnd(label, widths[col]))).join("  ");
-  const enLine = headersEn.map((label, col) => dim(padEnd(label, widths[col]))).join("  ");
+  const headerLine = headersEn.map((label, col) => bold(padEnd(label, widths[col]))).join("  ");
   const separator = widths.map((width) => dim("─".repeat(width))).join("──");
 
-  process.stdout.write(cnLine + "\n");
-  process.stdout.write(enLine + "\n");
+  process.stdout.write(headerLine + "\n");
   process.stdout.write(separator + "\n");
 
   for (const row of rows) {
@@ -301,7 +285,7 @@ function printModelTable(
     process.stdout.write(cells.join("  ") + "\n");
   }
 
-  process.stdout.write(dim(`\n共 ${items.length} 个模型 (Total: ${items.length})`) + "\n");
+  process.stdout.write(dim(`\nTotal: ${items.length} models`) + "\n");
 }
 
 export default defineCommand({
@@ -391,16 +375,31 @@ export default defineCommand({
       );
 
       const allItems: ModelStatisticItem[] = [];
-      const jsonResults: unknown[] = [];
       for (const result of results) {
         if (!result) continue;
-        jsonResults.push(result);
         const listData = extractListData(result);
         allItems.push(...listData.list);
       }
 
       if (format === "json") {
-        emitResult(jsonResults.length === 1 ? jsonResults[0] : jsonResults, format);
+        const items = allItems.map((item) => {
+          const usage = resolveUsageMap(item);
+          const clean: Record<string, unknown> = {
+            model: item.model,
+            successfulCalls: item.callSuccessCount ?? 0,
+          };
+          for (const [key, val] of Object.entries(usage)) {
+            clean[key] = val;
+          }
+          return clean;
+        });
+        emitResult(
+          {
+            period: { start: formatDate(startTime), end: formatDate(endTime), days: daysFlag },
+            items,
+          },
+          format,
+        );
         return;
       }
 
@@ -425,12 +424,37 @@ export default defineCommand({
         process.exit(1);
       }
 
+      const stat = extractOverviewData(result);
+
       if (format === "json") {
-        emitResult(result, format);
+        if (!stat) {
+          emitResult(
+            {
+              period: { start: formatDate(startTime), end: formatDate(endTime), days: daysFlag },
+              modelsCalled: 0,
+              successfulCalls: 0,
+            },
+            format,
+          );
+          return;
+        }
+        emitResult(
+          {
+            period: { start: formatDate(startTime), end: formatDate(endTime), days: daysFlag },
+            modelsCalled: stat.modelCount ?? 0,
+            successfulCalls: stat.callSuccessCount ?? 0,
+            usages: (stat.usages ?? []).map((u) => ({
+              key: u.key,
+              value: u.value,
+              unit: u.unit,
+              label: USAGE_KEY_LABELS[u.key]?.en ?? u.key,
+            })),
+          },
+          format,
+        );
         return;
       }
 
-      const stat = extractOverviewData(result);
       if (!stat) {
         process.stdout.write("No usage data found.\n");
         return;
diff --git a/packages/cli/src/commands/workspace/list.ts b/packages/cli/src/commands/workspace/list.ts
index b9f6edb..3f9a1e9 100644
--- a/packages/cli/src/commands/workspace/list.ts
+++ b/packages/cli/src/commands/workspace/list.ts
@@ -46,8 +46,7 @@ function printTable(workspaces: WorkspaceInfo[], noColor: boolean): void {
   const dim = noColor ? (text: string) => text : (text: string) => `\x1b[2m${text}\x1b[0m`;
   const green = noColor ? (text: string) => text : (text: string) => `\x1b[32m${text}\x1b[0m`;
 
-  const headersCn = ["空间名称", "Workspace ID", "默认空间"];
-  const headersEn = ["Name", "", "Default"];
+  const headersEn = ["Name", "Workspace ID", "Default"];
 
   const rows = workspaces.map((ws) => [
     ws.agentName,
@@ -55,20 +54,14 @@ function printTable(workspaces: WorkspaceInfo[], noColor: boolean): void {
     ws.defaultAgent ? "Yes" : "-",
   ]);
 
-  const widths = headersCn.map((label, col) =>
-    Math.max(
-      displayWidth(label),
-      displayWidth(headersEn[col]),
-      ...rows.map((row) => displayWidth(row[col])),
-    ),
+  const widths = headersEn.map((label, col) =>
+    Math.max(displayWidth(label), ...rows.map((row) => displayWidth(row[col]))),
   );
 
-  const cnLine = headersCn.map((label, col) => bold(padEnd(label, widths[col]))).join("  ");
-  const enLine = headersEn.map((label, col) => dim(padEnd(label, widths[col]))).join("  ");
+  const headerLine = headersEn.map((label, col) => bold(padEnd(label, widths[col]))).join("  ");
   const separator = widths.map((width) => dim("─".repeat(width))).join("──");
 
-  process.stdout.write(cnLine + "\n");
-  process.stdout.write(enLine + "\n");
+  process.stdout.write(headerLine + "\n");
   process.stdout.write(separator + "\n");
 
   for (const row of rows) {
@@ -79,9 +72,7 @@ function printTable(workspaces: WorkspaceInfo[], noColor: boolean): void {
     process.stdout.write(cells.join("  ") + "\n");
   }
 
-  process.stdout.write(
-    dim(`\n共 ${workspaces.length} 个空间 (Total: ${workspaces.length})`) + "\n",
-  );
+  process.stdout.write(dim(`\nTotal: ${workspaces.length} workspaces`) + "\n");
 }
 
 export default defineCommand({
@@ -117,21 +108,30 @@ export default defineCommand({
       region,
     });
 
-    if (format === "json") {
-      emitResult(result, format);
-      return;
-    }
-
     const resp = extractResponseData(result as Record<string, unknown>);
     const dataArr = resp.data as Record<string, unknown>[] | undefined;
     if (!Array.isArray(dataArr) || dataArr.length === 0) {
-      process.stdout.write("No workspace found.\n");
+      if (format === "json") {
+        emitResult([], format);
+      } else {
+        process.stdout.write("No workspace found.\n");
+      }
       return;
     }
 
     let workspaces = dataArr as unknown as WorkspaceInfo[];
     if (limit > 0) workspaces = workspaces.slice(0, limit);
 
+    if (format === "json") {
+      const items = workspaces.map((ws) => ({
+        workspaceId: ws.workspaceId,
+        name: ws.agentName,
+        default: ws.defaultAgent,
+      }));
+      emitResult(items, format);
+      return;
+    }
+
     printTable(workspaces, config.noColor);
   },
 });
diff --git a/packages/cli/tests/e2e/advisor-recommend.e2e.test.ts b/packages/cli/tests/e2e/advisor-recommend.e2e.test.ts
index cac6328..7f6724e 100644
--- a/packages/cli/tests/e2e/advisor-recommend.e2e.test.ts
+++ b/packages/cli/tests/e2e/advisor-recommend.e2e.test.ts
@@ -2,21 +2,21 @@ import { describe, expect, test } from "vite-plus/test";
 import { isDashScopeE2EReady, parseStdoutJson, runCli } from "./helpers.ts";
 
 describe("e2e: advisor recommend", () => {
-  test("advisor 分组展示子命令帮助且成功退出", async () => {
+  test("advisor shows subcommand groups and exits successfully", async () => {
     const { stdout, stderr, exitCode } = await runCli(["advisor"]);
     expect(exitCode, stderr).toBe(0);
     expect(`${stdout}\n${stderr}`).toMatch(/advisor|recommend/i);
   });
 
