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string modelName: string provider: string imageUrl: string | null duration: string durationMs: number cost: string success: boolean error?: string tokens?: { input: number output: number total: number } } export type TestResult = { testId: number timestamp: number prompt: string results: ImageResult[] } export type ModelConfig = { id: string name: string type: string provider: "Vercel AI Gateway" | "Fal AI" | "Prodia" cost: number color: string } [V0_FILE]typescript:file="lib/stats.ts" isMerged="true" import type { ImageResult } from "./types" export function calculateStats(images: ImageResult[]) { const successful = images.filter((img) => img.success) if (successful.length === 0) { return { avgDuration: "0", totalCost: "0.0000", fastestModel: null, } } const avgDuration = (successful.reduce((sum, img) => sum + img.durationMs, 0) / successful.length / 1000).toFixed(2) const totalCost = successful.reduce((sum, img) => sum + Number.parseFloat(img.cost.replace("$", "")), 0).toFixed(4) const fastestModel = successful.reduce((prev, curr) => (prev.durationMs < curr.durationMs ? prev : curr)) return { avgDuration, totalCost, fastestModel, } } export function sortImagesBySpeed(images: ImageResult[]): ImageResult[] { return [...images].sort((a, b) => a.durationMs - b.durationMs) } [V0_FILE]typescript:file="lib/pricing.ts" isQuickEdit="true" isMerged="true" // Fal AI pricing per megapixel export const FAL_PRICING = { "fal-ai/flux/schnell": 0.003, // per MP "fal-ai/flux/dev": 0.025, // per MP "fal-ai/flux-pro/v1.1": 0.04, // per MP "fal-ai/stable-diffusion-v35-medium": 0.02, // per MP "fal-ai/nano-banana": 0.04, // flat rate per image } // Prodia pricing by resolution and model export const PRODIA_PRICING = { "inference.flux-fast.schnell.txt2img.v1": { "1024x1024": 0.002, "512x512": 0.001 }, "inference.flux-fast.dev.txt2img.v1": { "1024x1024": 0.02 }, "inference.flux.pro11.txt2img.v1": { "1024x1024": 0.04 }, "inference.flux.dev.txt2img.v2": { "1024x1024": 0.02 }, "inference.sd15.txt2img.v1": { "512x512": 0.0025, "1024x1024": 0.008 }, } // Gemini pricing (per million tokens) export const GEMINI_PRICING = { input: 0.3, // $0.30 per 1M tokens output: 2.5, // $2.50 per 1M tokens } /** * Calculate cost for Fal AI models based on image dimensions */ export function calculateFalCost(modelType: string, width = 1024, height = 1024): number { const flatRate = FAL_PRICING[modelType as keyof typeof FAL_PRICING] return flatRate || 0 } /** * Calculate cost for Prodia models based on resolution */ export function calculateProdiaCost(modelType: string, resolution = "1024x1024"): number { const pricing = PRODIA_PRICING[modelType as keyof typeof PRODIA_PRICING] if (!pricing) return 0 // Use the specific resolution pricing or fallback to first available return pricing[resolution as keyof typeof pricing] || Object.values(pricing)[0] || 0 } /** * Calculate cost for Gemini models based on token usage */ export function calculateGeminiCost(inputTokens: number, outputTokens: number): number { const inputCost = (inputTokens / 1_000_000) * GEMINI_PRICING.input const outputCost = (outputTokens / 1_000_000) * GEMINI_PRICING.output return inputCost + outputCost } [V0_FILE]css:file="app/globals.css" isQuickEdit="true" isMerged="true" @import "tailwindcss"; @import "tw-animate-css"; @custom-variant dark (&:is(.dark *)); :root { --background: oklch(0.1 0 0); --foreground: oklch(0.95 0 0); --card: oklch(0.15 0 0); --card-foreground: oklch(0.95 0 0); --popover: oklch(0.15 0 0); --popover-foreground: oklch(0.95 0 0); --primary: oklch(0.95 0 0); --primary-foreground: oklch(0.1 0 0); --secondary: oklch(0.2 0 0); --secondary-foreground: oklch(0.95 0 0); --muted: oklch(0.18 0 0); --muted-foreground: oklch(0.6 0 0); --accent: oklch(0.25 0 0); --accent-foreground: oklch(0.95 0 0); --destructive: oklch(0.5 0.2 25); --destructive-foreground: oklch(1 0 0); --border: oklch(0.25 0 0); --input: oklch(0.25 0 0); --ring: oklch(0.95 0 0); --chart-1: oklch(0.8 0 0); --chart-2: oklch(0.7 0 0); --chart-3: oklch(0.6 0 0); --chart-4: oklch(0.5 0 0); --chart-5: oklch(0.4 0 0); /* Set radius to 0 to remove all rounded corners */ --radius: 0; --sidebar: oklch(0.15 0 0); --sidebar-foreground: oklch(0.95 0 0); --sidebar-primary: oklch(0.95 0 0); --sidebar-primary-foreground: oklch(0.1 0 0); --sidebar-accent: oklch(0.25 0 0); --sidebar-accent-foreground: oklch(0.95 0 0); --sidebar-border: oklch(0.25 0 0); --sidebar-ring: oklch(0.6 0 0); --ds-red-800: oklch(0.45 0.18 25); --ds-amber-800: oklch(0.55 0.15 65); --ds-orange-800: oklch(0.5 0.16 45); --ds-yellow-800: oklch(0.6 0.14 85); --ds-lime-800: oklch(0.55 0.12 125); --ds-green-800: oklch(0.45 0.13 145); --ds-emerald-800: oklch(0.48 0.12 165); --ds-teal-800: oklch(0.5 0.11 185); --ds-cyan-800: oklch(0.52 0.1 205); --ds-blue-800: oklch(0.48 0.14 245); --ds-indigo-800: oklch(0.45 0.15 265); --ds-violet-800: oklch(0.47 0.16 285); --ds-purple-800: oklch(0.46 0.17 305); --ds-pink-800: oklch(0.5 0.16 345); /* 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--accent-foreground: oklch(0.95 0 0); --destructive: oklch(0.5 0.2 25); --destructive-foreground: oklch(1 0 0); --border: oklch(0.25 0 0); --input: oklch(0.25 0 0); --ring: oklch(0.95 0 0); --sidebar: oklch(0.15 0 0); --sidebar-foreground: oklch(0.95 0 0); --sidebar-primary: oklch(0.95 0 0); --sidebar-primary-foreground: oklch(0.1 0 0); --sidebar-accent: oklch(0.25 0 0); --sidebar-accent-foreground: oklch(0.95 0 0); --sidebar-border: oklch(0.25 0 0); --sidebar-ring: oklch(0.6 0 0); } @theme inline { --font-sans: "Geist", "Geist Fallback"; --font-mono: "Geist Mono", "Geist Mono Fallback"; --color-background: var(--background); --color-foreground: var(--foreground); --color-card: var(--card); --color-card-foreground: var(--card-foreground); --color-popover: var(--popover); --color-popover-foreground: var(--popover-foreground); --color-primary: var(--primary); --color-primary-foreground: var(--primary-foreground); --color-secondary: var(--secondary); --color-secondary-foreground: var(--secondary-foreground); --color-muted: var(--muted); --color-muted-foreground: var(--muted-foreground); --color-accent: var(--accent); --color-accent-foreground: var(--accent-foreground); --color-destructive: var(--destructive); --color-destructive-foreground: var(--destructive-foreground); --color-border: var(--border); --color-input: var(--input); --color-ring: var(--ring); --color-chart-1: var(--chart-1); --color-chart-2: var(--chart-2); --color-chart-3: var(--chart-3); --color-chart-4: var(--chart-4); --color-chart-5: var(--chart-5); --radius-sm: calc(var(--radius) - 4px); --radius-md: calc(var(--radius) - 2px); --radius-lg: var(--radius); --radius-xl: calc(var(--radius) + 4px); --color-sidebar: var(--sidebar); --color-sidebar-foreground: var(--sidebar-foreground); --color-sidebar-primary: var(--sidebar-primary); --color-sidebar-primary-foreground: var(--sidebar-primary-foreground); --color-sidebar-accent: var(--sidebar-accent); --color-sidebar-accent-foreground: var(--sidebar-accent-foreground); --color-sidebar-border: var(--sidebar-border); --color-sidebar-ring: var(--sidebar-ring); } @layer base { * { @apply border-border outline-ring/50; } body { @apply bg-background text-foreground; } } [V0_FILE]typescriptreact:file="components/how-it-works-modal.tsx" isMerged="true" "use client" import { Dialog, DialogContent, DialogHeader, DialogTitle } from "@/components/ui/dialog" interface HowItWorksModalProps { open: boolean onClose: () => void } export function HowItWorksModal({ open, onClose }: HowItWorksModalProps) { return ( ) } [V0_FILE]typescriptreact:file="components/benchmark/image-modal.tsx" isMerged="true" "use client" import { useEffect } from "react" import { X } from "lucide-react" type ImageModalProps = { imageUrl: string | null onClose: () => void } export function ImageModal({ imageUrl, onClose }: ImageModalProps) { useEffect(() => { const handleEscape = (e: KeyboardEvent) => { if (e.key === "Escape") { onClose() } } if (imageUrl) { window.addEventListener("keydown", handleEscape) } return () => { window.removeEventListener("keydown", handleEscape) } }, [imageUrl, onClose]) if (!imageUrl) return null return (

e.stopPropagation()} />

) } [V0_FILE]typescriptreact:file="components/benchmark/stats-header.tsx" isMerged="true" import { Clock, DollarSign, Zap } from "lucide-react" import type { ImageResult } from "@/lib/types" import { calculateStats } from "@/lib/stats" type StatsHeaderProps = { images: ImageResult[] // Changed from Map to array totalModels: number loading: boolean completedCount: number } export function StatsHeader({ images, totalModels, loading, completedCount }: StatsHeaderProps) { const successfulImages = images.filter((img) => img.success) const allComplete = completedCount === totalModels && !loading const stats = calculateStats(successfulImages) return (

