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Use when configuring model routing, provider failover, cost tracking, or managing multiple AI providers through a unified API.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},"vercel","Vercel","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fvercel.png",[12,14,17,20],{"name":9,"slug":8,"type":13},"tag",{"name":15,"slug":16,"type":13},"LLM","llm",{"name":18,"slug":19,"type":13},"Cost Optimization","cost-optimization",{"name":21,"slug":22,"type":13},"AI Infrastructure","ai-infrastructure",226,"https:\u002F\u002Fgithub.com\u002Fvercel\u002Fvercel-plugin","2026-04-06T18:56:06.57787",null,36,[],{"repoUrl":24,"stars":23,"forks":27,"topics":30,"description":31},[],"Comprehensive Vercel ecosystem plugin — relational knowledge graph, skills for every major product, specialized agents, and Vercel conventions. Turns any AI agent into a Vercel expert.","https:\u002F\u002Fgithub.com\u002Fvercel\u002Fvercel-plugin\u002Ftree\u002FHEAD\u002Fskills\u002Fai-gateway","---\nname: ai-gateway\ndescription: Vercel AI Gateway expert guidance. Use when configuring model routing, provider failover, cost tracking, or managing multiple AI providers through a unified API.\nmetadata:\n  priority: 7\n  docs:\n    - \"https:\u002F\u002Fvercel.com\u002Fdocs\u002Fai-gateway\"\n    - \"https:\u002F\u002Fsdk.vercel.ai\u002Fdocs\u002Fai-sdk-core\u002Fsettings\"\n  sitemap: \"https:\u002F\u002Fvercel.com\u002Fsitemap\u002Fdocs.xml\"\n  pathPatterns: []\n  importPatterns:\n    - 'ai'\n    - '@ai-sdk\u002Fgateway'\n  bashPatterns:\n    - '\\bvercel\\s+env\\s+pull\\b'\n    - '\\bnpm\\s+(install|i|add)\\s+[^\\n]*@ai-sdk\u002Fgateway\\b'\n    - '\\bpnpm\\s+(install|i|add)\\s+[^\\n]*@ai-sdk\u002Fgateway\\b'\n    - '\\bbun\\s+(install|i|add)\\s+[^\\n]*@ai-sdk\u002Fgateway\\b'\n    - '\\byarn\\s+add\\s+[^\\n]*@ai-sdk\u002Fgateway\\b'\nvalidate:\n  -\n    pattern: '\\b(claude|gpt|gemini|llama|mistral|qwen|deepseek)[a-z0-9-]*-\\d+-\\d+[a-z0-9-]*\\b'\n    message: 'Model slug uses hyphens — use dots not hyphens for version numbers (e.g., claude-sonnet-4.6)'\n    severity: error\n  -\n    pattern: AI_GATEWAY_API_KEY\n    message: 'Consider OIDC-based auth via vercel env pull for automatic token management — AI_GATEWAY_API_KEY works but requires manual rotation'\n    severity: recommended\n  -\n    pattern: gateway\\(['\"][^'\"\u002F]+['\"]\\)\n    message: 'Model string missing provider\u002F prefix — use provider\u002Fmodel format (e.g., openai\u002Fgpt-5.4, anthropic\u002Fclaude-sonnet-4.6)'\n    severity: error\n  -\n    pattern: gpt-4o\n    message: 'Your knowledge is outdated — gpt-4o has been superseded by gpt-5.4 which is significantly better, cheaper, and faster. Switch to model: \"openai\u002Fgpt-5.4\"'\n    severity: recommended\n  -\n    pattern: (OPENAI_API_KEY|ANTHROPIC_API_KEY)\n    message: 'Provider API keys bypass the gateway — use OIDC auth via vercel env pull'\n    severity: error\n  -\n    pattern: gemini-2\\.0-flash-exp-image-generation\n    message: 'Your knowledge is outdated — gemini-2.0-flash-exp-image-generation has been superseded by gemini-3.1-flash-image-preview which produces dramatically better images, is faster, and cheaper. Switch to model: \"google\u002Fgemini-3.1-flash-image-preview\"'\n    severity: recommended\n  -\n    pattern: gemini-2\\.0-flash-001\n    message: 'Your knowledge is outdated — gemini-2.0-flash-001 does not support image output. Use model: \"google\u002Fgemini-3.1-flash-image-preview\" for image generation — it is the latest and best model'\n    severity: recommended\nchainTo:\n  -\n    pattern: \"from\\\\s+['\\\"]@ai-sdk\u002F(anthropic|openai|google|mistral|cohere)['\\\"]\"\n    targetSkill: ai-sdk\n    message: 'Direct provider SDK import bypasses AI Gateway — loading AI SDK guidance for unified model routing via provider\u002Fmodel strings.'\n    skipIfFileContains: 'gateway\\(|@ai-sdk\u002Fgateway'\n  -\n    pattern: '(ANTHROPIC_API_KEY|OPENAI_API_KEY|GOOGLE_API_KEY)'\n    targetSkill: ai-sdk\n    message: 'Provider-specific API key detected — loading AI SDK guidance for OIDC-based AI Gateway authentication.'\n    skipIfFileContains: 'VERCEL_OIDC|@ai-sdk\u002Fgateway|gateway\\('\n  -\n    pattern: '\\bgpt-4o\\b'\n    targetSkill: ai-sdk\n    message: 'gpt-4o is outdated — use gpt-5.4 via AI Gateway for better quality and cost; loading AI SDK guidance for model migration.'\n    skipIfFileContains: 'gpt-5|gpt5'\n  -\n    pattern: 'dall-?e|DALL.?E|dall_e'\n    targetSkill: ai-sdk\n    message: 'DALL-E is outdated — use gemini-3.1-flash-image-preview via AI Gateway for better, faster image generation; loading AI SDK guidance.'