-  test("advisor recommend --help 正常退出", async () => {
+  test("advisor recommend --help exits successfully", async () => {
     const { stderr, exitCode } = await runCli(["advisor", "recommend", "--help"]);
     expect(exitCode, stderr).toBe(0);
     expect(stderr).toMatch(/recommend|--message|dry-run/i);
   });
 });
 
-describe.skipIf(!isDashScopeE2EReady())("e2e: advisor recommend（DashScope）", () => {
-  test("advisor recommend 缺少 --message 时打印帮助并退出 (0)", async () => {
+describe.skipIf(!isDashScopeE2EReady())("e2e: advisor recommend (DashScope)", () => {
+  test("advisor recommend without --message prints help and exits", async () => {
     const { stdout, stderr, exitCode } = await runCli([
       "advisor",
       "recommend",
@@ -26,13 +26,13 @@ describe.skipIf(!isDashScopeE2EReady())("e2e: advisor recommend（DashScope）",
     expect(`${stdout}\n${stderr}`).toMatch(/--message|Usage:/i);
   });
 
-  test("advisor recommend --dry-run 输出意图分析和候选列表", async () => {
+  test("advisor recommend --dry-run outputs intent analysis and candidates", async () => {
     const { stdout, stderr, exitCode } = await runCli([
       "advisor",
       "recommend",
       "--dry-run",
       "--message",
-      "我想做一个能理解图片的客服机器人",
+      "I want to build a customer service bot that understands images",
       "--non-interactive",
       "--output",
       "json",
@@ -44,7 +44,7 @@ describe.skipIf(!isDashScopeE2EReady())("e2e: advisor recommend（DashScope）",
       candidateCount?: number;
       candidates?: Array<{ model?: string; score?: number }>;
     }>(stdout);
-    expect(data.userInput).toBe("我想做一个能理解图片的客服机器人");
+    expect(data.userInput).toBe("I want to build a customer service bot that understands images");
     expect(data.intent?.requiredCapabilities).toContain("VU");
     expect(data.intent?.inputModality).toContain("Image");
     expect(data.candidateCount).toBeGreaterThan(0);
@@ -52,40 +52,44 @@ describe.skipIf(!isDashScopeE2EReady())("e2e: advisor recommend（DashScope）",
     expect(data.candidates?.[0]?.score).toBeGreaterThan(0);
   }, 60_000);
 
-  test("advisor recommend 完整推荐流程返回结果", async () => {
+  test("advisor recommend full flow returns results", async () => {
     const { stdout, stderr, exitCode } = await runCli([
       "advisor",
       "recommend",
       "--message",
-      "低成本高并发的在线客服",
+      "low-cost high-concurrency online customer service",
       "--non-interactive",
       "--output",
       "json",
     ]);
     expect(exitCode, stderr).toBe(0);
     const data = parseStdoutJson<{
-      type?: string;
-      recommendations?: Array<{
-        model?: string;
-        name?: string;
-        reason?: string;
-      }>;
+      intent?: { taskSummary?: string };
+      result?: {
+        type?: string;
+        recommendations?: Array<{
+          model?: string;
+          name?: string;
+          reason?: string;
+        }>;
+      };
+      candidates?: number;
     }>(stdout);
-    expect(data.type).toBe("single");
-    expect(data.recommendations?.length).toBeGreaterThan(0);
-    expect(data.recommendations?.[0]?.model).toBeDefined();
-    expect(data.recommendations?.[0]?.reason).toBeDefined();
+    expect(data.result?.type).toBe("single");
+    expect(data.result?.recommendations?.length).toBeGreaterThan(0);
+    expect(data.result?.recommendations?.[0]?.model).toBeDefined();
+    expect(data.result?.recommendations?.[0]?.reason).toBeDefined();
   }, 120_000);
 
-  // ---- 模型偏好：正例 ----
+  // ---- Model preference: positive cases ----
 
-  test("scoped 偏好 — 限定系列时 intent 含 modelPreference.mode=scoped", async () => {
+  test("scoped preference — intent contains modelPreference.mode=scoped when family is specified", async () => {
     const { stdout, stderr, exitCode } = await runCli([
       "advisor",
       "recommend",
       "--dry-run",
       "--message",
-      "deepseek系列中哪个模型最适合用来进行快速推理",
+      "Which model in the deepseek family is best for fast reasoning?",
       "--non-interactive",
       "--output",
       "json",
@@ -103,13 +107,13 @@ describe.skipIf(!isDashScopeE2EReady())("e2e: advisor recommend（DashScope）",
     ).toBe(true);
   }, 60_000);
 
-  test("comparison 偏好 — 对比模型时 intent 含 modelPreference.mode=comparison", async () => {
+  test("comparison preference — intent contains modelPreference.mode=comparison when comparing models", async () => {
     const { stdout, stderr, exitCode } = await runCli([
       "advisor",
       "recommend",
       "--dry-run",
       "--message",
-      "qwen-max和deepseek-v3哪个更适合做代码生成",
+      "Which is better for code generation, qwen-max or deepseek-v3?",
       "--non-interactive",
       "--output",
       "json",
@@ -122,13 +126,13 @@ describe.skipIf(!isDashScopeE2EReady())("e2e: advisor recommend（DashScope）",
     expect(data.intent?.modelPreference?.targets?.length).toBeGreaterThanOrEqual(2);
   }, 60_000);
 
-  test("excludes 偏好 — 排除模型时 intent 识别出 modelPreference", async () => {
+  test("excludes preference — intent detects modelPreference when excluding models", async () => {
     const { stdout, stderr, exitCode } = await runCli([
       "advisor",
       "recommend",
       "--dry-run",
       "--message",
-      "不要qwen，推荐一个适合文本生成的模型",
+      "Not qwen, recommend a model suitable for text generation",
       "--non-interactive",
       "--output",
       "json",
@@ -147,15 +151,15 @@ describe.skipIf(!isDashScopeE2EReady())("e2e: advisor recommend（DashScope）",
     expect(hasExcludes).toBe(true);
   }, 60_000);
 
-  // ---- 模型偏好：反例 ----
+  // ---- Model preference: negative cases ----
 
-  test("无偏好 — 普通需求查询时 intent 不含 modelPreference 或 mode=unconstrained", async () => {
+  test("no preference — intent has no modelPreference or mode=unconstrained for generic queries", async () => {
     const { stdout, stderr, exitCode } = await runCli([
       "advisor",
       "recommend",
       "--dry-run",
       "--message",
-      "我要做一个能理解图片的客服机器人",
+      "I want to build a customer service bot that understands images",
       "--non-interactive",
       "--output",
       "json",
diff --git a/packages/cli/tests/e2e/quota.e2e.test.ts b/packages/cli/tests/e2e/quota.e2e.test.ts
index 19e537d..997f961 100644
--- a/packages/cli/tests/e2e/quota.e2e.test.ts
+++ b/packages/cli/tests/e2e/quota.e2e.test.ts
@@ -96,7 +96,7 @@ describe.skipIf(!isConsoleE2EReady())("e2e: quota（Console）", () => {
     expect(data.data?.input?.supports).toBeUndefined();
   });
 