Progress {completedCount}/{totalModels}

{allComplete && successfulImages.length > 0 && ( <>

Average {stats.avgDuration}s

Total Cost ${stats.totalCost}

{stats.fastestModel && (

Fastest {stats.fastestModel.modelName}

)} )}

) } [V0_FILE]typescriptreact:file="components/benchmark/image-grid.tsx" isMerged="true" "use client" import { Loader2, AlertCircle, Expand } from "lucide-react" import type { ImageResult, ModelConfig } from "@/lib/types" type ImageGridProps = { models: ModelConfig[] images: ImageResult[] // Changed from Map to array to maintain arrival order loading: boolean onImageClick: (imageUrl: string) => void gridColumns?: number } export function ImageGrid({ models, images, loading, onImageClick, gridColumns = 3 }: ImageGridProps) { const gridClass = { 2: "grid-cols-1 md:grid-cols-2", 3: "grid-cols-1 md:grid-cols-2 lg:grid-cols-3", 4: "grid-cols-1 md:grid-cols-2 lg:grid-cols-4", 5: "grid-cols-1 md:grid-cols-3 lg:grid-cols-5", }[gridColumns] || "grid-cols-1 md:grid-cols-2 lg:grid-cols-3" const imageMap = new Map(images.map((img) => [img.modelId, img])) const completedImages = images const pendingModels = models.filter((model) => !imageMap.has(model.id)) return (

{completedImages.map((image) => (

{ if (image.success && image.imageUrl) { onImageClick(image.imageUrl) } }} > {image.success && image.imageUrl ? ( <> {`${image.modelName}`}

) : image.error ? (

Failed

) : null}

{image.modelName}

{image.provider}

{image.success && ( <>

{image.duration}

{image.cost}

)}

))} {loading && pendingModels.map((model, index) => (

{/* Espacio vacío donde iría el nombre */}

))}

) } [V0_FILE]typescript:file="lib/models.ts" isQuickEdit="true" isMerged="true" import type { ModelConfig } from "./types" export const MODELS: ModelConfig[] = [ // { // id: "nano-banana-gateway", // name: "Nano Banana", // type: "gemini", // provider: "Vercel AI Gateway", // cost: 0.042, // Average cost for reference only // color: "#FF6B6B", // }, { id: "fal-nano-banana", name: "Nano Banana", type: "fal-ai/nano-banana", provider: "Fal AI", cost: 0.039, color: "#4ECDC4", }, { id: "fal-flux-schnell", name: "FLUX Schnell", type: "fal-ai/flux/schnell", provider: "Fal AI", cost: 0.003, // per MP for 1MP image color: "#45B7D1", }, { id: "fal-flux-dev", name: "FLUX Dev", type: "fal-ai/flux/dev", provider: "Fal AI", cost: 0.025, // per MP for 1MP image color: "#96CEB4", }, { id: "fal-flux-pro", name: "FLUX Pro v1.1", type: "fal-ai/flux-pro/v1.1", provider: "Fal AI", cost: 0.04, // per MP for 1MP image color: "#FFEAA7", }, { id: "fal-sd35-medium", name: "Stable Diffusion 3.5 Medium", type: "fal-ai/stable-diffusion-v35-medium", provider: "Fal AI", cost: 0.065, // per MP for 1MP image color: "#DFE6E9", }, { id: "flux-fast-schnell", name: "Flux Fast Schnell", type: "inference.flux-fast.schnell.txt2img.v1", provider: "Prodia", cost: 0.002, // for 1024x1024 color: "#74B9FF", }, { id: "flux-fast-dev", name: "Flux Fast Dev", type: "inference.flux-fast.dev.txt2img.v1", provider: "Prodia", cost: 0.02, // for 1024x1024 color: "#A29BFE", }, { id: "flux-pro-11", name: "Flux Pro 1.1", type: "inference.flux.pro11.txt2img.v1", provider: "Prodia", cost: 0.04, // for 1024x1024 color: "#FD79A8", }, { id: "flux-dev", name: "Flux Dev", type: "inference.flux.dev.txt2img.v2", provider: "Prodia", cost: 0.02, // for 1024x1024 color: "#FDCB6E", }, { id: "sd15", name: "Stable Diffusion 1.5", type: "inference.sd15.txt2img.v1", provider: "Prodia", cost: 0.0025, // for 512x512 color: "#6C5CE7", }, { id: "grok-2-image", name: "Grok 2 Image", type: "grok-2-image-1212", provider: "xAI", cost: 0.07, // per image color: "#FF6B35", }, ] export const MODEL_PRICING: Record = MODELS.reduce( (acc, model) => { acc[model.id] = model.cost return acc }, {} as Record, ) [V0_FILE]typescript:file="app/api/generate-single/route.ts" isQuickEdit="true" isMerged="true" import type { NextRequest } from "next/server" import { createProdia } from "prodia/v2" import * as fal from "@fal-ai/serverless-client" import { Buffer } from "buffer" import { calculateFalCost, calculateProdiaCost } from "@/lib/pricing" fal.config({ credentials: process.env.FAL_KEY, }) export async function POST(request: NextRequest) { const { prompt, model } = await request.json() if (!prompt || !model) { return Response.json({ error: "Prompt and model are required" }, { status: 400 }) } const startTime = performance.now() try { if (model.provider === "xAI") { const xaiApiKey = process.env.XAI_API_KEY if (!xaiApiKey) throw new Error("XAI_API_KEY not configured") const response = await fetch("https://api.x.ai/v1/images/generations", { method: "POST", headers: { Authorization: `Bearer ${xaiApiKey}`, "Content-Type": "application/json", }, body: JSON.stringify({ model: model.type, prompt, }), }) if (!response.ok) { const errorData = await response.json() throw new Error(`xAI API error: ${errorData.error?.message || response.statusText}`) } const result = await response.json() const endTime = performance.now() const durationMs = endTime - startTime const duration = (durationMs / 1000).toFixed(2) const imageUrl = result.data?.[0]?.url if (!imageUrl) throw new Error("No image was generated") // Download and convert to base64 const imageResponse = await fetch(imageUrl) const imageBuffer = await imageResponse.arrayBuffer() const base64Image = Buffer.from(imageBuffer).toString("base64") const base64Url = `data:image/jpeg;base64,${base64Image}` return Response.json({ modelId: model.id, modelName: model.name, provider: model.provider, imageUrl: base64Url, duration: `${duration}s`, durationMs, cost: `$${model.cost.toFixed(4)}`, success: true, }) } if (model.provider === "Fal AI") { const result = await fal.subscribe(model.type, { input: { prompt, image_size: "square_hd", num_inference_steps: model.type.includes("schnell") ? 4 : 28, num_images: 1, }, logs: false, onQueueUpdate: () => {}, }) const endTime = performance.now() const durationMs = endTime - startTime const duration = (durationMs / 1000).toFixed(2) const imageUrl = result.images?.[0]?.url if (!imageUrl) throw new Error("No image was generated") const imageResponse = await fetch(imageUrl) const imageBuffer = await imageResponse.arrayBuffer() const base64Image = Buffer.from(imageBuffer).toString("base64") const base64Url = `data:image/jpeg;base64,${base64Image}` const imageWidth = result.images[0]?.width || 1024 const imageHeight = result.images[0]?.height || 1024 const cost = calculateFalCost(model.type, imageWidth, imageHeight) return Response.json({ modelId: model.id, modelName: model.name, provider: model.provider, imageUrl: base64Url, duration: `${duration}s`, durationMs, cost: `$${cost.toFixed(4)}`, success: true, }) } const token = process.env.PRODIA_TOKEN if (!token) throw new Error("PRODIA_TOKEN not configured") const prodia = createProdia({ token }) const job = await prodia.job({ type: model.type as any, config: { prompt }, }) const endTime = performance.now() const durationMs = endTime - startTime const duration = (durationMs / 1000).toFixed(2) const imageBuffer = await job.arrayBuffer() const base64Image = Buffer.from(imageBuffer).toString("base64") const imageUrl = `data:image/jpeg;base64,${base64Image}` const resolution = "1024x1024" // Prodia default const cost = calculateProdiaCost(model.type, resolution) return Response.json({ modelId: model.id, modelName: model.name, provider: model.provider, imageUrl, duration: `${duration}s`, durationMs, cost: `$${cost.toFixed(4)}`, success: true, }) } catch (error) { const endTime = performance.now() const durationMs = endTime - startTime const duration = (durationMs / 1000).toFixed(2) return Response.json({ modelId: model.id, modelName: model.name, provider: model.provider, imageUrl: null, duration: `${duration}s`, durationMs, cost: "$0.0000", success: false, error: error instanceof Error ? error.message : "Unknown error", }) } } [V0_FILE]typescriptreact:file="components/benchmark/performance-ranking.tsx" isQuickEdit="true" isMerged="true" "use client" import type { ImageResult } from "@/lib/types" import { sortImagesBySpeed } from "@/lib/stats" type PerformanceRankingProps = { images: ImageResult[] } const modelColors: Record = { "nano-banana-gateway": "var(--ds-green-700)", "fal-nano-banana": "var(--ds-blue-700)", "fal-flux-schnell": "var(--ds-amber-700)", "fal-flux-dev": "var(--ds-red-700)", "fal-flux-pro": "var(--ds-purple-700)", "fal-sd35-medium": "var(--ds-cyan-700)", "flux-fast-schnell": "var(--ds-pink-700)", "flux-fast-dev": "var(--ds-lime-700)", "flux-pro-11": "var(--ds-orange-700)", "flux-dev": "var(--ds-teal-700)", sd15: "var(--ds-indigo-700)", "grok-2-image": "var(--ds-violet-700)", } export function PerformanceRanking({ images }: PerformanceRankingProps) { const sortedImages = sortImagesBySpeed(images) const maxDuration = Math.max(...images.map((img) => img.durationMs), 1) return (

{sortedImages.map((image, index) => { const percentage = (image.durationMs / maxDuration) * 100 const barColor = modelColors[image.modelId] || "var(--ds-gray-700)" return (