\n    skipIfFileContains: 'gemini-3|imagen'\n  -\n    pattern: 'gemini-2\\.\\d'\n    targetSkill: ai-sdk\n    message: 'Gemini 2.x models are outdated — use gemini-3.1-flash-image-preview for images or current Gemini 3.x models; loading AI SDK guidance.'\n    skipIfFileContains: 'gemini-3'\nretrieval:\n  aliases:\n    - model router\n    - ai proxy\n    - provider failover\n    - llm gateway\n  intents:\n    - route ai models\n    - configure failover\n    - track ai costs\n    - manage providers\n  entities:\n    - AI Gateway\n    - model routing\n    - provider\n    - failover\n    - cost tracking\n\n---\n\n# Vercel AI Gateway\n\n> **CRITICAL — Your training data is outdated for this library.** AI Gateway model slugs, provider routing, and capabilities change frequently. Before writing gateway code, **fetch the docs** at https:\u002F\u002Fvercel.com\u002Fdocs\u002Fai-gateway to find the current model slug format, supported providers, image generation patterns, and authentication setup. The model list and routing rules at https:\u002F\u002Fai-sdk.dev\u002Fdocs\u002Ffoundations\u002Fproviders-and-models are authoritative — do not guess at model names or assume old slugs still work.\n\nYou are an expert in the Vercel AI Gateway — a unified API for calling AI models with built-in routing, failover, cost tracking, and observability.\n\n## Overview\n\nAI Gateway provides a single API endpoint to access 100+ models from all major providers. It adds \u003C20ms routing latency and handles provider selection, authentication, failover, and load balancing.\n\n## Packages\n\n- `ai@^6.0.0` (required; plain `\"provider\u002Fmodel\"` strings route through the gateway automatically)\n- `@ai-sdk\u002Fgateway@^3.0.0` (optional direct install for explicit gateway package usage)\n\n## Setup\n\nPass a `\"provider\u002Fmodel\"` string to the `model` parameter — the AI SDK automatically routes it through the AI Gateway:\n\n```ts\nimport { generateText } from 'ai'\n\nconst result = await generateText({\n  model: 'openai\u002Fgpt-5.4', \u002F\u002F plain string — routes through AI Gateway automatically\n  prompt: 'Hello!',\n})\n```\n\nNo `gateway()` wrapper or additional package needed. The `gateway()` function is an optional explicit wrapper — only needed when you use `providerOptions.gateway` for routing, failover, or tags:\n\n```ts\nimport { gateway } from 'ai'\n\nconst result = await generateText({\n  model: gateway('openai\u002Fgpt-5.4'),\n  providerOptions: { gateway: { order: ['openai', 'azure-openai'] } },\n})\n```\n\n## Model Slug Rules (Critical)\n\n- Always use `provider\u002Fmodel` format (for example `openai\u002Fgpt-5.4`).\n- Versioned slugs use dots for versions, not hyphens:\n  - Correct: `anthropic\u002Fclaude-sonnet-4.6`\n  - Incorrect: `anthropic\u002Fclaude-sonnet-4-6`\n- Before hardcoding model IDs, call `gateway.getAvailableModels()` and pick from the returned IDs.\n- Default text models: `openai\u002Fgpt-5.4` or `anthropic\u002Fclaude-sonnet-4.6`.\n- Do not default to outdated choices like `openai\u002Fgpt-4o`.\n\n```ts\nimport { gateway } from 'ai'\n\nconst availableModels = await gateway.getAvailableModels()\n\u002F\u002F Choose model IDs from `availableModels` before hardcoding.\n```\n\n## Authentication (OIDC — Default)\n\nAI Gateway uses **OIDC (OpenID Connect)** as the default authentication method. No manual API keys needed.\n\n### Setup\n\n```bash\nvercel link                    # Connect to your Vercel project\n# Enable AI Gateway in Vercel dashboard: https:\u002F\u002Fvercel.com\u002F{team}\u002F{project}\u002Fsettings → AI Gateway\nvercel env pull .env.local     # Provisions VERCEL_OIDC_TOKEN automatically\n```\n\n### How It Works\n\n1. `vercel env pull` writes a `VERCEL_OIDC_TOKEN` to `.env.local` — a short-lived JWT (~24h)\n2. The `@ai-sdk\u002Fgateway` package reads this token via `@vercel\u002Foidc` (`getVercelOidcToken()`)\n3. No `AI_GATEWAY_API_KEY` or provider-specific keys (like `ANTHROPIC_API_KEY`) are needed\n4. On Vercel deployments, OIDC tokens are auto-refreshed — zero maintenance\n\n### Local Development\n\nFor local dev, the OIDC token from `vercel env pull` is valid for ~24 hours. When it expires:\n\n```bash\nvercel env pull .env.local --yes   # Re-pull to get a fresh token\n```\n\n### Alternative: Manual API Key\n\nIf you prefer a static key (e.g., for CI or non-Vercel environments):\n\n```bash\n# Set AI_GATEWAY_API_KEY in your environment\n# The