-  test("quota list 文本输出包含双行表头", async () => {
+  test("quota list 文本输出包含单行英⽂表头", async () => {
     const { stdout, stderr, exitCode } = await runCli([
       "quota",
       "list",
@@ -105,11 +105,9 @@ describe.skipIf(!isConsoleE2EReady())("e2e: quota（Console）", () => {
       "--no-color",
     ]);
     expect(exitCode, stderr).toBe(0);
-    expect(stdout).toContain("模型");
     expect(stdout).toContain("Model");
-    expect(stdout).toContain("RPM");
-    expect(stdout).toContain("TPM");
-    expect(stdout).toContain("可设上限 TPM");
+    expect(stdout).toContain("Req/min");
+    expect(stdout).toContain("Token/min");
     expect(stdout).toContain("Max TPM");
   });
 
@@ -125,7 +123,7 @@ describe.skipIf(!isConsoleE2EReady())("e2e: quota（Console）", () => {
     ]);
     expect(exitCode, stderr).toBe(0);
     expect(stdout).toContain("qwen3.6-plus");
-    expect(stdout).toMatch(/共 1 个模型/);
+    expect(stdout).toMatch(/Total: 1 models/);
   });
 
   test("quota list --model 不存在的模型报错", async () => {
@@ -141,14 +139,19 @@ describe.skipIf(!isConsoleE2EReady())("e2e: quota（Console）", () => {
     expect(stderr).toContain("no matching models found");
   });
 
-  test("quota list JSON 输出包含 qpmInfo", async () => {
+  test("quota list JSON 输出包含 model/rpm/tpm/maxTPM", async () => {
     const { stdout, stderr, exitCode } = await runCli(["quota", "list", "--output", "json"]);
     expect(exitCode, stderr).toBe(0);
-    const data = parseStdoutJson<Array<{ model?: string; qpmInfo?: unknown }>>(stdout);
+    const data =
+      parseStdoutJson<
+        Array<{ model?: string; rpm?: number | null; tpm?: number | null; maxTPM?: number | null }>
+      >(stdout);
     expect(Array.isArray(data)).toBe(true);
     expect(data.length).toBeGreaterThan(0);
     expect(data[0].model).toBeTypeOf("string");
-    expect(data[0].qpmInfo).toBeDefined();
+    expect(data[0].rpm).toBeTypeOf("number");
+    expect(data[0].tpm).toBeTypeOf("number");
+    expect(data[0].maxTPM).toBeTypeOf("number");
   });
 
   test("quota request --dry-run 输出请求参数", async () => {
@@ -235,7 +238,7 @@ describe.skipIf(!isConsoleE2EReady())("e2e: quota（Console）", () => {
     expect(data.apis).toContain("zeldaEasy.bailian-telemetry.monitor.getMonitorData");
   });
 
-  test("quota check 文本输出包含双行表头", async () => {
+  test("quota check 文本输出包含单行英⽂表头", async () => {
     const { stdout, stderr, exitCode } = await runCli([
       "quota",
       "check",
@@ -244,12 +247,10 @@ describe.skipIf(!isConsoleE2EReady())("e2e: quota（Console）", () => {
       "--no-color",
     ]);
     expect(exitCode, stderr).toBe(0);
-    expect(stdout).toContain("模型");
     expect(stdout).toContain("Model");
-    expect(stdout).toContain("RPM 用量/限额");
     expect(stdout).toContain("RPM Usage/Limit");
-    expect(stdout).toContain("TPM 用量/限额");
-    expect(stdout).toContain("状态");
+    expect(stdout).toContain("TPM Usage/Limit");
+    expect(stdout).toContain("Status");
   });
 
   test("quota check --model 指定单模型", async () => {
@@ -264,7 +265,7 @@ describe.skipIf(!isConsoleE2EReady())("e2e: quota（Console）", () => {
     ]);
     expect(exitCode, stderr).toBe(0);
     expect(stdout).toContain("qwen3.6-plus");
-    expect(stdout).toMatch(/共 1 个模型/);
+    expect(stdout).toMatch(/Total: 1 models/);
   });
 
   test("quota check --model 逗号分隔多模型", async () => {
@@ -280,7 +281,7 @@ describe.skipIf(!isConsoleE2EReady())("e2e: quota（Console）", () => {
     expect(exitCode, stderr).toBe(0);
     expect(stdout).toContain("qwen3.6-plus");
     expect(stdout).toContain("qwen-plus");
-    expect(stdout).toMatch(/共 2 个模型/);
+    expect(stdout).toMatch(/Total: 2 models/);
   });
 
   test("quota check JSON 输出包含用量和限额字段", async () => {
@@ -311,7 +312,7 @@ describe.skipIf(!isConsoleE2EReady())("e2e: quota（Console）", () => {
     expect(data[0].tpmLimit).toBeTypeOf("number");
   });
 
-  test("quota check 状态列显示正常/接近限流/已限流之一", async () => {
+  test("quota check 状态列显示 Normal/Near Limit/Throttled 之一", async () => {
     const { stdout, stderr, exitCode } = await runCli([
       "quota",
       "check",
@@ -323,7 +324,7 @@ describe.skipIf(!isConsoleE2EReady())("e2e: quota（Console）", () => {
     ]);
     expect(exitCode, stderr).toBe(0);
     const hasStatus =
-      stdout.includes("正常") || stdout.includes("接近限流") || stdout.includes("已限流");
+      stdout.includes("Normal") || stdout.includes("Near Limit") || stdout.includes("Throttled");
     expect(hasStatus).toBe(true);
   });
 
diff --git a/packages/cli/tests/e2e/usage-free.e2e.test.ts b/packages/cli/tests/e2e/usage-free.e2e.test.ts
index 064f027..ff4087a 100644
--- a/packages/cli/tests/e2e/usage-free.e2e.test.ts
+++ b/packages/cli/tests/e2e/usage-free.e2e.test.ts
@@ -133,12 +133,21 @@ describe.skipIf(!isConsoleE2EReady())("e2e: usage free（Console）", () => {
       "json",
     ]);
     expect(exitCode, stderr).toBe(0);
-    const data = parseStdoutJson<{
-      code?: string;
-      successResponse?: boolean;
-    }>(stdout);
-    expect(data.code).toBe("200");
-    expect(data.successResponse).toBe(true);
+    const data = parseStdoutJson<
+      Array<{
+        model?: string;
+        type?: string | null;
+        remaining?: number | null;
+        total?: number | null;
+        usagePercent?: number | null;
+        expires?: string | null;
+        autoStop?: boolean | string | null;
+      }>
+    >(stdout);
+    expect(Array.isArray(data)).toBe(true);
+    expect(data.length).toBeGreaterThan(0);
+    expect(data[0].model).toBe("qwen3-max");
+    expect(data[0].type).toBeTypeOf("string");
   });
 