{index + 1}

{image.modelName} {image.provider}

{image.duration} {image.cost}

) })}

) } [V0_FILE]typescript:file="lib/auth.ts" isMerged="true" export const AUTH_CONFIG = { clientId: process.env.NEXT_PUBLIC_VERCEL_CLIENT_ID!, clientSecret: process.env.VERCEL_CLIENT_SECRET!, redirectUri: process.env.NEXT_PUBLIC_VERCEL_REDIRECT_URI!, authorizationEndpoint: "https://vercel.com/oauth/authorize", tokenEndpoint: "https://vercel.com/api/login/oauth/token", scopes: ["openid", "email", "profile"], } // Generate PKCE code verifier and challenge export function generateCodeVerifier(): string { const array = new Uint8Array(32) crypto.getRandomValues(array) return base64UrlEncode(array) } export async function generateCodeChallenge(verifier: string): Promise { const encoder = new TextEncoder() const data = encoder.encode(verifier) const hash = await crypto.subtle.digest("SHA-256", data) return base64UrlEncode(new Uint8Array(hash)) } function base64UrlEncode(array: Uint8Array): string { const base64 = btoa(String.fromCharCode(...array)) return base64.replace(/\+/g, "-").replace(/\//g, "_").replace(/=/g, "") } export function generateState(): string { const array = new Uint8Array(16) crypto.getRandomValues(array) return base64UrlEncode(array) } [V0_FILE]typescript:file="app/api/auth/login/route.ts" isMerged="true" import { NextResponse } from "next/server" import { AUTH_CONFIG, generateCodeVerifier, generateCodeChallenge, generateState } from "@/lib/auth" export async function GET() { try { const codeVerifier = generateCodeVerifier() const codeChallenge = await generateCodeChallenge(codeVerifier) const state = generateState() // Build authorization URL const params = new URLSearchParams({ client_id: AUTH_CONFIG.clientId, redirect_uri: AUTH_CONFIG.redirectUri, response_type: "code", scope: AUTH_CONFIG.scopes.join(" "), state, code_challenge: codeChallenge, code_challenge_method: "S256", }) const authUrl = `${AUTH_CONFIG.authorizationEndpoint}?${params.toString()}` // Store code_verifier and state in cookies for callback verification const response = NextResponse.redirect(authUrl) response.cookies.set("code_verifier", codeVerifier, { httpOnly: true, secure: process.env.NODE_ENV === "production", sameSite: "lax", maxAge: 60 * 10, // 10 minutes path: "/", }) response.cookies.set("oauth_state", state, { httpOnly: true, secure: process.env.NODE_ENV === "production", sameSite: "lax", maxAge: 60 * 10, // 10 minutes path: "/", }) return response } catch (error) { console.error("[v0] Auth login error:", error) return NextResponse.json({ error: "Failed to initialize OAuth flow" }, { status: 500 }) } } [V0_FILE]typescript:file="app/api/auth/user/route.ts" isMerged="true" import { type NextRequest, NextResponse } from "next/server" function parseJwt(token: string) { try { const base64Url = token.split(".")[1] const base64 = base64Url.replace(/-/g, "+").replace(/_/g, "/") const jsonPayload = decodeURIComponent( atob(base64) .split("") .map((c) => "%" + ("00" + c.charCodeAt(0).toString(16)).slice(-2)) .join(""), ) return JSON.parse(jsonPayload) } catch (error) { return null } } export async function GET(request: NextRequest) { try { const idToken = request.cookies.get("id_token")?.value if (!idToken) { return NextResponse.json({ user: null }, { status: 200 }) } // Decode ID token (JWT) to get user info const payload = parseJwt(idToken) if (!payload) { return NextResponse.json({ user: null }, { status: 200 }) } return NextResponse.json({ user: { id: payload.sub, name: payload.name, username: payload.preferred_username, email: payload.email, picture: payload.picture, }, }) } catch (error) { console.error("[v0] Get user error:", error) return NextResponse.json({ error: "Failed to get user" }, { status: 500 }) } } [V0_FILE]typescript:file="app/api/auth/logout/route.ts" isMerged="true" import { type NextRequest, NextResponse } from "next/server" export async function POST(request: NextRequest) { try { const response = NextResponse.json({ success: true }) // Clear auth cookies response.cookies.delete("access_token") response.cookies.delete("id_token") return response } catch (error) { console.error("[v0] Logout error:", error) return NextResponse.json({ error: "Failed to logout" }, { status: 500 }) } } [V0_FILE]xml:file="public/vercel-logo.svg" isMerged="true" [V0_FILE]typescriptreact:file="components/auth/user-menu.tsx" isEdit="true" isMerged="true" "use client" import { useState } from "react" import { LogOut, User } from "lucide-react" import { Button } from "@/components/ui/button" type UserMenuProps = { user: { name: string username: string email: string picture?: string } } export function UserMenu({ user }: UserMenuProps) { const [isOpen, setIsOpen] = useState(false) const handleLogout = async () => { await fetch("/api/auth/logout", { method: "POST" }) window.location.reload() } return (