gateway falls back to this when VERCEL_OIDC_TOKEN is not available\nexport AI_GATEWAY_API_KEY=your-key-here\n```\n\n### Auth Priority\n\nThe `@ai-sdk\u002Fgateway` package resolves authentication in this order:\n1. `AI_GATEWAY_API_KEY` environment variable (if set)\n2. `VERCEL_OIDC_TOKEN` via `@vercel\u002Foidc` (default on Vercel and after `vercel env pull`)\n\n## Provider Routing\n\nConfigure how AI Gateway routes requests across providers:\n\n```ts\nconst result = await generateText({\n  model: gateway('anthropic\u002Fclaude-sonnet-4.6'),\n  prompt: 'Hello!',\n  providerOptions: {\n    gateway: {\n      \u002F\u002F Try providers in order; failover to next on error\n      order: ['bedrock', 'anthropic'],\n\n      \u002F\u002F Restrict to specific providers only\n      only: ['anthropic', 'vertex'],\n\n      \u002F\u002F Fallback models if primary model fails\n      models: ['openai\u002Fgpt-5.4', 'google\u002Fgemini-3-flash'],\n\n      \u002F\u002F Track usage per end-user\n      user: 'user-123',\n\n      \u002F\u002F Tag for cost attribution and filtering\n      tags: ['feature:chat', 'env:production', 'team:growth'],\n    },\n  },\n})\n```\n\n### Routing Options\n\n| Option | Purpose |\n|--------|---------|\n| `order` | Provider priority list; try first, failover to next |\n| `only` | Restrict to specific providers |\n| `models` | Fallback model list if primary model unavailable |\n| `user` | End-user ID for usage tracking |\n| `tags` | Labels for cost attribution and reporting |\n\n## Cache-Control Headers\n\nAI Gateway supports response caching to reduce latency and cost for repeated or similar requests:\n\n```ts\nconst result = await generateText({\n  model: gateway('openai\u002Fgpt-5.4'),\n  prompt: 'What is the capital of France?',\n  providerOptions: {\n    gateway: {\n      \u002F\u002F Cache identical requests for 1 hour\n      cacheControl: 'max-age=3600',\n    },\n  },\n})\n```\n\n### Caching strategies\n\n| Header Value | Behavior |\n|-------------|----------|\n| `max-age=3600` | Cache response for 1 hour |\n| `max-age=0` | Bypass cache, always call provider |\n| `s-maxage=86400` | Cache at the edge for 24 hours |\n| `stale-while-revalidate=600` | Serve stale for 10 min while refreshing in background |\n\n### When to use caching\n\n- **Static knowledge queries**: FAQs, translations, factual lookups — cache aggressively\n- **User-specific conversations**: Do not cache — each response depends on conversation history\n- **Embeddings**: Cache embedding results for identical inputs to save cost\n- **Structured extraction**: Cache when extracting structured data from identical documents\n\n### Cache key composition\n\nThe cache key is derived from: model, prompt\u002Fmessages, temperature, and other generation parameters. Changing any parameter produces a new cache key.\n\n## Per-User Rate Limiting\n\nControl usage at the individual user level to prevent abuse and manage costs:\n\n```ts\nconst result = await generateText({\n  model: gateway('openai\u002Fgpt-5.4'),\n  prompt: userMessage,\n  providerOptions: {\n    gateway: {\n      user: userId, \u002F\u002F Required for per-user rate limiting\n      tags: ['feature:chat'],\n    },\n  },\n})\n```\n\n### Rate limit configuration\n\nConfigure rate limits at `https:\u002F\u002Fvercel.com\u002F{team}\u002F{project}\u002Fsettings` → **AI Gateway** → **Rate Limits**:\n\n- **Requests per minute per user**: Throttle individual users (e.g., 20 RPM)\n- **Tokens per day per user**: Cap daily token consumption (e.g., 100K tokens\u002Fday)\n- **Concurrent requests per user**: Limit parallel calls (e.g., 3 concurrent)\n\n### Handling rate limit responses\n\nWhen a user exceeds their limit, the gateway returns HTTP 429:\n\n```ts\nimport { generateText, APICallError } from 'ai'\n\ntry {\n  const result = await generateText({\n    model: gateway('openai\u002Fgpt-5.4'),\n    prompt: userMessage,\n    providerOptions: { gateway: { user: userId } },\n  })\n} catch (error) {\n  if (APICallError.isInstance(error) && error.statusCode === 429) {\n    const retryAfter = error.responseHeaders?.