   test("usage free --model 单模型文本输出包含表头", async () => {
@@ -276,7 +285,9 @@ describe.skipIf(!isConsoleE2EReady())("e2e: usage free（Console）", () => {
       "json",
     ]);
     expect(exitCode, stderr).toBe(0);
-    const data = parseStdoutJson<{ code?: string }>(stdout);
-    expect(data.code).toBe("200");
+    const data = parseStdoutJson<Array<{ model?: string }>>(stdout);
+    expect(Array.isArray(data)).toBe(true);
+    expect(data.length).toBeGreaterThan(0);
+    expect(data[0].model).toBe("qwen3-max");
   });
 });
diff --git a/packages/cli/tests/e2e/usage-stats.e2e.test.ts b/packages/cli/tests/e2e/usage-stats.e2e.test.ts
index 29c9e0d..9aab48b 100644
--- a/packages/cli/tests/e2e/usage-stats.e2e.test.ts
+++ b/packages/cli/tests/e2e/usage-stats.e2e.test.ts
@@ -169,11 +169,17 @@ describe.skipIf(!isConsoleE2EReady())("e2e: usage stats（Console）", () => {
     ]);
     expect(exitCode, stderr).toBe(0);
     const data = parseStdoutJson<{
-      code?: string;
-      successResponse?: boolean;
+      period?: { start?: string; end?: string; days?: number };
+      modelsCalled?: number;
+      successfulCalls?: number;
+      usages?: Array<{ key?: string; value?: number }>;
     }>(stdout);
-    expect(data.code).toBe("200");
-    expect(data.successResponse).toBe(true);
+    expect(data.period).toBeDefined();
+    expect(data.period?.start).toBeTypeOf("string");
+    expect(data.period?.end).toBeTypeOf("string");
+    expect(data.period?.days).toBeTypeOf("number");
+    expect(data.modelsCalled).toBeTypeOf("number");
+    expect(data.successfulCalls).toBeTypeOf("number");
   });
 
   test("usage stats 概览文本输出包含中英文表头", async () => {
diff --git a/packages/core/src/advisor/constants/prompts.ts b/packages/core/src/advisor/constants/prompts.ts
index 9d2f04d..2930f92 100644
--- a/packages/core/src/advisor/constants/prompts.ts
+++ b/packages/core/src/advisor/constants/prompts.ts
@@ -1,196 +1,181 @@
-export const INTENT_MODEL = "qwen-turbo";
+export const INTENT_MODEL = "qwen-flash";
 export const RANKING_MODEL = "qwen3.6-flash";
-export const RANKING_MODEL_FAST = "qwen-turbo";
-
-export const INTENT_SYSTEM_PROMPT = `你是一个意图分析器。根据用户的需求描述，先理解用户场景，再提取结构化信息。
-
-## 分析步骤
-1. 用一句话总结用户的核心需求（taskSummary），要体现具体场景而非泛泛描述
-2. 推断场景特征（scenarioHints），例如：["需要低延迟","面向C端用户","高并发","对话式交互","离线批处理","需要精准度"]
-3. 基于场景特征推断 budget 和 qualityPreference
-   - 只在用户明确表达或场景强烈暗示时偏离默认值
-   - 用户明确说"低成本"、"便宜"、"省钱" → budget:"low"
-   - 用户明确说"最好的"、"高精度"、"不计成本" → qualityPreference:"flagship"
-   - 场景本身有强约束时才推断：如"日均百万请求的客服" → budget:"low"（高并发=成本敏感）
-   - 其他情况保持 budget:"medium", qualityPreference:"balanced"
-4. 提取模态、能力、特性等结构化字段
-
-## 示例
-
-用户: "做一个低成本高并发的在线客服"
-→ budget:"low", qualityPreference:"cost-optimized"（用户明确说了低成本）
-
-用户: "法律合同审查，要求高精准度"
-→ budget:"medium", qualityPreference:"flagship"（用户明确要求高精准度，但没提预算）
-
-用户: "我要做一个能理解图片的客服机器人"
-→ budget:"medium", qualityPreference:"balanced"（用户没提成本和质量要求，不过度推断）
-
-用户: "帮我选一个写代码的模型"
-→ budget:"medium", qualityPreference:"balanced"（通用需求，无明确倾向）
-
-用户: "预算有限，做个简单的文本摘要功能"
-→ budget:"low", qualityPreference:"cost-optimized"（用户说了预算有限）
-
-用户: "企业级知识库问答，准确率是第一优先级"
-→ budget:"high", qualityPreference:"flagship"（企业级+准确率第一=愿投入高成本）
-
-用户: "个人学习项目，试试AI生成图片"
-→ budget:"low", qualityPreference:"cost-optimized"（个人学习=成本敏感）
-
-用户: "做一个Agent自动根据用户意图生成动画片"
-→ budget:"medium", qualityPreference:"balanced"（复杂pipeline，但没明确成本/质量约束）
-
-## 模型偏好识别
-分析用户是否提到了特定的模型、模型系列或厂商，据此判断推荐模式：
-- 用户未提到任何模型/系列/厂商 → mode:"unconstrained"，不填 targets
-- 用户限定了范围（如"deepseek系列哪个好"、"通义千问的模型推荐"、"开源的推理模型"） → mode:"scoped"，targets:["deepseek"] 或 ["通义千问"]
-- 用户要对比特定模型（如"wan2.6和wan2.7哪个好"、"qwen-max和deepseek-v3对比"、"qwen-max适合做法律分析吗"） → mode:"comparison"，targets:["wan2.6","wan2.7"]
-  - 单模型评估也算 comparison，targets 只填一个
-- 用户以某模型为参照找替代（如"有没有类似qwen-max但更便宜的"） → mode:"alternative"，targets:["qwen-max"]
-- 用户明确排除某些模型/系列（如"除了qwen还有什么好的"） → excludes:["qwen"]，mode 根据其他条件判断
-- targets 填写用户原文中的模型/系列名称，保持原文写法
-
-## 输出字段
-- taskSummary: 一句话场景理解（必须具体，禁止"用户想用AI做某事"这种废话）
-- scenarioHints: 推断的场景特征数组
-- complexity: "single"（单一模型可完成）或 "pipeline"（需要多个模型协同）
-- segments: 仅 pipeline 时填写，每步包含 step/inputModality/outputModality/requiredCapabilities。
-  - step 必须是一句话描述该步骤在用户任务中解决的具体问题，例如"解析天气预报数据，生成适合视频制作的场景描述文本"，禁止用编号或泛化的模态标签
-  - segments 必须形成模态链路：每步的 inputModality 应包含上一步的 outputModality，确保上下游数据可以衔接
-- inputModality: 用户输入涉及的模态 ["Text","Image","Video","Audio"]
-- outputModality: 期望输出的模态
-- requiredCapabilities: 需要的能力。可选代码（必须严格使用，不要自创）：
-  TG=文本生成, Reasoning=推理, VU=视觉理解, IG=图像生成, VG=视频生成,
-  TTS=语音合成, ASR=语音识别, Realtime-ASR=实时语音识别,
-  Realtime-Text-to-Speech=实时语音合成, Realtime-Audio-Translate=实时音频翻译,
-  Realtime-Omni=实时全模态, Multimodal-Omni=全模态, ME=多模态嵌入,
-  TR=翻译, 3D-generation=3D生成
-- requiredFeatures: 需要的特性 (function-calling, web-search, structured-outputs, prefix-completion)
-- budget: "low"/"medium"/"high"（基于场景推断，不要默认 medium）
+export const RANKING_MODEL_FAST = "qwen-flash";
+
+export const INTENT_SYSTEM_PROMPT = `You are an intent analyzer. Given the user's requirement, understand the scenario first, then extract structured information.
+
+CRITICAL: You MUST respond entirely in English. Do not use any Chinese characters anywhere in your response. All text fields (taskSummary, scenarioHints) must be in English.
+
+## Analysis Steps
+1. Summarize the user's core need in one sentence (taskSummary) — be specific about the scenario, not generic
+2. Infer scenario hints (scenarioHints), e.g.: ["low-latency", "consumer-facing", "high-concurrency", "conversational", "offline-batch", "high-precision"]
+3. Infer budget and qualityPreference from scenario hints
+   - Only deviate from defaults when the user explicitly states or the scenario strongly implies
+   - User says "low cost", "cheap", "save money" → budget:"low"
+   - User says "best", "high precision", "cost no object" → qualityPreference:"flagship"
+   - Infer from scenario constraints only when strong: e.g. "1M requests/day customer service" → budget:"low" (high concurrency = cost-sensitive)
+   - Otherwise keep budget:"medium", qualityPreference:"balanced"
+4. Extract modalities, capabilities, features etc.
+
+## Model preference detection
+Analyze whether the user mentioned specific models, model families, or vendors:
+- No models/families/vendors mentioned → mode:"unconstrained", no targets
+- User scoped the range (e.g. "recommend from the deepseek family", "open-source reasoning models") → mode:"scoped", targets:["deepseek"]
+- User wants to compare specific models (e.g. "compare wan2.6 and wan2.7", "is qwen-max good for legal analysis") → mode:"comparison", targets:["wan2.6","wan2.7"]
+  - Single model evaluation is also comparison with one target
+- User wants alternatives to a reference model (e.g. "something like qwen-max but cheaper") → mode:"alternative", targets:["qwen-max"]
+- User explicitly excludes certain models/families (e.g. "good models besides qwen") → excludes:["qwen"], mode determined by other signals
+- targets should capture the model/family names as the user wrote them
+
+## Output fields
+- taskSummary: one-sentence scenario understanding (must be specific, never generic like "user wants AI")
+- scenarioHints: array of inferred scenario features
+- complexity: "single" or "pipeline"
+- segments: only for pipeline, each with step/inputModality/outputModality/requiredCapabilities
+  - step must describe the specific problem this step solves in the user's task, no numbered or generic modal labels
+  - segments must form a modality chain: each step's inputModality should cover the previous step's outputModality
+- inputModality: user input modalities ["Text","Image","Video","Audio"]
+- outputModality: expected output modalities
+- requiredCapabilities: capability codes (use strictly from the list, don't invent):
+  TG=Text Generation, Reasoning=Reasoning, VU=Vision Understanding, IG=Image Generation, VG=Video Generation,
+  TTS=Text-to-Speech, ASR=Speech-to-Text, Realtime-ASR=Realtime Speech-to-Text,
+  Realtime-Text-to-Speech=Realtime Text-to-Speech, Realtime-Audio-Translate=Realtime Audio Translation,
+  Realtime-Omni=Realtime Omni-modal, Multimodal-Omni=Multimodal Omni, ME=Multimodal Embedding,
+  TR=Translation, 3D-generation=3D Generation
+- requiredFeatures: required features (function-calling, web-search, structured-outputs, prefix-completion)
+- budget: "low"/"medium"/"high"
 - contextNeed: "standard"/"large"/"extra-large"
-- qualityPreference: "flagship"/"balanced"/"cost-optimized"（基于场景推断，不要默认 balanced）
-- modelPreference: { mode, targets?, excludes? }（见上方"模型偏好识别"）
+- qualityPreference: "flagship"/"balanced"/"cost-optimized"
+- modelPreference: { mode, targets?, excludes? }
 