{isOpen && ( <>

setIsOpen(false)} />

{user.name}

{user.email}

)}

) } [V0_FILE]typescript:file="lib/supabase/client.ts" isMerged="true" import { createBrowserClient } from "@supabase/ssr" export function createClient() { return createBrowserClient(process.env.NEXT_PUBLIC_SUPABASE_URL!, process.env.NEXT_PUBLIC_SUPABASE_ANON_KEY!) } [V0_FILE]typescript:file="lib/supabase/server.ts" isMerged="true" import { createServerClient } from "@supabase/ssr" import { cookies } from "next/headers" export async function createClient() { const cookieStore = await cookies() return createServerClient(process.env.NEXT_PUBLIC_SUPABASE_URL!, process.env.NEXT_PUBLIC_SUPABASE_ANON_KEY!, { cookies: { getAll() { return cookieStore.getAll() }, setAll(cookiesToSet) { try { cookiesToSet.forEach(({ name, value, options }) => cookieStore.set(name, value, options)) } catch { // The "setAll" method was called from a Server Component. // This can be ignored if you have middleware refreshing // user sessions. } }, }, }) } [V0_FILE]sql:file="scripts/001_create_tracking_tables.sql" isMerged="true" -- IP Usage Tracking Table CREATE TABLE IF NOT EXISTS public.ip_usage ( id UUID PRIMARY KEY DEFAULT gen_random_uuid(), ip_address TEXT NOT NULL UNIQUE, prompt_count INTEGER DEFAULT 0, last_used_at TIMESTAMPTZ DEFAULT NOW(), created_at TIMESTAMPTZ DEFAULT NOW() ); -- User Login Tracking Table CREATE TABLE IF NOT EXISTS public.user_logins ( id UUID PRIMARY KEY DEFAULT gen_random_uuid(), vercel_user_id TEXT NOT NULL UNIQUE, email TEXT, name TEXT, avatar_url TEXT, first_login_at TIMESTAMPTZ DEFAULT NOW(), last_login_at TIMESTAMPTZ DEFAULT NOW(), total_prompts INTEGER DEFAULT 0, created_at TIMESTAMPTZ DEFAULT NOW() ); -- Prompt Generation History CREATE TABLE IF NOT EXISTS public.prompt_history ( id UUID PRIMARY KEY DEFAULT gen_random_uuid(), ip_address TEXT, vercel_user_id TEXT, prompt TEXT NOT NULL, model_count INTEGER NOT NULL, success_count INTEGER NOT NULL, total_cost DECIMAL(10, 4), created_at TIMESTAMPTZ DEFAULT NOW() ); -- Enable Row Level Security (RLS) ALTER TABLE public.ip_usage ENABLE ROW LEVEL SECURITY; ALTER TABLE public.user_logins ENABLE ROW LEVEL SECURITY; ALTER TABLE public.prompt_history ENABLE ROW LEVEL SECURITY; -- RLS Policies for ip_usage (allow all reads, service role can write) CREATE POLICY "Allow public read access to ip_usage" ON public.ip_usage FOR SELECT USING (true); CREATE POLICY "Allow service role to manage ip_usage" ON public.ip_usage FOR ALL USING (true); -- RLS Policies for user_logins (allow all reads, service role can write) CREATE POLICY "Allow public read access to user_logins" ON public.user_logins FOR SELECT USING (true); CREATE POLICY "Allow service role to manage user_logins" ON public.user_logins FOR ALL USING (true); -- RLS Policies for prompt_history (allow all reads, service role can write) CREATE POLICY "Allow public read access to prompt_history" ON public.prompt_history FOR SELECT USING (true); CREATE POLICY "Allow service role to manage prompt_history" ON public.prompt_history FOR ALL USING (true); -- Create indexes for performance CREATE INDEX IF NOT EXISTS idx_ip_usage_ip_address ON public.ip_usage(ip_address); CREATE INDEX IF NOT EXISTS idx_user_logins_vercel_user_id ON public.user_logins(vercel_user_id); CREATE INDEX IF NOT EXISTS idx_prompt_history_created_at ON public.prompt_history(created_at DESC); [V0_FILE]typescript:file="app/api/track-login/route.ts" isMerged="true" import { type NextRequest, NextResponse } from "next/server" import { createClient } from "@/lib/supabase/server" export async function POST(request: NextRequest) { try { const supabase = await createClient() const { userId, email, name, avatarUrl } = await request.json() if (!userId) { return NextResponse.json({ error: "User ID is required" }, { status: 400 }) } // Check if user exists const { data: existingUser, error: fetchError } = await supabase .from("user_logins") .select("*") .eq("vercel_user_id", userId) .single() if (fetchError && fetchError.code !== "PGRST116") { throw fetchError } if (existingUser) { // Update existing user const { data, error } = await supabase .from("user_logins") .update({ email, name, avatar_url: avatarUrl, last_login_at: new Date().toISOString(), }) .eq("vercel_user_id", userId) .select() .single() if (error) throw error return NextResponse.json({ success: true, user: data }) } else { // Create new user record const { data, error } = await supabase .from("user_logins") .insert({ vercel_user_id: userId, email, name, avatar_url: avatarUrl, }) .select() .single() if (error) throw error return NextResponse.json({ success: true, user: data, isNewUser: true }) } } catch (error) { console.error("Error tracking login:", error) return NextResponse.json({ error: "Failed to track login" }, { status: 500 }) } } [V0_FILE]typescript:file="app/api/track-prompt/route.ts" isMerged="true" import { type NextRequest, NextResponse } from "next/server" import { createClient } from "@/lib/supabase/server" export async function POST(request: NextRequest) { try { const supabase = await createClient() const { prompt, modelCount, successCount, totalCost, userId } = await request.json() // Get IP address from headers const forwarded = request.headers.get("x-forwarded-for") const ip = forwarded ? forwarded.split(",")[0] : request.headers.get("x-real-ip") || "unknown" // Save prompt to history const { data, error } = await supabase .from("prompt_history") .insert({ ip_address: ip, vercel_user_id: userId || null, prompt, model_count: modelCount, success_count: successCount, total_cost: totalCost, }) .select() .single() if (error) throw error // If user is logged in, update their total prompts if (userId) { const { data: user, error: userError } = await supabase .from("user_logins") .select("total_prompts") .eq("vercel_user_id", userId) .single() if (user && !userError) { await supabase .from("user_logins") .update({ total_prompts: user.total_prompts + 1, }) .eq("vercel_user_id", userId) } } return NextResponse.json({ success: true, data }) } catch (error) { console.error("Error tracking prompt:", error) return NextResponse.json({ error: "Failed to track prompt" }, { status: 500 }) } } [V0_FILE]typescript:file="app/api/analytics/route.ts" isMerged="true" import { NextResponse } from "next/server" import { createClient } from "@/lib/supabase/server" export async function GET() { try { const supabase = await createClient() // Get total users const { count: totalUsers, error: usersError } = await supabase .from("user_logins") .select("*", { count: "exact", head: true }) if (usersError) throw usersError // Get total prompts const { count: totalPrompts, error: promptsError } = await supabase .from("prompt_history") .select("*", { count: "exact", head: true }) if (promptsError) throw promptsError // Get anonymous prompts (prompts with IP but no user ID) const { count: anonymousPrompts, error: anonError } = await supabase .from("prompt_history") .select("*", { count: "exact", head: true }) .is("vercel_user_id", null) if (anonError) throw anonError // Get recent users (last 10) const { data: recentUsers, error: recentError } = await supabase .from("user_logins") .select("*") .order("last_login_at", { ascending: false }) .limit(10) if (recentError) throw recentError // Get top users by prompt count const { data: topUsers, error: topError } = await supabase .from("user_logins") .select("*") .order("total_prompts", { ascending: false }) .limit(10) if (topError) throw topError return NextResponse.json({ totalUsers: totalUsers || 0, totalPrompts: totalPrompts || 0, anonymousPrompts: anonymousPrompts || 0, authenticatedPrompts: (totalPrompts || 0) - (anonymousPrompts || 0), recentUsers, topUsers, }) } catch (error) { console.error("Error fetching analytics:", error) return NextResponse.json({ error: "Failed to fetch analytics" }, { status: 500 }) } } [V0_FILE]typescript:file="app/api/auth/callback/route.ts" isEdit="true" isQuickEdit="true" isMerged="true" import { type NextRequest, NextResponse } from "next/server" import { AUTH_CONFIG } from "@/lib/auth" export async function GET(request: NextRequest) { try { const searchParams = request.nextUrl.searchParams const code = searchParams.get("code") const state = searchParams.get("state") const error = searchParams.get("error") if (error) { const errorDescription = searchParams.get("error_description") console.error("[v0] OAuth error:", error, errorDescription) return NextResponse.redirect( new URL(`/?auth_error=${encodeURIComponent(errorDescription || error)}`, request.url), ) } if (!code || !state) { return NextResponse.redirect(new URL("/?auth_error=missing_code_or_state", request.url)) } const storedState = request.cookies.get("oauth_state")?.value if (state !== storedState) { return NextResponse.redirect(new URL("/?auth_error=invalid_state", request.url)) } const codeVerifier = request.cookies.get("code_verifier")?.value if (!codeVerifier) { return NextResponse.redirect(new URL("/?auth_error=missing_code_verifier", request.url)) } const tokenResponse = await fetch(AUTH_CONFIG.tokenEndpoint, { method: "POST", headers: { "Content-Type": "application/x-www-form-urlencoded", }, body: new URLSearchParams({ grant_type: "authorization_code", client_id: AUTH_CONFIG.clientId, client_secret: AUTH_CONFIG.clientSecret, code, code_verifier: codeVerifier, redirect_uri: AUTH_CONFIG.redirectUri, }), }) if (!tokenResponse.ok) { const errorData = await tokenResponse.text() console.error("[v0] Token exchange failed:", errorData) return NextResponse.redirect(new URL("/?auth_error=token_exchange_failed", request.url)) } const tokens = await tokenResponse.json() const idToken = tokens.id_token let userId = null let userEmail = null let userName = null let userAvatar = null if (idToken) { try { const base64Url = idToken.split(".")[1] const base64 = base64Url.replace(/-/g, "+").replace(/_/g, "/") const jsonPayload = decodeURIComponent( atob(base64) .split("") .map((c) => "%" + ("00" + c.charCodeAt(0).toString(16)).slice(-2)) .join(""), ) const payload = JSON.parse(jsonPayload) userId = payload.sub userEmail = payload.email userName = payload.name userAvatar = payload.picture // Track login in database if (userId) { await fetch(new URL("/api/track-login", request.url).toString(), { method: "POST", headers: { "Content-Type": "application/json" }, body: JSON.stringify({ userId, email: userEmail, name: userName, avatarUrl: userAvatar, }), }) } } catch (parseError) { console.error("[v0] Failed to parse ID token:", parseError) } } const response = NextResponse.redirect(new URL("/", request.url)) response.cookies.set("access_token", tokens.access_token, { httpOnly: true, secure: process.env.NODE_ENV === "production", sameSite: "lax", maxAge: tokens.expires_in || 3600, path: "/", }) if (tokens.id_token) { response.cookies.set("id_token", tokens.id_token, { httpOnly: true, secure: process.env.NODE_ENV === "production", sameSite: "lax", maxAge: tokens.expires_in || 3600, path: "/", }) } response.cookies.delete("code_verifier") response.cookies.delete("oauth_state") return response } catch (error) { console.error("[v0] Callback error:", error) return NextResponse.redirect(new URL("/?auth_error=unexpected_error", request.url)) } } [V0_FILE]typescript:file="app/api/track-ip/route.ts" isEdit="true" isMerged="true" import { type NextRequest, NextResponse } from "next/server" import { createClient } from "@/lib/supabase/server" export async function POST(request: NextRequest) { try { const supabase = await createClient() // Get IP address from headers const forwarded = request.headers.get("x-forwarded-for") const ip = forwarded ? forwarded.split(",")[0] : request.headers.get("x-real-ip") || "unknown" const { data: existingIp, error: fetchError } = await supabase .from("ip_usage") .select("*") .eq("ip_address", ip) .maybeSingle() if (fetchError) { throw fetchError } if (existingIp) { // Update existing IP const { data, error } = await supabase .from("ip_usage") .update({ prompt_count: existingIp.prompt_count + 1, last_used_at: new Date().toISOString(), }) .eq("ip_address", ip) .select() .single() if (error) throw error return NextResponse.json({ promptCount: data.prompt_count, canGenerate: data.prompt_count < 1, }) } else { // Create new IP record const { data, error } = await supabase .from("ip_usage") .insert({ ip_address: ip, prompt_count: 1, }) .select() .single() if (error) throw error return NextResponse.json({ promptCount: data.prompt_count, canGenerate: data.prompt_count < 1, }) } } catch (error) { console.error("Error tracking IP:", error) return NextResponse.json({ error: "Failed to track IP" }, { status: 500 }) } } export async function GET(request: NextRequest) { try { const supabase = await createClient() // Get IP address from headers const forwarded = request.headers.get("x-forwarded-for") const ip = forwarded ? forwarded.split(",")[0] : request.headers.get("x-real-ip") || "unknown" const { data, error } = await supabase.from("ip_usage").select("*").eq("ip_address", ip).maybeSingle() if (error) { throw error } return NextResponse.json({ promptCount: data?.prompt_count || 0, canGenerate: (data?.prompt_count || 0) < 1, }) } catch (error) { console.error("Error fetching IP:", error) return NextResponse.json({ error: "Failed to fetch IP" }, { status: 500 }) } } [V0_FILE]typescript:file="lib/storage.ts" isEdit="true" isMerged="true" import type { TestResult } from "./types" export function loadHistory(): TestResult[] { return [] } export function saveHistory(_history: TestResult[]): void { // No-op: history only in memory } export function clearHistory(): void { // No-op: history only in memory } [V0_FILE]typescriptreact:file="components/auth/sign-in-modal.tsx" isEdit="true" isQuickEdit="true" isMerged="true" "use client" import { X } from "lucide-react" import { Button } from "@/components/ui/button" type SignInModalProps = { onClose: () => void currentGenerations?: Array<{ prompt: string timestamp: number images: Array<{ modelId: string modelName: string provider: string imageUrl: string | null duration: string durationMs: number cost: string success: boolean error?: string }> }> } export function SignInModal({ onClose, currentGenerations }: SignInModalProps) { const handleSignIn = () => { if (currentGenerations && currentGenerations.length > 0) { try { sessionStorage.setItem("saved_generations", JSON.stringify(currentGenerations)) } catch (e) { console.error("Failed to save generations:", e) } } window.location.href = "/api/auth/login" } return (