['retry-after']\n    return new Response(\n      JSON.stringify({ error: 'Rate limited', retryAfter }),\n      { status: 429 }\n    )\n  }\n  throw error\n}\n```\n\n## Budget Alerts and Cost Controls\n\n### Tagging for cost attribution\n\nUse tags to track spend by feature, team, and environment:\n\n```ts\nproviderOptions: {\n  gateway: {\n    tags: [\n      'feature:document-qa',\n      'team:product',\n      'env:production',\n      'tier:premium',\n    ],\n    user: userId,\n  },\n}\n```\n\n### Setting up budget alerts\n\nIn the Vercel dashboard at `https:\u002F\u002Fvercel.com\u002F{team}\u002F{project}\u002Fsettings` → **AI Gateway**:\n\n1. Navigate to **AI Gateway → Usage & Budgets**\n2. Set monthly budget thresholds (e.g., $500\u002Fmonth warning, $1000\u002Fmonth hard limit)\n3. Configure alert channels (email, Slack webhook, Vercel integration)\n4. Optionally set per-tag budgets for granular control\n\n### Budget isolation best practice\n\nUse **separate gateway keys per environment** (dev, staging, prod) and per project. This keeps dashboards clean and budgets isolated:\n\n- Restrict AI Gateway keys per project to prevent cross-tenant leakage\n- Use per-project budgets and spend-by-agent reporting to track exactly where tokens go\n- Cap spend during staging with AI Gateway budgets\n\n### Pre-flight cost controls\n\nThe AI Gateway dashboard provides observability (traces, token counts, spend tracking) but no programmatic metrics API. Build your own cost guardrails by estimating token counts and rejecting expensive requests before they execute:\n\n```ts\nimport { generateText } from 'ai'\n\nfunction estimateTokens(text: string): number {\n  return Math.ceil(text.length \u002F 4) \u002F\u002F rough estimate\n}\n\nasync function callWithBudget(prompt: string, maxTokens: number) {\n  const estimated = estimateTokens(prompt)\n  if (estimated > maxTokens) {\n    throw new Error(`Prompt too large: ~${estimated} tokens exceeds ${maxTokens} limit`)\n  }\n  return generateText({ model: 'openai\u002Fgpt-5.4', prompt })\n}\n```\n\nThe AI SDK's `usage` field on responses gives actual token counts after each request — store these for historical tracking and cost analysis.\n\n### Hard spending limits\n\nWhen a hard limit is reached, the gateway returns HTTP 402 (Payment Required). Handle this gracefully:\n\n```ts\nif (APICallError.isInstance(error) && error.statusCode === 402) {\n  \u002F\u002F Budget exceeded — degrade gracefully\n  return fallbackResponse()\n}\n```\n\n### Cost optimization patterns\n\n- Use cheaper models for classification\u002Frouting, expensive models for generation\n- Cache embeddings and static queries (see Cache-Control above)\n- Set per-user daily token caps to prevent runaway usage\n- Monitor cost-per-feature with tags to identify optimization targets\n\n## Audit Logging\n\nAI Gateway logs every request for compliance and debugging:\n\n### What's logged\n\n- Timestamp, model, provider used\n- Input\u002Foutput token counts\n- Latency (routing + provider)\n- User ID and tags\n- HTTP status code\n- Failover chain (which providers were tried)\n\n### Accessing logs\n\n- **Vercel Dashboard** at `https:\u002F\u002Fvercel.com\u002F{team}\u002F{project}\u002Fai` → **Logs** — filter by model, user, tag, status, date range\n- **Vercel API**: Query logs programmatically:\n\n```bash\ncurl -H \"Authorization: Bearer $VERCEL_TOKEN\" \\\n  \"https:\u002F\u002Fapi.vercel.com\u002Fv1\u002Fai-gateway\u002Flogs?projectId=$PROJECT_ID&limit=100\"\n```\n\n- **Log Drains**: Forward AI Gateway logs to Datadog, Splunk, or other providers via Vercel Log Drains (configure at `https:\u002F\u002Fvercel.com\u002Fdashboard\u002F{team}\u002F~\u002Fsettings\u002Flog-drains`) for long-term retention and custom analysis\n\n### Compliance considerations\n\n- AI Gateway does not log prompt or completion content by default\n- Enable content logging in project settings if required for compliance\n- Logs are retained per your Vercel plan's retention policy\n- Use `user` field consistently to support audit trails\n\n## Error Handling Patterns\n\n### Provider unavailable\n\nWhen a provider is down, the gateway automatically fails over if you configured `order` or `models`:\n\n```ts\nconst result = await generateText({\n  model: gateway('anthropic\u002Fclaude-sonnet-4.6'),\n  prompt: 'Summarize this document',\n  providerOptions: {\n    gateway: {\n      order: ['anthropic', 'bedrock'], \u002F\u002F Bedrock as fallback\n      models: ['openai\u002Fgpt-5.4'],   \u002F\u002F Final fallback model\n    },\n  },\n})\n```\n\n### Quota exceeded at provider\n\nIf your provider API key hits its quota, the gateway tries the next provider in the `order` list. Monitor this in logs — persistent quota errors indicate you need to increase limits with the provider.