-只输出 JSON，不要有其他文字。`;
+Output only JSON, no other text.`;
 
-export const SINGLE_SYSTEM_PROMPT = `你是阿里云百炼平台的模型推荐顾问。从以下候选模型中选出最佳推荐。
+export const SINGLE_SYSTEM_PROMPT = `You are a model recommendation advisor for Alibaba Cloud Model Studio. From the candidate models below, select the best recommendations.
 
-## 背景
-系统已根据用户意图预筛选了候选模型，你只需从中精选并排序。
-意图分析中包含 budget 和 qualityPreference 字段，这代表了用户的实际需求层次。
+CRITICAL: You MUST respond entirely in English. Do not use any Chinese characters anywhere in your response. Every field — reason, highlights, step, summary — must be written in English.
 
-## 推荐策略
+## Background
+The system has pre-filtered candidate models based on intent analysis. Your job is to rank and pick from these candidates.
+The intent includes budget and qualityPreference fields representing the user's actual needs.
 
-推荐 3 个不同档次的模型，但排序必须反映用户的真实需求：
+## Recommendation Strategy
 
-- 推荐 #1（最佳推荐）：根据 budget 和 qualityPreference 判断哪个档次最适合用户，把那个档次的最佳模型放在第一位
-- 推荐 #2（次优选择）：另一个档次中值得考虑的模型，说明与 #1 相比的 tradeoff
-- 推荐 #3（备选参考）：第三个视角的选择，说明适用场景差异
+Recommend 3 models at different tiers, but ordering must reflect the user's true needs:
 
-关键原则：
-- budget:"low" / qualityPreference:"cost-optimized" → 推荐 #1 应该是性价比最高的模型，而非旗舰模型
-- budget:"high" / qualityPreference:"flagship" → 推荐 #1 应该是能力最强的旗舰模型
-- budget:"medium" / qualityPreference:"balanced" → 推荐 #1 应该是综合匹配度最高的模型，不预设档次偏好
+- #1 (Best Pick): Based on budget and qualityPreference, pick the best-fitting tier and put its top model first
+- #2 (Runner-Up): A worthy consideration from another tier, explaining tradeoffs vs #1
+- #3 (Alternative): A third-perspective choice, explaining scenario differences
 
-每个推荐都必须说明该模型为什么适合（或作为备选为什么值得考虑），理由必须关联用户的具体需求。
+Key principles:
+- budget:"low" / qualityPreference:"cost-optimized" → #1 should be the best value model, not a flagship
+- budget:"high" / qualityPreference:"flagship" → #1 should be the most capable flagship model
+- budget:"medium" / qualityPreference:"balanced" → #1 should be the best all-around match
 
-## 规则
-- 只能推荐候选列表中的模型，严禁推荐列表外的模型
-- 严禁使用泛泛的推荐理由（如"性能强大"、"综合能力好"、"效果不错"），每条 reason 必须说明该模型解决用户任务中的什么具体问题
-- 三个推荐的理由不允许雷同，每个必须从不同维度论证
-- 有定价信息时：结合 budget 字段权衡，把最符合用户预算的放在最前面
-- 有家族信息时：避免推荐同一家族的多个模型，优先推荐稳定版本
-- 有版本标签时：优先推荐 stable/latest 版本，除非用户明确需要特定版本
-- 没有增强字段的模型：按能力和描述排序即可，不因缺少信息而降权
-- 如果没有合适的模型，返回空数组
-- 如果你认为该需求实际需要多模型协同完成（pipeline），可以输出 type:"pipeline" 格式
-- 输出严格 JSON，不要输出其他内容
+Each recommendation must explain why the model fits (or as an alternative, why it's worth considering), with reasoning tied to the user's specific needs.
 