e.stopPropagation()}>

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) } [V0_FILE]json:file="package.json" isEdit="true" isMerged="true" { "name": "my-v0-project", "version": "0.1.0", "private": true, "scripts": { "dev": "next dev", "build": "next build", "start": "next start", "lint": "eslint ." }, "dependencies": { "@hookform/resolvers": "^3.10.0", "@radix-ui/react-accordion": "1.2.2", "@radix-ui/react-alert-dialog": "1.1.4", "@radix-ui/react-aspect-ratio": "1.1.1", "@radix-ui/react-avatar": "1.1.2", "@radix-ui/react-checkbox": "1.1.3", "@radix-ui/react-collapsible": "1.1.2", "@radix-ui/react-context-menu": "2.2.4", "@radix-ui/react-dialog": "1.1.4", "@radix-ui/react-dropdown-menu": "2.1.4", "@radix-ui/react-hover-card": "1.1.4", "@radix-ui/react-label": "2.1.1", "@radix-ui/react-menubar": "1.1.4", "@radix-ui/react-navigation-menu": "1.2.3", "@radix-ui/react-popover": "1.1.4", "@radix-ui/react-progress": "1.1.1", "@radix-ui/react-radio-group": "1.2.2", "@radix-ui/react-scroll-area": "1.2.2", "@radix-ui/react-select": "2.1.4", "@radix-ui/react-separator": "1.1.1", "@radix-ui/react-slider": "1.2.2", "@radix-ui/react-slot": "1.1.1", "@radix-ui/react-switch": "1.1.2", "@radix-ui/react-tabs": "1.1.2", "@radix-ui/react-toast": "1.2.4", "@radix-ui/react-toggle": "1.1.1", "@radix-ui/react-toggle-group": "1.1.1", "@radix-ui/react-tooltip": "1.1.6", "@vercel/analytics": "1.3.1", "autoprefixer": "^10.4.20", "class-variance-authority": "^0.7.1", "clsx": "^2.1.1", "cmdk": "1.0.4", "date-fns": "4.1.0", "embla-carousel-react": "8.5.1", "input-otp": "1.4.1", "lucide-react": "^0.454.0", "next": "16.0.0", "next-themes": "^0.4.6", "react": "19.2.0", "react-day-picker": "9.8.0", "react-dom": "19.2.0", "react-hook-form": "^7.60.0", "react-resizable-panels": "^2.1.7", "recharts": "2.15.4", "sonner": "^1.7.4", "tailwind-merge": "^3.3.1", "tailwindcss-animate": "^1.0.7", "vaul": "^0.9.9", "zod": "3.25.76", "prodia": "1.6.0", "ai": "5.0.86", "@ai-sdk/google": "2.0.27", "@fal-ai/serverless-client": "0.15.0", "buffer": "6.0.3", "html-to-image": "^1.11.11", "@supabase/ssr": "0.7.0" }, "devDependencies": { "@tailwindcss/postcss": "^4.1.9", "@types/node": "^22", "@types/react": "^19", "@types/react-dom": "^19", "postcss": "^8.5", "tailwindcss": "^4.1.9", "tw-animate-css": "1.3.3", "typescript": "^5" } } [V0_FILE]typescriptreact:file="app/layout.tsx" isEdit="true" isMerged="true" import type React from "react" import type { Metadata } from "next" import { Geist, Geist_Mono } from "next/font/google" import { Analytics } from "@vercel/analytics/next" import "./globals.css" const _geist = Geist({ subsets: ["latin"] }) const _geistMono = Geist_Mono({ subsets: ["latin"] }) export const metadata: Metadata = { title: "AI Image Generation Benchmark - Model Performance Comparison", description: "Compare image generation speed, cost, and quality across multiple AI models and providers", generator: "v0.app", icons: { icon: "/favicon.svg", }, } export default function RootLayout({ children, }: Readonly<{ children: React.ReactNode }>) { return ( {children} ) } [V0_FILE]typescript:file="app/api/check-user-limit/route.ts" isMerged="true" import { type NextRequest, NextResponse } from "next/server" import { createClient } from "@/lib/supabase/server" export async function GET(request: NextRequest) { try { const supabase = await createClient() const { searchParams } = new URL(request.url) const userId = searchParams.get("userId") if (!userId) { return NextResponse.json({ canGenerate: false, promptsUsed: 0, promptsLimit: 5 }) } // Get user's total prompts const { data: user, error } = await supabase .from("user_logins") .select("total_prompts") .eq("vercel_user_id", userId) .maybeSingle() if (error) throw error const promptsUsed = user?.total_prompts || 0 const promptsLimit = 5 const canGenerate = promptsUsed < promptsLimit return NextResponse.json({ canGenerate, promptsUsed, promptsLimit, }) } catch (error) { console.error("Error checking user limit:", error) return NextResponse.json({ error: "Failed to check user limit" }, { status: 500 }) } } [V0_FILE]xml:file="public/logo.svg" isEdit="true" isMerged="true" [V0_FILE]xml:file="public/favicon.svg" isEdit="true" isMerged="true" [V0_FILE]typescriptreact:file="app/page.tsx" isEdit="true" isQuickEdit="true" isMerged="true" "use client" import { useRef, useState, useEffect } from "react" import type React from "react" import { Button } from "@/components/ui/button" import { Input } from "@/components/ui/input" import { Loader2, HelpCircle, Grid2X2, LogIn } from "lucide-react" import { MODELS } from "@/lib/models" import { saveHistory } from "@/lib/storage" import type { ImageResult, TestResult } from "@/lib/types" import { StatsHeader } from "@/components/benchmark/stats-header" import { ImageGrid } from "@/components/benchmark/image-grid" import { PerformanceRanking } from "@/components/benchmark/performance-ranking" import { HistoricalChart } from "@/components/benchmark/historical-chart" import { ImageModal } from "@/components/benchmark/image-modal" import { HowItWorksModal } from "@/components/how-it-works-modal" import { SignInModal } from "@/components/auth/sign-in-modal" import { LimitReachedModal } from "@/components/auth/limit-reached-modal" import { UserMenu } from "@/components/auth/user-menu" type GenerationGroup = { prompt: string timestamp: number images: ImageResult[] } type User = { id: string name: string username: string email: string picture?: string } export default function Home() { const [prompt, setPrompt] = useState("") const [generations, setGenerations] = useState([]) const [loading, setLoading] = useState(false) const [error, setError] = useState("") const [history, setHistory] = useState([]) const [expandedImage, setExpandedImage] = useState(null) const [showHowItWorks, setShowHowItWorks] = useState(false) const [hasRunTest, setHasRunTest] = useState(false) const [completedCount, setCompletedCount] = useState(0) const [sidebarWidth, setSidebarWidth] = useState(400) const [isResizing, setIsResizing] = useState(false) const [isSidebarCollapsed, setIsSidebarCollapsed] = useState(false) const [isHoveringHandle, setIsHoveringHandle] = useState(false) const [isHoveringCollapsedArea, setIsHoveringCollapsedArea] = useState(false) const [resizeStartX, setResizeStartX] = useState(0) const [hasDragged, setHasDragged] = useState(false) const [gridColumns, setGridColumns] = useState(3) const sidebarRef = useRef(null) const [user, setUser] = useState(null) const [showSignInModal, setShowSignInModal] = useState(false) const [showLimitReachedModal, setShowLimitReachedModal] = useState(false) const [signingIn, setSigningIn] = useState(false) useEffect(() => { const isSigningIn = localStorage.getItem("signing_in") === "true" if (isSigningIn) { setSigningIn(true) } }, []) useEffect(() => { const initializeAuth = async () => { try { const userRes = await fetch("/api/auth/user") const userData = await userRes.json() if (userData.user) { localStorage.removeItem("signing_in") setSigningIn(false) setUser(userData.user) const savedGenerations = sessionStorage.getItem("saved_generations") const savedPrompt = sessionStorage.getItem("saved_prompt") if (savedGenerations) { try { const parsedGenerations = JSON.parse(savedGenerations) setGenerations(parsedGenerations) setHasRunTest(true) if (parsedGenerations.length > 0) { const restoredHistory: TestResult[] = parsedGenerations.map((gen: GenerationGroup, index: number) => ({ testId: index + 1, timestamp: gen.timestamp, prompt: gen.prompt, results: gen.images, })) setHistory(restoredHistory) saveHistory(restoredHistory) setPrompt(parsedGenerations[parsedGenerations.length - 1].prompt) } sessionStorage.removeItem("saved_generations") } catch (e) { console.error("Failed to restore generations:", e) } } // Restore the prompt that was typed but not submitted if (savedPrompt) { setPrompt(savedPrompt) sessionStorage.removeItem("saved_prompt") } // Track login await fetch("/api/track-login", { method: "POST", headers: { "Content-Type": "application/json" }, body: JSON.stringify({ userId: userData.user.id, email: userData.user.email, name: userData.user.name, avatarUrl: userData.user.picture, }), }) } } catch (err) { console.error("Failed to initialize auth:", err) } } initializeAuth() }, []) const handleTitleClick = () => { setHasRunTest(false) setGenerations([]) setCompletedCount(0) setLoading(false) setError("") setPrompt("") sessionStorage.clear() } const checkCanGenerate = async (): Promise => { // Check logged-in user limit (5 prompts) if (user) { try { const response = await fetch(`/api/check-user-limit?userId=${user.id}`) const data = await response.json() if (!data.canGenerate) { setShowLimitReachedModal(true) return false } return true } catch (error) { console.error("Failed to check user limit:", error) return false } } // Check IP limit for non-logged users (1 prompt) try { const response = await fetch("/api/track-ip") const data = await response.json() if (!data.canGenerate) { if (prompt.trim()) { sessionStorage.setItem("saved_prompt", prompt) } setShowSignInModal(true) return false } return true } catch (error) { console.error("Failed to check IP usage:", error) return false } } const generateImages = async () => { if (!prompt.trim()) { setError("Please enter a prompt") return } setLoading(true) setError("") const currentPrompt = prompt const newGeneration: GenerationGroup = { prompt: currentPrompt, timestamp: Date.now(), images: [], } // Show optimistic UI immediately setGenerations((prev) => [...prev, newGeneration]) setCompletedCount(0) setHasRunTest(true) // Check permissions in background const canGenerate = await checkCanGenerate() if (!canGenerate) { // Revert optimistic UI if permission check fails setLoading(false) setGenerations((prev) => prev.slice(0, -1)) setHasRunTest((prev) => prev.length > 0) return } // Track IP usage for non-logged users if (!user) { try { await fetch("/api/track-ip", { method: "POST" }) } catch (error) { console.error("Failed to track IP:", error) } } try { const promises = MODELS.map(async (model) => { try { const response = await fetch("/api/generate-single", { method: "POST", headers: { "Content-Type": "application/json" }, body: JSON.stringify({ prompt: currentPrompt, model }), }) const result: ImageResult = await response.json() return { result, completionTime: performance.now() } } catch (err) { const errorResult: ImageResult = { modelId: model.id, modelName: model.name, provider: model.provider, imageUrl: null, duration: "0s", durationMs: 0, cost: "$0.0000", success: false, error: err instanceof Error ? err.message : "Unknown error", } return { result: errorResult, completionTime: performance.now() } } }) const completedResults: Array<{ result: ImageResult; completionTime: number }> = [] for (const promise of promises) { promise.then((data) => { completedResults.push(data) completedResults.sort((a, b) => a.completionTime - b.completionTime) setGenerations((prev) => { const updated = [...prev] const lastIndex = updated.length - 1 if (lastIndex >= 0) { updated[lastIndex] = { ...updated[lastIndex], images: completedResults.map((r) => r.result), } } return updated }) setCompletedCount(completedResults.length) }) } await Promise.all(promises) // Save to history and track prompt setGenerations((prev) => { const lastGen = prev[prev.length - 1] if (lastGen) { const newTest: TestResult = { testId: history.length + 1, timestamp: Date.now(), prompt: currentPrompt, results: lastGen.images, } const updatedHistory = [...history, newTest] setHistory(updatedHistory) saveHistory(updatedHistory) const successCount = lastGen.images.filter((img) => img.success).length const totalCost = lastGen.images .filter((img) => img.success) .reduce((sum, img) => sum + Number.parseFloat(img.cost.replace("$", "")), 0) fetch("/api/track-prompt", { method: "POST", headers: { "Content-Type": "application/json" }, body: JSON.stringify({ prompt: currentPrompt, modelCount: MODELS.length, successCount, totalCost: totalCost.toFixed(4), userId: user?.id || null, }), }).catch((err) => console.error("Failed to track prompt:", err)) } return prev }) } catch (err) { setError(err instanceof Error ? err.message : "Unknown error") } finally { setLoading(false) } } const handleMouseDown = (e: React.MouseEvent) => { e.preventDefault() e.stopPropagation() const currentWidth = isSidebarCollapsed ? 0 : sidebarWidth setResizeStartX(e.clientX) setIsResizing(true) setHasDragged(false) } useEffect(() => { const handleMouseMove = (e: MouseEvent) => { if (!isResizing) return const dragDistance = Math.abs(e.clientX - resizeStartX) if (dragDistance < 5) return if (!hasDragged) { setHasDragged(true) if (isSidebarCollapsed) { setIsSidebarCollapsed(false) } } const distanceFromRight = window.innerWidth - e.clientX const newWidth = Math.max(0, distanceFromRight - 32) if (newWidth < 200) { if (!isSidebarCollapsed) { setIsSidebarCollapsed(true) } return } if (isSidebarCollapsed && newWidth >= 200) { setIsSidebarCollapsed(false) } const constrainedWidth = Math.min(Math.max(newWidth, 250), 600) setSidebarWidth(constrainedWidth) } const handleMouseUp = () => { setIsResizing(false) setHasDragged(false) } if (isResizing) { document.body.style.cursor = "col-resize" document.body.style.userSelect = "none" document.addEventListener("mousemove", handleMouseMove) document.addEventListener("mouseup", handleMouseUp) } else { document.body.style.cursor = "" document.body.style.userSelect = "" } return () => { document.body.style.cursor = "" document.body.style.userSelect = "" document.removeEventListener("mousemove", handleMouseMove) document.removeEventListener("mouseup", handleMouseUp) } }, [isResizing, isSidebarCollapsed, sidebarWidth, resizeStartX, hasDragged]) const handleKeyPress = (e: React.KeyboardEvent) => { if (e.key === "Enter" && !loading) { generateImages() } } const handleSignIn = () => { setSigningIn(true) localStorage.setItem("signing_in", "true") window.location.href = "/api/auth/login" } const latestGeneration = generations[generations.length - 1] const currentImages = latestGeneration?.images || [] const allComplete = completedCount === MODELS.length && !loading return (