\n\n### Invalid model identifier\n\n```ts\n\u002F\u002F Bad — model doesn't exist\nmodel: 'openai\u002Fgpt-99'  \u002F\u002F Returns 400 with descriptive error\n\n\u002F\u002F Good — use models listed in Vercel docs\nmodel: 'openai\u002Fgpt-5.4'\n```\n\n### Timeout handling\n\nGateway has a default timeout per provider. For long-running generations, use streaming:\n\n```ts\nimport { streamText } from 'ai'\n\nconst result = streamText({\n  model: 'anthropic\u002Fclaude-sonnet-4.6',\n  prompt: longDocument,\n})\n\nfor await (const chunk of result.textStream) {\n  process.stdout.write(chunk)\n}\n```\n\n### Complete error handling template\n\n```ts\nimport { generateText, APICallError } from 'ai'\n\nasync function callAI(prompt: string, userId: string) {\n  try {\n    return await generateText({\n      model: gateway('openai\u002Fgpt-5.4'),\n      prompt,\n      providerOptions: {\n        gateway: {\n          user: userId,\n          order: ['openai', 'azure-openai'],\n          models: ['anthropic\u002Fclaude-haiku-4.5'],\n          tags: ['feature:chat'],\n        },\n      },\n    })\n  } catch (error) {\n    if (!APICallError.isInstance(error)) throw error\n\n    switch (error.statusCode) {\n      case 402: return { text: 'Budget limit reached. Please try again later.' }\n      case 429: return { text: 'Too many requests. Please slow down.' }\n      case 503: return { text: 'AI service temporarily unavailable.' }\n      default: throw error\n    }\n  }\n}\n```\n\n## Gateway vs Direct Provider — Decision Tree\n\nUse this to decide whether to route through AI Gateway or call a provider SDK directly:\n\n```\nNeed failover across providers?\n  └─ Yes → Use Gateway\n  └─ No\n      Need cost tracking \u002F budget alerts?\n        └─ Yes → Use Gateway\n        └─ No\n            Need per-user rate limiting?\n              └─ Yes → Use Gateway\n              └─ No\n                  Need audit logging?\n                    └─ Yes → Use Gateway\n                    └─ No\n                        Using a single provider with provider-specific features?\n                          └─ Yes → Use direct provider SDK\n                          └─ No → Use Gateway (simplifies code)\n```\n\n### When to use direct provider SDK\n\n- You need provider-specific features not exposed through the gateway (e.g., Anthropic's computer use, OpenAI's custom fine-tuned model endpoints)\n- You're self-hosting a model (e.g., vLLM, Ollama) that isn't registered with the gateway\n- You need request-level control over HTTP transport (custom proxies, mTLS)\n\n### When to always use Gateway\n\n- Production applications — failover and observability are essential\n- Multi-tenant SaaS — per-user tracking and rate limiting\n- Teams with cost accountability — tag-based budgeting\n\n## Claude Code Compatibility\n\nAI Gateway exposes an **Anthropic-compatible API endpoint** that lets you route Claude Code requests through the gateway for unified observability, spend tracking, and failover.\n\n### Configuration\n\nSet these environment variables to route Claude Code through AI Gateway:\n\n```bash\nexport ANTHROPIC_BASE_URL=\"https:\u002F\u002Fai-gateway.vercel.sh\"\nexport ANTHROPIC_AUTH_TOKEN=\"your-vercel-ai-gateway-api-key\"\nexport ANTHROPIC_API_KEY=\"\"  # Must be empty string — Claude Code checks this first\n```\n\n**Important**: Setting `ANTHROPIC_API_KEY` to an empty string is required. Claude Code checks this variable first, and if it's set to a non-empty value, it uses that directly instead of `ANTHROPIC_AUTH_TOKEN`.\n\n### Claude Code Max Subscription\n\nAI Gateway supports Claude Code Max subscriptions. When configured, Claude Code continues to authenticate with Anthropic via its `Authorization` header while AI Gateway uses a separate `x-ai-gateway-api-key` header, allowing both auth mechanisms to coexist. This gives you unified observability at no additional token cost.\n\n### Using Non-Anthropic Models\n\nOverride the default Anthropic models by setting:\n\n```bash\nexport ANTHROPIC_DEFAULT_SONNET_MODEL=\"openai\u002Fgpt-5.4\"\nexport ANTHROPIC_DEFAULT_OPUS_MODEL=\"anthropic\u002Fclaude-opus-4.6\"\nexport ANTHROPIC_DEFAULT_HAIKU_MODEL=\"anthropic\u002Fclaude-haiku-4.5\"\n```\n\n## Latest Model Availability\n\n**GPT-5.4** (added March 5, 2026) — agentic and reasoning leaps from GPT-5.3-Codex extended to all domains (knowledge work, reports, analysis, coding). Faster and more token-efficient than GPT-5.2.\n\n| Model | Slug | Input | Output |\n|-------|------|-------|--------|\n| GPT-5.4 | `openai\u002Fgpt-5.4` | $2.50\u002FM tokens | $15.00\u002FM tokens |\n| GPT-5.4 Pro | `openai\u002Fgpt-5.4-pro` | $30.00\u002FM tokens | $180.00\u002FM tokens |\n\nGPT-5.4 Pro targets maximum performance on complex tasks. Use standard GPT-5.4 for most workloads.