-## 输出格式
+## Rules
+- Only recommend models from the candidate list — never recommend outside it
+- No generic reasons ("powerful", "good performance", "effective"). Each reason must describe how the model solves a specific aspect of the user's task
+- All three recommendations must have distinct reasoning angles, not duplicate reasons
+- When pricing is available: factor in budget, put the most budget-friendly option first
+- When family info is available: avoid recommending multiple models from the same family, prefer stable versions
+- When version tags are available: prefer stable/latest versions unless the user explicitly needs a specific version
+- Models without enriched fields: rank by capability and description — don't penalize for missing info
+- If no model fits, return an empty array
+- If you believe the task actually requires multi-model collaboration (pipeline), you may output type:"pipeline" format
+- Output strict JSON, no other text
 
-单一任务：
-{"type":"single","recommendations":[{"model":"模型ID","reason":"推荐理由","highlights":["亮点"]}]}
+## Output Format
 
-复合任务（仅当你确信需要多模型协同时）：
-{"type":"pipeline","summary":"一句话方案描述","steps":[{"step":"步骤描述","recommendations":[{"model":"模型ID","reason":"选择理由","highlights":["亮点"]}]}]}`;
+Single task:
+{"type":"single","recommendations":[{"model":"model ID","reason":"recommendation reason","highlights":["key highlights"]}]}
 
-export const PIPELINE_SYSTEM_PROMPT = `你是阿里云百炼平台的模型推荐顾问。用户需求已被拆解为多步骤流水线，请为每步选出最佳模型。
+Pipeline (only when confident multi-model is needed):
+{"type":"pipeline","summary":"one-line solution description","steps":[{"step":"step description","recommendations":[{"model":"model ID","reason":"reason for choosing","highlights":["highlights"]}]}]}`;
 
-## 背景
-系统已根据各步骤需求预筛选了候选模型。
-意图分析中包含 budget 和 qualityPreference 字段，这代表了用户的实际需求层次。
+export const PIPELINE_SYSTEM_PROMPT = `You are a model recommendation advisor for Alibaba Cloud Model Studio. The user's need has been decomposed into multi-step pipeline. Select the best model for each step.
 
-## 推荐策略
+CRITICAL: You MUST respond entirely in English. Do not use any Chinese characters anywhere in your response. Every field — reason, highlights, step, summary — must be written in English.
 
-每步推荐 3 个不同档次的模型，但排序必须反映用户的真实需求：
+## Background
+The system has pre-filtered candidate models for each step's requirements.
+The intent includes budget and qualityPreference fields representing the user's actual needs.
 
-- 推荐 #1（最佳推荐）：根据 budget 和 qualityPreference 判断哪个档次最适合用户，把那个档次的最佳模型放在第一位
-- 推荐 #2（次优选择）：另一个档次中值得考虑的模型，说明 tradeoff
-- 推荐 #3（备选参考）：第三个视角的选择，说明适用场景差异
+## Recommendation Strategy
 
-关键原则：
-- budget:"low" / qualityPreference:"cost-optimized" → 推荐 #1 应该是性价比最高的模型
-- budget:"high" / qualityPreference:"flagship" → 推荐 #1 应该是能力最强的旗舰模型
-- budget:"medium" / qualityPreference:"balanced" → 推荐 #1 应该是综合匹配度最高的模型
+Recommend 3 models at different tiers per step, ordering by user needs:
 
-## 规则
-- 只能推荐候选列表中的模型
-- 每步推荐多个模型，按优先级排序，每个推荐给出简短理由和关键亮点
-- step 字段必须用一句话描述该步骤在用户任务中解决的具体问题，禁止用编号或泛化的模态标签（如"输出: Text"）
-- 严禁使用泛泛的推荐理由，每条 reason 必须说明该模型在这一步解决用户任务中的什么具体问题
-- 有定价信息时：结合 budget 字段权衡，把最符合用户预算的放在最前面
-- 有家族信息时：避免在相邻步骤使用同一家族的不同规格模型，除非确实需要
-- 没有增强字段的模型：按能力和描述排序即可，不因缺少信息而降权
-- 相邻步骤的模型必须模态兼容：上一步模型的输出模态必须被下一步模型的输入模态支持
-- 如果你认为该需求其实单模型可以完成，可以输出 type:"single" 格式
-- 输出严格 JSON
+- #1 (Best Pick): Based on budget and qualityPreference, pick the best-fitting tier and put its top model first
+- #2 (Runner-Up): A worthy consideration from another tier, explaining tradeoffs
+- #3 (Alternative): A third-perspective choice
 
-## 输出格式
+Key principles:
+- budget:"low" / qualityPreference:"cost-optimized" → #1 should be the best value model
+- budget:"high" / qualityPreference:"flagship" → #1 should be the most capable flagship model
+- budget:"medium" / qualityPreference:"balanced" → #1 should be the best all-around match
 
-{"type":"pipeline","summary":"一句话方案描述","steps":[{"step":"该步骤在用户任务中解决的具体问题","recommendations":[{"model":"模型ID","reason":"该模型如何解决这一步的具体问题","highlights":["亮点"]}]}]}
+## Rules
+- Only recommend models from the candidate list
+- Each step recommends multiple models sorted by priority, each with brief reason and key highlights
+- The "step" field must describe the specific problem this step solves in the user's task — no numbered or generic modal labels (e.g. "Output: Text")
+- No generic reasons. Each reason must describe how the model solves a specific aspect of the user's task at this step
+- When pricing is available: factor in budget, put the most budget-friendly option first
+- When family info is available: avoid using different tiers of the same family in adjacent steps unless truly needed
+- Models without enriched fields: rank by capability and description — don't penalize for missing info
+- Adjacent steps must be modality-compatible: the previous step's output modalities must be supported as input modalities by the next step
+- If you believe the task can be done with a single model, output type:"single" format
+- Output strict JSON
 
-或者（如果你认为单模型即可）：
-{"type":"single","recommendations":[{"model":"模型ID","reason":"推荐理由","highlights":["亮点"]}]}`;
+## Output Format
 
-export const COMPARISON_SYSTEM_PROMPT = `你是阿里云百炼平台的模型对比顾问。用户想对比特定模型，请根据使用场景进行对比分析。
+{"type":"pipeline","summary":"one-line solution description","steps":[{"step":"specific problem this step solves in the user's task","recommendations":[{"model":"model ID","reason":"how this model solves the specific problem at this step","highlights":["highlights"]}]}]}
 
-## 背景
-用户指定了要对比的模型，系统已将这些模型和相关候选预筛选到列表中。
-意图分析中的 modelPreference.targets 是用户要对比的模型。
+Or (if single model suffices):
+{"type":"single","recommendations":[{"model":"model ID","reason":"recommendation reason","highlights":
+["key highlights"]}]}`;
 
-## 对比策略
-- 用户指定的模型必须全部出现在推荐结果中，按适合程度排序
-- 每个模型的 reason 必须是对比性的，说明该模型相对于其他对比模型的优势和劣势
-- 如果候选中有比用户指定的更合适的模型，可以额外推荐，但用户指定的必须优先包含
-- 单模型评估场景（targets 只有一个）：评估该模型是否适合用户需求，同时推荐更优的替代
+export const COMPARISON_SYSTEM_PROMPT = `You are a model comparison advisor for Alibaba Cloud Model Studio. The user wants to compare specific models — analyze them against the use case.
 
