diff --git a/ui/litellm-dashboard/src/components/view_logs/columns.test.tsx b/ui/litellm-dashboard/src/components/view_logs/columns.test.tsx
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+++ b/ui/litellm-dashboard/src/components/view_logs/columns.test.tsx
@@ -0,0 +1,181 @@
+import { describe, it, expect, vi } from "vitest";
+import { render } from "@testing-library/react";
+import React from "react";
+import { createColumns, LogEntry } from "./columns";
+import type { LogsSortField } from "./columns";
+
+vi.mock("../provider_info_helpers", () => ({
+  getProviderLogoAndName: (provider: string) => ({
+    logo: `https://logo.test/${provider}.png`,
+    displayName: provider,
+  }),
+}));
+
+const makeLogEntry = (overrides: Partial<LogEntry> = {}): LogEntry => ({
+  request_id: "req-1",
+  api_key: "sk-test",
+  team_id: "team-1",
+  model: "gpt-4",
+  model_id: "gpt-4-id",
+  call_type: "completion",
+  spend: 0.005,
+  total_tokens: 150,
+  prompt_tokens: 100,
+  completion_tokens: 50,
+  startTime: "2025-01-15T10:00:00Z",
+  endTime: "2025-01-15T10:00:02Z",
+  cache_hit: "none",
+  messages: [],
+  response: [],
+  ...overrides,
+});
+
+const renderCell = (columnKey: string, row: LogEntry) => {
+  const cols = createColumns();
+  const col = cols.find(
+    (c: any) => c.accessorKey === columnKey || c.id === columnKey,
+  );
+  if (!col || !col.cell) throw new Error(`Column "${columnKey}" not found`);
+
+  const nestedValue = columnKey.includes(".")
+    ? columnKey.split(".").reduce((obj: any, key: string) => obj?.[key], row)
+    : (row as any)[columnKey];
+
+  const cellContext: any = {
+    getValue: () => nestedValue,
+    row: { original: row, getIsExpanded: () => false, getCanExpand: () => false, getToggleExpandedHandler: () => () => {} },
+  };
+
+  const CellComponent = typeof col.cell === "function" ? col.cell : () => null;
+  const { container } = render(<>{CellComponent(cellContext)}</>);
+  return container;
+};
+
+// ---------------------------------------------------------------------------
+// createColumns — sort prop behavior
+// ---------------------------------------------------------------------------
+describe("createColumns", () => {
+  it("uses plain string headers when no sortProps provided", () => {
+    const cols = createColumns();
+    const timeCol = cols.find((c: any) => c.accessorKey === "startTime");
+    expect(timeCol?.header).toBe("Time");
+  });
+
+  it("uses SortableHeader render functions when sortProps provided", () => {
+    const cols = createColumns({
+      sortBy: "startTime" as LogsSortField,
+      sortOrder: "desc",
+      onSortChange: vi.fn(),
+    });
+    const timeCol = cols.find((c: any) => c.accessorKey === "startTime");
+    expect(typeof timeCol?.header).toBe("function");
+  });
+});
+
+// ---------------------------------------------------------------------------
+// Duration column — ms to seconds conversion
+// ---------------------------------------------------------------------------
+describe("Duration column", () => {
+  it("converts ms to seconds with 2 decimal places", () => {
+    const container = renderCell("request_duration_ms", makeLogEntry({ request_duration_ms: 1234 }));
+    expect(container.textContent).toContain("1.23");
+  });
+
+  it('renders "-" when duration is null', () => {
+    const container = renderCell("request_duration_ms", makeLogEntry({ request_duration_ms: undefined }));
+    expect(container.textContent).toBe("-");
+  });
+
+  it("does not treat 0 as null", () => {
+    const container = renderCell("request_duration_ms", makeLogEntry({ request_duration_ms: 0 }));
+    expect(container.textContent).toContain("0.00");
+  });
+});
+
+// ---------------------------------------------------------------------------
+// TTFT column — timestamp math with edge cases
+// ---------------------------------------------------------------------------
+describe("TTFT column", () => {
+  it("computes TTFT from startTime and completionStartTime", () => {
+    const container = renderCell("completionStartTime", makeLogEntry({
+      startTime: "2025-01-15T10:00:00.000Z",
+      endTime: "2025-01-15T10:00:03.000Z",
+      completionStartTime: "2025-01-15T10:00:01.500Z",
+    }));
+    expect(container.textContent).toContain("1.50");
+  });
+
+  it('renders "-" when completionStartTime is null', () => {
+    const container = renderCell("completionStartTime", makeLogEntry({ completionStartTime: undefined }));
+    expect(container.textContent).toBe("-");
+  });
+
+  it('renders "-" when completionStartTime equals endTime (non-streaming)', () => {
+    const container = renderCell("completionStartTime", makeLogEntry({
+      endTime: "2025-01-15T10:00:02Z",
+      completionStartTime: "2025-01-15T10:00:02Z",
+    }));
+    expect(container.textContent).toBe("-");
+  });
+
+  it('renders "-" when TTFT would be negative', () => {
+    const container = renderCell("completionStartTime", makeLogEntry({
+      startTime: "2025-01-15T10:00:05.000Z",
+      endTime: "2025-01-15T10:00:06.000Z",
+      completionStartTime: "2025-01-15T10:00:04.000Z",
+    }));
+    expect(container.textContent).toBe("-");
+  });
+});
+
+// ---------------------------------------------------------------------------
+// Tags column — overflow count logic
+// ---------------------------------------------------------------------------
+describe("Tags column", () => {
+  it('renders "-" for empty tags', () => {
+    const container = renderCell("request_tags", makeLogEntry({ request_tags: {} }));
+    expect(container.textContent).toBe("-");
+  });
+
+  it('renders "-" for undefined tags', () => {
+    const container = renderCell("request_tags", makeLogEntry({ request_tags: undefined }));
+    expect(container.textContent).toBe("-");
+  });
+
+  it("renders single tag without +N suffix", () => {
+    const container = renderCell("request_tags", makeLogEntry({ request_tags: { env: "prod" } }));
+    expect(container.textContent).toContain("env: prod");
+    expect(container.textContent).not.toContain("+");
+  });
+
+  it("renders first tag with +N for additional tags", () => {
+    const container = renderCell("request_tags", makeLogEntry({
+      request_tags: { env: "prod", team: "ml", version: "2" },
+    }));
+    expect(container.textContent).toContain("env: prod");
+    expect(container.textContent).toContain("+2");
+  });
+});
+
+// ---------------------------------------------------------------------------
+// Model column — logo fallback chain
+// ---------------------------------------------------------------------------
+describe("Model column", () => {
+  it("uses default provider logo", () => {
+    const container = renderCell("model", makeLogEntry({ model: "claude-3", custom_llm_provider: "anthropic" }));
+    const img = container.querySelector("img");
+    expect(img?.getAttribute("src")).toContain("anthropic");
+  });
+
+  it("prefers MCP server logo over default provider logo", () => {
+    const container = renderCell("model", makeLogEntry({
+      model: "tool-model",
+      custom_llm_provider: "openai",
+      metadata: {
+        mcp_tool_call_metadata: { mcp_server_logo_url: "https://custom-logo.test/mcp.png" },
+      },
+    }));
+    const img = container.querySelector("img");
+    expect(img?.getAttribute("src")).toBe("https://custom-logo.test/mcp.png");
+  });
+});