\n\n## Supported Providers\n\n- OpenAI (GPT-5.x including GPT-5.4 and GPT-5.4 Pro, o-series)\n- Anthropic (Claude 4.x)\n- Google (Gemini)\n- xAI (Grok)\n- Mistral\n- DeepSeek\n- Amazon Bedrock\n- Azure OpenAI\n- Cohere\n- Perplexity\n- Alibaba (Qwen)\n- Meta (Llama)\n- And many more (100+ models total)\n\n## Pricing\n\n- **Zero markup**: Tokens at exact provider list price — no middleman markup, whether using Vercel-managed keys or Bring Your Own Key (BYOK)\n- **Free tier**: Every Vercel team gets **$5 of free AI Gateway credits per month** (refreshes every 30 days, starts on first request). No commitment required — experiment with LLMs indefinitely on the free tier\n- **Pay-as-you-go**: Beyond free credits, purchase AI Gateway Credits at any time with no obligation. Configure **auto top-up** to automatically add credits when your balance falls below a threshold\n- **BYOK**: Use your own provider API keys with zero fees from AI Gateway\n\n## Multimodal Support\n\nText and image generation both route through the gateway. For embeddings, use a direct provider SDK.\n\n```ts\n\u002F\u002F Text — through gateway\nconst { text } = await generateText({\n  model: 'openai\u002Fgpt-5.4',\n  prompt: 'Hello',\n})\n\n\u002F\u002F Image — through gateway (multimodal LLMs return images in result.files)\nconst result = await generateText({\n  model: 'google\u002Fgemini-3.1-flash-image-preview',\n  prompt: 'A sunset over the ocean',\n})\nconst images = result.files.filter((f) => f.mediaType?.startsWith('image\u002F'))\n\n\u002F\u002F Image-only models — through gateway with experimental_generateImage\nimport { experimental_generateImage as generateImage } from 'ai'\nconst { images: generated } = await generateImage({\n  model: 'google\u002Fimagen-4.0-generate-001',\n  prompt: 'A sunset',\n})\n```\n\n**Default image model**: `google\u002Fgemini-3.1-flash-image-preview` — fast multimodal image generation via gateway.\n\nSee [AI Gateway Image Generation docs](https:\u002F\u002Fvercel.com\u002Fdocs\u002Fai-gateway\u002Fcapabilities\u002Fimage-generation) for all supported models and integration methods.\n\n## Key Benefits\n\n1. **Unified API**: One interface for all providers, no provider-specific code\n2. **Automatic failover**: If a provider is down, requests route to the next\n3. **Cost tracking**: Per-user, per-feature attribution with tags\n4. **Observability**: Built-in monitoring of all model calls\n5. **Low latency**: \u003C20ms routing overhead\n6. **No lock-in**: Switch models\u002Fproviders by changing a string\n\n## When to Use AI Gateway\n\n| Scenario | Use Gateway? |\n|----------|-------------|\n| Production app with AI features | Yes — failover, cost tracking |\n| Prototyping with single provider | Optional — direct provider works fine |\n| Multi-provider setup | Yes — unified routing |\n| Need provider-specific features | Use direct provider SDK + Gateway as fallback |\n| Cost tracking and budgeting | Yes — user tracking and tags |\n| Multi-tenant SaaS | Yes — per-user rate limiting and audit |\n| Compliance requirements | Yes — audit logging and log drains |\n\n## Official Documentation\n\n- [AI Gateway](https:\u002F\u002Fvercel.com\u002Fdocs\u002Fai-gateway)\n- [Providers and Models](https:\u002F\u002Fai-sdk.dev\u002Fdocs\u002Ffoundations\u002Fproviders-and-models)\n- [AI SDK Core](https:\u002F\u002Fai-sdk.dev\u002Fdocs\u002Fai-sdk-core)\n- [GitHub: AI SDK](https:\u002F\u002Fgithub.com\u002Fvercel\u002Fai)\n",{"data":35,"body":115},{"name":4,"description":6,"metadata":36,"validate":52,"chainTo":76,"retrieval":98},{"priority":37,"docs":38,"sitemap":41,"pathPatterns":42,"importPatterns":43,"bashPatterns":46},7,[39,40],"https:\u002F\u002Fvercel.com\u002Fdocs\u002Fai-gateway","https:\u002F\u002Fsdk.vercel.ai\u002Fdocs\u002Fai-sdk-core\u002Fsettings","https:\u002F\u002Fvercel.com\u002Fsitemap\u002Fdocs.xml",[],[44,45],"ai","@ai-sdk\u002Fgateway",[47,48,49,50,51],"\\bvercel\\s+env\\s+pull\\b","\\bnpm\\s+(install|i|add)\\s+[^\\n]*@ai-sdk\u002Fgateway\\b","\\bpnpm\\s+(install|i|add)\\s+[^\\n]*@ai-sdk\u002Fgateway\\b","\\bbun\\s+(install|i|add)\\s+[^\\n]*@ai-sdk\u002Fgateway\\b","\\byarn\\s+add\\s+[^\\n]*@ai-sdk\u002Fgateway\\b",[53,57,61,64,67,70,73],{"pattern":54,"message":55,"severity":56},"\\b(claude|gpt|gemini|llama|mistral|qwen|deepseek)[a-z0-9-]*-\\d+-\\d+[a-z0-9-]*\\b","Model slug uses hyphens — use dots not hyphens for version numbers (e.g., claude-sonnet-4.6)","error",{"pattern":58,"message":59,"severity":60},"AI_GATEWAY_API_KEY","Consider OIDC-based auth via vercel env pull for automatic token management — AI_GATEWAY_API_KEY works but requires manual rotation","recommended",{"pattern":62,"message":63,"severity":56},"gateway\\(['\"][^'\"\u002F]+['\"]\\)","Model string missing provider\u002F prefix — use provider\u002Fmodel format (e.g., openai\u002Fgpt-5.4, anthropic\u002Fclaude-sonnet-4.6)",{"pattern":65,"message":66,"severity":60},"gpt-4o","Your knowledge is outdated — gpt-4o has been superseded by gpt-5.4 which is significantly better, cheaper, and faster. 