-## 规则
-- 只能推荐候选列表中的模型
-- reason 必须包含对比视角：该模型相比其他模型在哪些方面更好/更差
-- highlights 突出各模型的差异化特点
-- 输出严格 JSON，不要输出其他内容
+CRITICAL: You MUST respond entirely in English. Do not use any Chinese characters anywhere in your response. Every field — reason, highlights — must be written in English.
 
-## 输出格式
-{"type":"single","recommendations":[{"model":"模型ID","reason":"对比分析理由","highlights":["差异化亮点"]}]}`;
+## Background
+The user specified models to compare. The system has pre-filtered these models and related candidates into the list.
+The intent's modelPreference.targets are the models to compare.
 
-export const ALTERNATIVE_SYSTEM_PROMPT = `你是阿里云百炼平台的模型替代顾问。用户以某个模型为参照，寻找替代方案。
+## Comparison Strategy
+- All user-specified models must appear in the results, sorted by suitability
+- Each model's reason must be comparative: describe strengths and weaknesses relative to other models being compared
+- If candidates contain better fits than what the user specified, they can be additionally recommended, but user-specified models take priority
+- Single-model evaluation (one target): evaluate if the model fits, and recommend better alternatives
 
-## 背景
-用户以某个模型为参照点，想找到在特定维度上更优的替代方案（如更便宜、更快、更强）。
-意图分析中的 modelPreference.targets 是参照模型。
+## Rules
+- Only recommend models from the candidate list
+- reason must include comparative perspective: how this model is better/worse compared to others
+- highlights should emphasize differentiating characteristics
+- Output strict JSON
 
-## 替代策略
-- 推荐 #1：如果参照模型在候选中，先评估它是否满足用户需求，给出其基本定位
-- 推荐 #2~#3：推荐替代方案，reason 必须说明相比参照模型在用户关注维度上的 tradeoff
-- 关注用户提到的替代维度（如"更便宜"→重点对比定价，"更强"→重点对比能力）
-
-## 规则
-- 只能推荐候选列表中的模型
-- 参照模型必须包含在结果中（如果在候选列表中）
-- 替代推荐的 reason 必须说明与参照模型的具体差异
-- 避免推荐和参照模型同系列的其他版本（除非确实有显著差异）
-- 输出严格 JSON，不要输出其他内容
+## Output Format
+{"type":"single","recommendations":[{"model":"model ID","reason":"comparative analysis","highlights":["differentiators"]}]}`;
 
-## 输出格式
-{"type":"single","recommendations":[{"model":"模型ID","reason":"替代分析理由","highlights":["差异化亮点"]}]}`;
+export const ALTERNATIVE_SYSTEM_PROMPT = `You are a model alternative advisor for Alibaba Cloud Model Studio. The user has a reference model and wants to find alternatives.
+
+CRITICAL: You MUST respond entirely in English. Do not use any Chinese characters anywhere in your response. Every field — reason, highlights — must be written in English.
+
+## Background
+The user has a reference model and wants to find alternatives that are better in specific dimensions (cheaper, faster, more capable).
+The intent's modelPreference.targets is the reference model.
+
+## Alternative Strategy
+- #1: If the reference model is in candidates, first evaluate if it meets the user's needs — give its positioning
+- #2~#3: Recommend alternatives. reason must explain the tradeoff vs the reference model in the user's dimensions of interest
+- Focus on the user's stated alternative dimension (e.g. "cheaper" → focus on pricing comparison, "better" → focus on capability comparison)
+
+## Rules
+- Only recommend models from the candidate list
+- The reference model must be included in results if it's in the candidate list
+- Alternative recommendations must explain concrete differences from the reference model
+- Avoid recommending other versions from the same family unless there's a significant difference
+- Output strict JSON
+
+## Output Format
+{"type":"single","recommendations":[{"model":"model ID","reason":"alternative analysis","highlights":["differentiators"]}]}`;
diff --git a/packages/core/src/advisor/embedding.ts b/packages/core/src/advisor/embedding.ts
index fb9311e..d8fc620 100644
--- a/packages/core/src/advisor/embedding.ts
+++ b/packages/core/src/advisor/embedding.ts
@@ -76,20 +76,20 @@ async function embedBatch(config: Config, texts: string[]): Promise<number[][]>
 }
 
 const CAPABILITY_LABELS: Record<string, string> = {
-  TG: "文本生成",
-  Reasoning: "推理",
-  VU: "视觉理解",
-  IG: "图像生成",
-  VG: "视频生成",
-  TTS: "语音合成",
-  ASR: "语音识别",
+  TG: "Text Generation",
+  Reasoning: "Reasoning",
+  VU: "Vision Understanding",
+  IG: "Image Generation",
+  VG: "Video Generation",
+  TTS: "Text-to-Speech",
+  ASR: "Speech-to-Text",
 };
 
 const MODALITY_LABELS: Record<string, string> = {
-  Text: "文本",
-  Image: "图片/图像",
-  Video: "视频",
-  Audio: "音频/语音",
+  Text: "Text",
+  Image: "Image",
+  Video: "Video",
+  Audio: "Audio",
 };
 
 interface GroupData {
@@ -135,12 +135,12 @@ function buildModelText(model: ModelProfile, descriptions: Map<string, string>):
     model.name,
     model.model,
     description,
-    caps ? `能力: ${caps}` : "",
-    inputMods ? `输入: ${inputMods}` : "",
-    outputMods ? `输出: ${outputMods}` : "",
-    model.features?.length ? `特性: ${model.features.join(", ")}` : "",
+    caps ? `Capabilities: ${caps}` : "",
+    inputMods ? `Input: ${inputMods}` : "",
+    outputMods ? `Output: ${outputMods}` : "",
+    model.features?.length ? `Features: ${model.features.join(", ")}` : "",
     model.familyName || "",
-    model.category ? `定位: ${model.category}` : "",
+    model.category ? `Category: ${model.category}` : "",
   ].filter(Boolean);
 