{hasRunTest && (

AI Image Generation Benchmark

Compare image generation performance across {MODELS.length} models

Grid:

{[2, 3, 4, 5].map((cols) => ( ))}

{user ? ( ) : ( )}

setPrompt(e.target.value)} onKeyPress={handleKeyPress} disabled={loading} autoFocus className="flex-1 font-mono text-base h-20 sm:h-12" />

{error && (

{error}

)}

)} {!hasRunTest && (

{user ? ( ) : ( )}

AI Image Generation Benchmark

Compare image generation performance across {MODELS.length} models

setPrompt(e.target.value)} onKeyPress={handleKeyPress} disabled={loading} autoFocus className="flex-1 font-mono text-base h-20 sm:h-14" />

{error && (

{error}

)}

{generations.length > 0 && (

{[...generations].reverse().map((generation, reversedIndex) => { const genIndex = generations.length - 1 - reversedIndex const isLatest = genIndex === generations.length - 1 const genSuccessfulImages = generation.images.filter((img) => img.success) const genAllComplete = isLatest ? allComplete : true return (

Prompt: {generation.prompt}

{new Date(generation.timestamp).toLocaleString()}

{/* Main content area - full width on mobile, constrained on desktop */}

{/* Sidebar - below content on mobile, beside on desktop */}

{isSidebarCollapsed && isLatest && (

setIsHoveringCollapsedArea(true)} onMouseLeave={() => setIsHoveringCollapsedArea(false)} onMouseDown={handleMouseDown} style={{ width: "48px", height: "100%", cursor: "pointer", }} > {isHoveringCollapsedArea && (

)}

)} {!isSidebarCollapsed && ( <> {isLatest && (

setIsHoveringHandle(true)} onMouseLeave={() => setIsHoveringHandle(false)} style={{ width: "16px" }} >

{(isHoveringHandle || isResizing) && ( <>

)}

= 1024 ? `${sidebarWidth}px` : "100%", }} >

Summary

{!genAllComplete && loading ? (

{[...Array(Math.min(completedCount || 5, MODELS.length))].map((_, i) => (

))}

) : ( genSuccessfulImages.length > 0 && )}

{isLatest && history.length >= 2 && ( )}

)}

) })}

)}

setExpandedImage(null)} /> setShowHowItWorks(false)} /> {showSignInModal && setShowSignInModal(false)} currentGenerations={generations} />} {showLimitReachedModal && setShowLimitReachedModal(false)} />}

) } [V0_FILE]typescriptreact:file="components/auth/limit-reached-modal.tsx" isEdit="true" isMerged="true" "use client" import { X, ExternalLink } from "lucide-react" import { Button } from "@/components/ui/button" type LimitReachedModalProps = { onClose: () => void } export function LimitReachedModal({ onClose }: LimitReachedModalProps) { return (

e.stopPropagation()}>

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) } [V0_FILE]typescriptreact:file="components/benchmark/historical-chart.tsx" isEdit="true" isQuickEdit="true" "use client" import { useState, useEffect } from "react" import { Maximize2, Download, X } from "lucide-react" import { toPng } from "html-to-image" import type { TestResult, ModelConfig } from "@/lib/types" type HistoricalChartProps = { history: TestResult[] models: ModelConfig[] onClearHistory: () => void compact?: boolean } type HoveredPoint = { testIndex: number modelId: string modelName: string provider: string duration: string color: string } const CHART_COLORS = [ "#3b82f6", // blue "#10b981", // emerald "#f59e0b", // amber "#ef4444", // red "#a855f7", // purple "#06b6d4", // cyan "#ec4899", // pink "#84cc16", // lime "#f97316", // orange "#14b8a6", // teal ] const ChartSVG = ({ id, compact, history, models, hoveredPoint, setHoveredPoint, selectedModels, toggleModel, maxTime, }: { id: string compact: boolean history: TestResult[] models: ModelConfig[] hoveredPoint: HoveredPoint | null setHoveredPoint: (point: HoveredPoint | null) => void selectedModels: Set toggleModel: (modelId: string) => void maxTime: number }) => { const width = compact ? 400 : 800 const height = compact ? 180 : 400 const marginLeft = compact ? 30 : 60 const marginRight = compact ? 20 : 40 const marginTop = compact ? 20 : 40 const marginBottom = compact ? 20 : 60 const chartWidth = width - marginLeft - marginRight const chartHeight = height - marginTop - marginBottom const fontSize = compact ? 8 : 12 const pointRadius = compact ? 3 : 5 const lineWidth = compact ? 2 : 3 return ( ) } export function HistoricalChart({ history, models, onClearHistory, compact = false }: HistoricalChartProps) { const [hoveredPoint, setHoveredPoint] = useState(null) const [selectedModels, setSelectedModels] = useState>(new Set(models.map((m) => m.id))) const [isFullscreen, setIsFullscreen] = useState(false) useEffect(() => { if (isFullscreen) { document.body.style.overflow = "hidden" } else { document.body.style.overflow = "" } return () => { document.body.style.overflow = "" } }, [isFullscreen]) useEffect(() => { const handleEscape = (e: KeyboardEvent) => { if (e.key === "Escape" && isFullscreen) { setIsFullscreen(false) } } if (isFullscreen) { window.addEventListener("keydown", handleEscape) } return () => { window.removeEventListener("keydown", handleEscape) } }, [isFullscreen]) if (history.length < 2) { return (

Need at least 2 prompts for historical chart

) } const allDurations = history.flatMap((test) => test.results.filter((r) => r.success).map((r) => r.durationMs)) const maxTime = Math.max(...allDurations, 1) const toggleModel = (modelId: string) => { setSelectedModels((prev) => { const next = new Set(prev) if (next.has(modelId)) { next.delete(modelId) } else { next.add(modelId) } return next }) } const downloadChart = async () => { try { const container = document.getElementById("fullscreen-download-container") if (!container) { console.error("Container not found") return } const dataUrl = await toPng(container, { quality: 1, pixelRatio: 2, backgroundColor: "#09090b", skipFonts: true, cacheBust: true, }) const link = document.createElement("a") link.download = `benchmark-history-${Date.now()}.png` link.href = dataUrl link.click() } catch (error) { // Error is expected from CORS issues with external fonts but doesn't affect download } } return ( <>

Historical Performance

{history.length} test runs

{hoveredPoint && (

{hoveredPoint.modelName}

{hoveredPoint.provider}

Duration: {hoveredPoint.duration}

)}

Model Legend

{models.map((model, modelIndex) => { const lineColor = CHART_COLORS[modelIndex % CHART_COLORS.length] const isSelected = selectedModels.has(model.id) return ( ) })}