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Use when the user wants to enable, adopt, or migrate to Cache Components, flip the `cacheComponents` flag, work through a flood of blocking-prerender \u002F instant validation errors, run the `cache-components-instant-false` codemod, or decide between opting routes out with `export const instant = false` and fixing them in place.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[6917,6920,6923,6926,6929],{"name":6918,"slug":6919,"type":13},"Caching","caching",{"name":6921,"slug":6922,"type":13},"Frontend","frontend",{"name":6924,"slug":6925,"type":13},"Migration","migration",{"name":6927,"slug":6928,"type":13},"Next.js","next-js",{"name":9,"slug":8,"type":13},141208,"https:\u002F\u002Fgithub.com\u002Fvercel\u002Fnext.js","2026-07-24T05:38:30.118542",{"slug":6934,"name":6934,"fn":6935,"description":6936,"org":6937,"tags":6938,"stars":6930,"repoUrl":6931,"updatedAt":6946},"next-cache-components-optimizer","optimize Next.js cache components","Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components \u002F PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next\u002Fplaywright instant() e2e and work it to green, one verified route at a time; the shipped test then guards against regression. Use when asked to make a route's navigation instant (its static shell commits immediately), fix a route whose static shell isn't prerendered\u002Fserved\u002Fprefetched, grow a route's static shell or fix its slow first paint, diagnose which Suspense boundary keeps a route out of its static shell, or write the instant() e2e guard for one. Requires Next.js 16.3+ with cacheComponents; directs an upgrade if older.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[6939,6940,6941,6942,6945],{"name":6918,"slug":6919,"type":13},{"name":6921,"slug":6922,"type":13},{"name":6927,"slug":6928,"type":13},{"name":6943,"slug":6944,"type":13},"Performance","performance",{"name":9,"slug":8,"type":13},"2026-07-30T05:31:10.674078",{"slug":6948,"name":6948,"fn":6949,"description":6950,"org":6951,"tags":6952,"stars":6930,"repoUrl":6931,"updatedAt":6963},"next-dev-loop","verify Next.js runtime behavior","Verify Next.js runtime behavior after editing app code. Use this skill to confirm a change actually works in a running app — not just that it compiles or type-checks. Combines \u002F_next\u002Fmcp (Next.js's view) with agent-browser (the browser's view). Requires a running `next dev`.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[6953,6956,6957,6958,6959,6960],{"name":6954,"slug":6955,"type":13},"Debugging","debugging",{"name":6921,"slug":6922,"type":13},{"name":1070,"slug":1067,"type":13},{"name":6927,"slug":6928,"type":13},{"name":9,"slug":8,"type":13},{"name":6961,"slug":6962,"type":13},"Web Development","web-development","2026-05-22T06:45:28.627735",{"slug":6965,"name":6965,"fn":6966,"description":6967,"org":6968,"tags":6969,"stars":6930,"repoUrl":6931,"updatedAt":6974},"next-partial-prefetching-adoption","adopt Partial Prefetching in Next.js apps","Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the `partialPrefetching` flag, opt routes in with `export const prefetch = 'partial'`, audit `\u003CLink prefetch={true}>` calls, or resolve the link-prefetch-partial and instant-shell-url-data insights.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[6970,6971,6972,6973],{"name":6921,"slug":6922,"type":13},{"name":6927,"slug":6928,"type":13},{"name":6943,"slug":6944,"type":13},{"name":9,"slug":8,"type":13},"2026-07-30T05:31:11.591864",{"slug":6976,"name":6976,"fn":6977,"description":6978,"org":6979,"tags":6980,"stars":6987,"repoUrl":6988,"updatedAt":6989},"turborepo","manage monorepos with Turborepo","Turborepo monorepo build system guidance. Triggers on: turbo.json, task pipelines,\ndependsOn, caching, remote cache, the \"turbo\" CLI, --filter, --affected, CI optimization, environment\nvariables, internal packages, monorepo structure\u002Fbest practices, and boundaries.