   return parts.join(" | ");
diff --git a/packages/core/src/advisor/recommend.ts b/packages/core/src/advisor/recommend.ts
index 863334c..53244d8 100644
--- a/packages/core/src/advisor/recommend.ts
+++ b/packages/core/src/advisor/recommend.ts
@@ -46,26 +46,27 @@ function buildCandidatesContext(candidates: ScoredCandidate[]): string {
     .map(({ model: profile }) => {
       const parts = [
         `ID: ${profile.model}`,
-        `名称: ${profile.name}`,
-        `描述: ${profile.shortDescription || profile.description}`,
-        `能力: ${profile.capabilities.join(", ")}`,
-        `特性: ${profile.features.join(", ")}`,
+        `Name: ${profile.name}`,
+        `Description: ${profile.shortDescription || profile.description}`,
+        `Capabilities: ${profile.capabilities.join(", ")}`,
+        `Features: ${profile.features.join(", ")}`,
       ];
-      if (profile.contextWindow) parts.push(`上下文窗口: ${profile.contextWindow}`);
-      if (profile.maxOutputTokens) parts.push(`最大输出: ${profile.maxOutputTokens}`);
-      if (profile.category) parts.push(`类别: ${profile.category}`);
+      if (profile.contextWindow) parts.push(`Context Window: ${profile.contextWindow}`);
+      if (profile.maxOutputTokens) parts.push(`Max Output: ${profile.maxOutputTokens}`);
+      if (profile.category) parts.push(`Category: ${profile.category}`);
       const modality = profile.inferenceMetadata;
       if (modality?.request_modality?.length)
-        parts.push(`输入模态: ${modality.request_modality.join(", ")}`);
+        parts.push(`Input Modality: ${modality.request_modality.join(", ")}`);
       if (modality?.response_modality?.length)
-        parts.push(`输出模态: ${modality.response_modality.join(", ")}`);
+        parts.push(`Output Modality: ${modality.response_modality.join(", ")}`);
       const prices = formatPrices(profile);
-      if (prices) parts.push(`定价: ${prices}`);
+      if (prices) parts.push(`Pricing: ${prices}`);
       const qpm = formatQpm(profile);
       if (qpm) parts.push(`QPM: ${qpm}`);
-      if (profile.versionTag) parts.push(`版本: ${profile.versionTag}`);
-      if (profile.openSource !== undefined) parts.push(`开源: ${profile.openSource ? "是" : "否"}`);
-      if (profile.family) parts.push(`家族: ${profile.family}`);
+      if (profile.versionTag) parts.push(`Version: ${profile.versionTag}`);
+      if (profile.openSource !== undefined)
+        parts.push(`Open Source: ${profile.openSource ? "Yes" : "No"}`);
+      if (profile.family) parts.push(`Family: ${profile.family}`);
       return parts.join(" | ");
     })
     .join("\n");
@@ -86,29 +87,29 @@ function buildIntentContext(intent: IntentProfile): string {
     modelPreference,
   } = intent;
   const parts: string[] = [];
-  if (taskSummary) parts.push(`场景理解: ${taskSummary}`);
-  if (scenarioHints.length) parts.push(`场景特征: ${scenarioHints.join(", ")}`);
-  if (inputModality.length) parts.push(`输入模态: ${inputModality.join(", ")}`);
-  if (outputModality.length) parts.push(`输出模态: ${outputModality.join(", ")}`);
-  if (requiredCapabilities.length) parts.push(`所需能力: ${requiredCapabilities.join(", ")}`);
-  if (requiredFeatures.length) parts.push(`所需特性: ${requiredFeatures.join(", ")}`);
-  parts.push(`预算倾向: ${budget}`);
-  parts.push(`质量偏好: ${qualityPreference}`);
-  if (contextNeed !== ContextNeeds.Standard) parts.push(`上下文需求: ${contextNeed}`);
+  if (taskSummary) parts.push(`Task: ${taskSummary}`);
+  if (scenarioHints.length) parts.push(`Scenario: ${scenarioHints.join(", ")}`);
+  if (inputModality.length) parts.push(`Input: ${inputModality.join(", ")}`);
+  if (outputModality.length) parts.push(`Output: ${outputModality.join(", ")}`);
+  if (requiredCapabilities.length) parts.push(`Capabilities: ${requiredCapabilities.join(", ")}`);
+  if (requiredFeatures.length) parts.push(`Features: ${requiredFeatures.join(", ")}`);
+  parts.push(`Budget: ${budget}`);
+  parts.push(`Quality: ${qualityPreference}`);
+  if (contextNeed !== ContextNeeds.Standard) parts.push(`Context: ${contextNeed}`);
   if (modelPreference && modelPreference.mode !== "unconstrained") {
-    parts.push(`模型偏好: ${modelPreference.mode}`);
+    parts.push(`Mode: ${modelPreference.mode}`);
     if (modelPreference.targets?.length)
-      parts.push(`目标模型: ${modelPreference.targets.join(", ")}`);
+      parts.push(`Targets: ${modelPreference.targets.join(", ")}`);
     if (modelPreference.excludes?.length)
-      parts.push(`排除模型: ${modelPreference.excludes.join(", ")}`);
+      parts.push(`Excludes: ${modelPreference.excludes.join(", ")}`);
   }
   if (segments?.length) {
-    parts.push(`拆解步骤:`);
+    parts.push(`Pipeline Steps:`);
     for (const seg of segments) {
-      const inMod = seg.inputModality.join(",") || "无";
-      const outMod = seg.outputModality.join(",") || "无";
-      const caps = seg.requiredCapabilities.join(",") || "无";
-      parts.push(`  - ${seg.step} (输入: ${inMod} → 输出: ${outMod}, 能力: ${caps})`);
+      const inMod = seg.inputModality.join(",") || "none";
+      const outMod = seg.outputModality.join(",") || "none";
+      const caps = seg.requiredCapabilities.join(",") || "none";
+      parts.push(`  - ${seg.step} (Input: ${inMod} → Output: ${outMod}, Capabilities: ${caps})`);
     }
   }
   return parts.join("\n");
@@ -175,7 +176,7 @@ function validatePipelineCompatibility(
       const compatible = accepts.some((mod) => prevOutputs.has(mod));
       if (!compatible && accepts.length > 0) {
         warnings.push(
-          `${rec.name} 的输入模态 [${accepts.join(", ")}] 可能不兼容上一步的输出模态 [${[...prevOutputs].join(", ")}]`,
+          `${rec.name}'s input modalities [${accepts.join(", ")}] may not be compatible with the previous step's output modalities [${[...prevOutputs].join(", ")}]`,
         );
       }
     }
@@ -204,7 +205,7 @@ export async function rankModels(
     systemPrompt = ALTERNATIVE_SYSTEM_PROMPT;
   } else if (preferenceMode === "scoped") {
     const scopeNote = intent.modelPreference?.targets?.length
-      ? `\n\n## 范围限定\n用户明确要求在以下范围内推荐：${intent.modelPreference.targets.join("、")}。请优先从匹配该范围的模型中选择。`
+      ? `\n\n## Scope Restriction\nThe user explicitly requested recommendations from: ${intent.modelPreference.targets.join(", ")}. Prioritize models within this scope.`
       : "";
     systemPrompt =
       (intent.complexity === Complexities.Pipeline
@@ -219,8 +220,8 @@ export async function rankModels(
 
   const userMessage =
     intent.complexity === Complexities.Pipeline
-      ? `意图分析结果：\n${intentContext}\n\n候选模型列表：\n${candidatesContext}\n\n用户原始需求：${userInput}\n\n请为流水线各步骤各推荐最多 ${top} 个模型。`
-      : `意图分析结果：\n${intentContext}\n\n候选模型列表：\n${candidatesContext}\n\n用户原始需求：${userInput}\n\n请推荐最多 ${top} 个模型。`;
+      ? `Intent Analysis:\n${intentContext}\n\nCandidate Models:\n${candidatesContext}\n\nUser Request: ${userInput}\n\nRecommend up to ${top} models for each pipeline step. Respond in English only.`
+      : `Intent Analysis:\n${intentContext}\n\nCandidate Models:\n${candidatesContext}\n\nUser Request: ${userInput}\n\nRecommend up to ${top} models. Respond in English only.`;
 
   const body: Record<string, unknown> = {
     model: useThinkingModel ? RANKING_MODEL : RANKING_MODEL_FAST,