{/* Header */}

Performance History

{history.length} test runs across all models

{/* Chart */}

{}} selectedModels={selectedModels} toggleModel={toggleModel} maxTime={maxTime} />

{/* Legend */}

Model Legend

{models.map((model, modelIndex) => { const lineColor = CHART_COLORS[modelIndex % CHART_COLORS.length] const isSelected = selectedModels.has(model.id) return (

{model.name} ({model.provider})

) })}

{isFullscreen && (

setIsFullscreen(false)} >

e.stopPropagation()}> {/* Header */}

Performance History

{history.length} test runs across all models

{/* Chart */}

{hoveredPoint && (

{hoveredPoint.modelName}

{hoveredPoint.provider}

Duration: {hoveredPoint.duration}

)}

{/* Legend */}

Model Legend

{models.map((model, modelIndex) => { const lineColor = CHART_COLORS[modelIndex % CHART_COLORS.length] const isSelected = selectedModels.has(model.id) return ( ) })}

)} ) }32:o218,d9:["$","$Le0",null,{"chatId":"hAmrQc0tcmJ","children":["$","$Le1",null,{"data":{"@\"active-block\",\"hAmrQc0tcmJ\",":{"id":"b_UjWgxrsY6h1","messageId":"3OasQTI4xUzS4vXRN9xRDdirlXyP5DCb","source":"$e2","language":"typescriptreact","type":"code-project","closed":true,"meta":{"type":"code-project","project":"Code","taskNameActive":"Hiding fullscreen on mobile","taskNameComplete":"Hid fullscreen on mobile"},"hasSourceBeenEdited":false,"forkedFromBlockId":"$undefined"},"@\"chat:vm-mode\",\"hAmrQc0tcmJ\",":false},"children":"$Le3"}]}] 32:o504,e4:"PublishedPageProvider" e5:I[845210,["$31","$32","$33","$34","$35","$36","$37","$38","$39","$3a","$3b","$3c","$3d","$3e","$3f","$40","$41","$42","$43","$44","$45","$46","$47","$48","$49","$4a","$4b","$4c","$4d","$4e","$4f","$50","$51","$52","$62","$63","$64","$65","$66"],"$e4"] e6:"FullscreenContainer" e7:I[481556,["$31","$32","$33","$34","$35","$36","$37","$38","$39","$3a","$3b","$3c","$3d","$3e","$3f","$40","$41","$42","$43","$44","$45","$46","$47","$48","$49","$4a","$4b","$4c","$4d","$4e","$4f","$50","$51","$52","$62","$63","$64","$65","$66"],"$e6"] e8:"TemplateBlockViewClient" e9:I[552151,["$31","$32","$33","$34","$35","$36","$37","$38","$39","$3a","$3b","$3c","$3d","$3e","$3f","$40","$41","$42","$43","$44","$45","$46","$47","$48","$49","$4a","$4b","$4c","$4d","$4e","$4f","$50","$51","$52","$62","$63","$64","$65","$66"],"$e8"] e3:["$","$Le5",null,{"children":["$","$Le7",null,{"mode":"$undefined","children":["$","$Le9",null,{"block":"$d9:props:children:props:data:@\"active-block\",\"hAmrQc0tcmJ\",","allowMobileFullscreen":true,"initialSize":"$undefined","title":"AI Image Generation Benchmark","templateCanonId":"kz69KY1ux5H","templateVersionId":"FBfnXxXObMA","authorizePublicFrame":true,"vmEnabled":false,"canViewConsole":false,"vercelDeploymentUrl":null}]}]}] 32:o11,30:true 2f:44423 32:o22,2e:C 2d:C 1:C 25:C 20:C 1b:C 18:C 32:C e5:T1bc7b,[V0_FILE]typescript:file="lib/types.ts" isMerged="true" export type ImageResult = { modelId: string modelName: string provider: string imageUrl: string | null duration: string durationMs: number cost: string success: boolean error?: string tokens?: { input: number output: number total: number } } export type TestResult = { testId: number timestamp: number prompt: string results: ImageResult[] } export type ModelConfig = { id: string name: string type: string provider: "Vercel AI Gateway" | "Fal AI" | "Prodia" cost: number color: string } [V0_FILE]typescript:file="lib/stats.ts" isMerged="true" import type { ImageResult } from "./types" export function calculateStats(images: ImageResult[]) { const successful = images.filter((img) => img.success) if (successful.length === 0) { return { avgDuration: "0", totalCost: "0.0000", fastestModel: null, } } const avgDuration = (successful.reduce((sum, img) => sum + img.durationMs, 0) / successful.length / 1000).toFixed(2) const totalCost = successful.reduce((sum, img) => sum + Number.parseFloat(img.cost.replace("$", "")), 0).toFixed(4) const fastestModel = successful.reduce((prev, curr) => (prev.durationMs < curr.durationMs ? prev : curr)) return { avgDuration, totalCost, fastestModel, } } export function sortImagesBySpeed(images: ImageResult[]): ImageResult[] { return [...images].sort((a, b) => a.durationMs - b.durationMs) } [V0_FILE]typescript:file="lib/pricing.ts" isQuickEdit="true" isMerged="true" // Fal AI pricing per megapixel export const FAL_PRICING = { "fal-ai/flux/schnell": 0.003, // per MP "fal-ai/flux/dev": 0.025, // per MP "fal-ai/flux-pro/v1.1": 0.04, // per MP "fal-ai/stable-diffusion-v35-medium": 0.02, // per MP "fal-ai/nano-banana": 0.04, // flat rate per image } // Prodia pricing by resolution and model export const PRODIA_PRICING = { "inference.flux-fast.schnell.txt2img.v1": { "1024x1024": 0.002, "512x512": 0.001 }, "inference.flux-fast.dev.txt2img.v1": { "1024x1024": 0.02 }, "inference.flux.pro11.txt2img.v1": { "1024x1024": 0.04 }, "inference.flux.dev.txt2img.v2": { "1024x1024": 0.02 }, "inference.sd15.txt2img.v1": { "512x512": 0.0025, "1024x1024": 0.008 }, } // Gemini pricing (per million tokens) export const GEMINI_PRICING = { input: 0.3, // $0.30 per 1M tokens output: 2.5, // $2.50 per 1M tokens } /** * Calculate cost for Fal AI models based on image dimensions */ export function calculateFalCost(modelType: string, width = 1024, height = 1024): number { const flatRate = FAL_PRICING[modelType as keyof typeof FAL_PRICING] return flatRate || 0 } /** * Calculate cost for Prodia models based on resolution */ export function calculateProdiaCost(modelType: string, resolution = "1024x1024"): number { const pricing = PRODIA_PRICING[modelType as keyof typeof PRODIA_PRICING] if (!pricing) return 0 // Use the specific resolution pricing or fallback to first available return pricing[resolution as keyof typeof pricing] || Object.values(pricing)[0] || 0 } /** * Calculate cost for Gemini models based on token usage */ export function calculateGeminiCost(inputTokens: number, outputTokens: number): number { const inputCost = (inputTokens / 1_000_000) * GEMINI_PRICING.input const outputCost = (outputTokens / 1_000_000) * GEMINI_PRICING.output return inputCost + outputCost } [V0_FILE]css:file="app/globals.css" isQuickEdit="true" isMerged="true" @import "tailwindcss"; @import "tw-animate-css"; @custom-variant dark (&:is(.dark *)); :root { --background: oklch(0.1 0 0); --foreground: oklch(0.95 0 0); --card: oklch(0.15 0 0); --card-foreground: oklch(0.95 0 0); --popover: oklch(0.15 0 0); --popover-foreground: oklch(0.95 0 0); --primary: oklch(0.95 0 0); --primary-foreground: oklch(0.1 0 0); --secondary: oklch(0.2 0 0); --secondary-foreground: oklch(0.95 0 0); --muted: oklch(0.18 0 0); --muted-foreground: oklch(0.6 0 0); --accent: oklch(0.25 0 0); --accent-foreground: oklch(0.95 0 0); --destructive: oklch(0.5 0.2 25); --destructive-foreground: oklch(1 0 0); --border: oklch(0.25 0 0); --input: oklch(0.25 0 0); --ring: oklch(0.95 0 0); --chart-1: oklch(0.8 0 0); --chart-2: oklch(0.7 0 0); --chart-3: oklch(0.6 0 0); --chart-4: oklch(0.5 0 0); --chart-5: oklch(0.4 0 0); /* Set radius to 0 to remove all rounded corners */ --radius: 0; --sidebar: oklch(0.15 0 0); --sidebar-foreground: oklch(0.95 0 0); --sidebar-primary: oklch(0.95 0 0); --sidebar-primary-foreground: oklch(0.1 0 0); --sidebar-accent: oklch(0.25 0 0); --sidebar-accent-foreground: oklch(0.95 0 0); --sidebar-border: oklch(0.25 0 0); --sidebar-ring: oklch(0.6 0 0); --ds-red-800: oklch(0.45 0.18 25); --ds-amber-800: oklch(0.55 0.15 65); --ds-orange-800: oklch(0.5 0.16 45); --ds-yellow-800: oklch(0.6 0.14 85); --ds-lime-800: oklch(0.55 0.12 125); --ds-green-800: oklch(0.45 0.13 145); --ds-emerald-800: oklch(0.48 0.12 165); --ds-teal-800: oklch(0.5 0.11 185); --ds-cyan-800: oklch(0.52 0.1 205); --ds-blue-800: oklch(0.48 0.14 245); --ds-indigo-800: oklch(0.45 0.15 265); --ds-violet-800: oklch(0.47 0.16 285); --ds-purple-800: oklch(0.46 0.17 305); --ds-pink-800: oklch(0.5 0.16 345); /* Adding ds-700 variants for lighter chart colors */ --ds-red-700: oklch(0.55 0.18 25); --ds-amber-700: oklch(0.65 0.15 65); --ds-orange-700: oklch(0.6 0.16 45); --ds-yellow-700: oklch(0.7 0.14 85); --ds-lime-700: oklch(0.65 0.12 125); --ds-green-700: oklch(0.55 0.13 145); --ds-emerald-700: oklch(0.58 0.12 165); --ds-teal-700: oklch(0.6 0.11 185); --ds-cyan-700: oklch(0.62 0.1 205); --ds-blue-700: oklch(0.58 0.14 245); --ds-indigo-700: oklch(0.55 0.15 265); --ds-violet-700: oklch(0.57 0.16 285); --ds-purple-700: oklch(0.56 0.17 305); --ds-pink-700: oklch(0.6 0.16 345); } .dark { --background: oklch(0.1 0 0); --foreground: oklch(0.95 0 0); --card: oklch(0.15 0 0); --card-foreground: oklch(0.95 0 0); --popover: oklch(0.15 0 0); --popover-foreground: oklch(0.95 0 0); --primary: oklch(0.95 0 0); --primary-foreground: oklch(0.1 0 0); --secondary: oklch(0.2 0 0); --secondary-foreground: oklch(0.95 0 0); --muted: oklch(0.18 0 0); --muted-foreground: oklch(0.6 0 0); --accent: oklch(0.25 0 0); --accent-foreground: oklch(0.95 0 0); --destructive: oklch(0.5 0.2 25); --destructive-foreground: oklch(1 0 0); --border: oklch(0.25 0 0); --input: oklch(0.25 0 0); --ring: oklch(0.95 0 0); --sidebar: oklch(0.15 0 0); --sidebar-foreground: oklch(0.95 0 0); --sidebar-primary: oklch(0.95 0 0); --sidebar-primary-foreground: oklch(0.1 0 0); --sidebar-accent: oklch(0.25 0 0); --sidebar-accent-foreground: oklch(0.95 0 0); --sidebar-border: oklch(0.25 0 0); --sidebar-ring: oklch(0.6 0 0); } @theme inline { --font-sans: "Geist", "Geist Fallback"; --font-mono: "Geist Mono", "Geist Mono Fallback"; --color-background: var(--background); --color-foreground: var(--foreground); --color-card: var(--card); --color-card-foreground: var(--card-foreground); --color-popover: var(--popover); --color-popover-foreground: var(--popover-foreground); --color-primary: var(--primary); --color-primary-foreground: var(--primary-foreground); --color-secondary: var(--secondary); --color-secondary-foreground: var(--secondary-foreground); --color-muted: var(--muted); --color-muted-foreground: var(--muted-foreground); --color-accent: var(--accent); --color-accent-foreground: var(--accent-foreground); --color-destructive: var(--destructive); --color-destructive-foreground: var(--destructive-foreground); --color-border: var(--border); --color-input: var(--input); --color-ring: var(--ring); --color-chart-1: var(--chart-1); --color-chart-2: var(--chart-2); --color-chart-3: var(--chart-3); --color-chart-4: var(--chart-4); --color-chart-5: var(--chart-5); --radius-sm: calc(var(--radius) - 4px); --radius-md: calc(var(--radius) - 2px); --radius-lg: var(--radius); --radius-xl: calc(var(--radius) + 4px); --color-sidebar: var(--sidebar); --color-sidebar-foreground: var(--sidebar-foreground); --color-sidebar-primary: var(--sidebar-primary); --color-sidebar-primary-foreground: var(--sidebar-primary-foreground); --color-sidebar-accent: var(--sidebar-accent); --color-sidebar-accent-foreground: var(--sidebar-accent-foreground); --color-sidebar-border: var(--sidebar-border); --color-sidebar-ring: var(--sidebar-ring); } @layer base { * { @apply border-border outline-ring/50; } body { @apply bg-background text-foreground; } } [V0_FILE]typescriptreact:file="components/how-it-works-modal.tsx" isMerged="true" "use client" import { Dialog, DialogContent, DialogHeader, DialogTitle } from "@/components/ui/dialog" interface HowItWorksModalProps { open: boolean onClose: () => void } export function HowItWorksModal({ open, onClose }: HowItWorksModalProps) { return ( ) } [V0_FILE]typescriptreact:file="components/benchmark/image-modal.tsx" isMerged="true" "use client" import { useEffect } from "react" import { X } from "lucide-react" type ImageModalProps = { imageUrl: string | null onClose: () => void } export function ImageModal({ imageUrl, onClose }: ImageModalProps) { useEffect(() => { const handleEscape = (e: KeyboardEvent) => { if (e.key === "Escape") { onClose() } } if (imageUrl) { window.addEventListener("keydown", handleEscape) } return () => { window.removeEventListener("keydown", handleEscape) } }, [imageUrl, onClose]) if (!imageUrl) return null return (

e.stopPropagation()} />

Progress {completedCount}/{totalModels}

{allComplete && successfulImages.length > 0 && ( <>

Average {stats.avgDuration}s

Total Cost ${stats.totalCost}

{stats.fastestModel && (

Fastest {stats.fastestModel.modelName}

)} )}

{completedImages.map((image) => (

{ if (image.success && image.imageUrl) { onImageClick(image.imageUrl) } }} > {image.success && image.imageUrl ? ( <> {`${image.modelName}`}

) : image.error ? (

Failed

) : null}

{image.modelName}

{image.provider}

{image.success && ( <>

{image.duration}

{image.cost}

)}

))} {loading && pendingModels.map((model, index) => (

{/* Espacio vacío donde iría el nombre */}

))}

{sortedImages.map((image, index) => { const percentage = (image.durationMs / maxDuration) * 100 const barColor = modelColors[image.modelId] || "var(--ds-gray-700)" return (

{index + 1}

{image.modelName} {image.provider}

{image.duration} {image.cost}

) })}

{isOpen && ( <>

setIsOpen(false)} />

{user.name}

{user.email}

)}

e.stopPropagation()}>

Sign in to continue

You've used your free prompt. Sign in with Vercel to continue testing models.

Free and unlimited access after signing in

{hasRunTest && (

AI Image Generation Benchmark

Compare image generation performance across {MODELS.length} models

Grid:

{[2, 3, 4, 5].map((cols) => ( ))}

{user ? ( ) : ( )}

setPrompt(e.target.value)} onKeyPress={handleKeyPress} disabled={loading} autoFocus className="flex-1 font-mono text-base h-20 sm:h-12" />

{error && (

{error}

)}

)} {!hasRunTest && (

{user ? ( ) : ( )}

AI Image Generation Benchmark

Compare image generation performance across {MODELS.length} models

setPrompt(e.target.value)} onKeyPress={handleKeyPress} disabled={loading} autoFocus className="flex-1 font-mono text-base h-20 sm:h-14" />

{error && (

{error}

)}

{generations.length > 0 && (

Prompt: {generation.prompt}

{new Date(generation.timestamp).toLocaleString()}

{/* Main content area - full width on mobile, constrained on desktop */}

{/* Sidebar - below content on mobile, beside on desktop */}

{isSidebarCollapsed && isLatest && (

)}

)} {!isSidebarCollapsed && ( <> {isLatest && (

setIsHoveringHandle(true)} onMouseLeave={() => setIsHoveringHandle(false)} style={{ width: "16px" }} >

{(isHoveringHandle || isResizing) && ( <>

)}

= 1024 ? `${sidebarWidth}px` : "100%", }} >

Summary

{!genAllComplete && loading ? (

{[...Array(Math.min(completedCount || 5, MODELS.length))].map((_, i) => (

))}

) : ( genSuccessfulImages.length > 0 && )}

{isLatest && history.length >= 2 && ( )}

)}

) })}

)}

e.stopPropagation()}>

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Need at least 2 prompts for historical chart

Historical Performance

{history.length} test runs

{hoveredPoint && (

{hoveredPoint.modelName}

{hoveredPoint.provider}

Duration: {hoveredPoint.duration}

)}

Model Legend

{models.map((model, modelIndex) => { const lineColor = CHART_COLORS[modelIndex % CHART_COLORS.length] const isSelected = selectedModels.has(model.id) return ( ) })}

{/* Header */}

Performance History

{history.length} test runs across all models

{/* Chart */}

{}} selectedModels={selectedModels} toggleModel={toggleModel} maxTime={maxTime} />

{/* Legend */}

Model Legend

{models.map((model, modelIndex) => { const lineColor = CHART_COLORS[modelIndex % CHART_COLORS.length] const isSelected = selectedModels.has(model.id) return (

{model.name} ({model.provider})

) })}

{isFullscreen && (

setIsFullscreen(false)} >

e.stopPropagation()}> {/* Header */}

Performance History

{history.length} test runs across all models

{/* Chart */}

{hoveredPoint && (

{hoveredPoint.modelName}

{hoveredPoint.provider}

Duration: {hoveredPoint.duration}

)}

{/* Legend */}

Model Legend

{models.map((model, modelIndex) => { const lineColor = CHART_COLORS[modelIndex % CHART_COLORS.length] const isSelected = selectedModels.has(model.id) return ( ) })}

)} ) }dc:["$","$Le3",null,{"chatId":"hAmrQc0tcmJ","children":["$","$Le4",null,{"data":{"@\"active-block\",\"hAmrQc0tcmJ\",":{"id":"b_UjWgxrsY6h1","messageId":"3OasQTI4xUzS4vXRN9xRDdirlXyP5DCb","source":"$e5","language":"typescriptreact","type":"code-project","closed":true,"meta":{"type":"code-project","project":"Code","taskNameActive":"Hiding fullscreen on mobile","taskNameComplete":"Hid fullscreen on mobile"},"hasSourceBeenEdited":false,"forkedFromBlockId":"$undefined"},"@\"chat:vm-mode\",\"hAmrQc0tcmJ\",":false},"children":"$Le6"}]}]

Parallel Benchmarking

What We Measure

Visualizations

Providers

Fair Testing

{image.modelName}

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AI Image Generation Benchmark

AI Image Generation Benchmark

Prompt: {generation.prompt}

Summary

You've reached your limit

Create Your Own Benchmark

Historical Performance

Performance History

Performance History

Parallel Benchmarking

What We Measure

Visualizations

Providers

Fair Testing

{image.modelName}

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AI Image Generation Benchmark

AI Image Generation Benchmark

Prompt: {generation.prompt}

Summary

You've reached your limit

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Historical Performance

Performance History

Performance History

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