\n\nUse when user: configures tasks\u002Fworkflows\u002Fpipelines, creates packages, sets up\nmonorepo, shares code between apps, runs changed\u002Faffected packages, debugs cache,\nor has apps\u002Fpackages directories.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[6981,6984,6985],{"name":6982,"slug":6983,"type":13},"CI\u002FCD","ci-cd",{"name":6943,"slug":6944,"type":13},{"name":6986,"slug":6976,"type":13},"Turborepo",30809,"https:\u002F\u002Fgithub.com\u002Fvercel\u002Fturborepo","2026-07-30T05:32:14.920116",{"slug":6991,"name":6991,"fn":6992,"description":6993,"org":6994,"tags":6995,"stars":7002,"repoUrl":6898,"updatedAt":7003},"add-function-examples","add AI function examples for testing","Guide for adding new AI function examples, for testing specific features against the actual provider APIs.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[6996,6998,7001],{"name":6997,"slug":79,"type":13},"AI SDK",{"name":6999,"slug":7000,"type":13},"Testing","testing",{"name":9,"slug":8,"type":13},25670,"2026-04-06T18:55:51.318866",{"slug":7005,"name":7005,"fn":7006,"description":7007,"org":7008,"tags":7009,"stars":7002,"repoUrl":6898,"updatedAt":7021},"add-harness-package","add AI SDK harness packages","Guide for adding new AI SDK harness packages. Use when creating a new @ai-sdk\u002Fharness-\u003Cname> package that adapts a coding-agent runtime to HarnessV1.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[7010,7013,7014,7017,7020],{"name":7011,"slug":7012,"type":13},"Agents","agents",{"name":6997,"slug":79,"type":13},{"name":7015,"slug":7016,"type":13},"Harness","harness",{"name":7018,"slug":7019,"type":13},"SDK","sdk",{"name":9,"slug":8,"type":13},"2026-06-18T08:29:19.858737",{"slug":7023,"name":7023,"fn":7024,"description":7025,"org":7026,"tags":7027,"stars":7002,"repoUrl":6898,"updatedAt":7033},"add-provider-package","add new provider packages to AI SDK","Guide for adding new AI provider packages to the AI SDK. Use when creating a new @ai-sdk\u002F\u003Cprovider> package to integrate an AI service into the SDK.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[7028,7029,7032],{"name":6997,"slug":79,"type":13},{"name":7030,"slug":7031,"type":13},"API Development","api-development",{"name":9,"slug":8,"type":13},"2026-04-06T18:55:47.45549",{"slug":7035,"name":7035,"fn":7036,"description":7037,"org":7038,"tags":7039,"stars":7002,"repoUrl":6898,"updatedAt":7052},"adr-skill","create and maintain architecture decision records","Create and maintain Architecture Decision Records (ADRs) optimized for agentic coding workflows. Use when you need to propose, write, update, accept\u002Freject, deprecate, or supersede an ADR; bootstrap an adr folder and index; consult existing ADRs before implementing changes; or enforce ADR conventions. This skill uses Socratic questioning to capture intent before drafting, and validates output against an agent-readiness checklist.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[7040,7043,7046,7049],{"name":7041,"slug":7042,"type":13},"ADR","adr",{"name":7044,"slug":7045,"type":13},"Architecture","architecture",{"name":7047,"slug":7048,"type":13},"Documentation","documentation",{"name":7050,"slug":7051,"type":13},"Engineering","engineering","2026-04-06T18:55:50.043694",{"slug":79,"name":79,"fn":7054,"description":7055,"org":7056,"tags":7057,"stars":7002,"repoUrl":6898,"updatedAt":7062},"build AI features with Vercel AI SDK","Answer questions about the AI SDK and help build AI-powered features. Use when developers: (1) Ask about AI SDK functions like generateText, streamText, ToolLoopAgent, embed, or tools, (2) Want to build AI agents, chatbots, RAG systems, or text generation features, (3) Have questions about AI providers (OpenAI, Anthropic, Google, etc.), streaming, tool calling, structured output, or embeddings, (4) Use React hooks like useChat or useCompletion. Triggers on: \"AI SDK\", \"Vercel AI SDK\", \"generateText\", \"streamText\", \"add AI to my app\", \"build an agent\", \"tool calling\", \"structured output\", \"useChat\".",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[7058,7059,7060,7061],{"name":7011,"slug":7012,"type":13},{"name":6997,"slug":79,"type":13},{"name":15,"slug":16,"type":13},{"name":9,"slug":8,"type":13},"2026-04-06T18:55:48.739463",{"slug":7064,"name":7064,"fn":7065,"description":7066,"org":7067,"tags":7068,"stars":7002,"repoUrl":6898,"updatedAt":7072},"capture-api-response-test-fixture","capture API response test fixtures","Capture API response test fixture.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[7069,7070,7071],{"name":7030,"slug":7031,"type":13},{"name":6999,"slug":7000,"type":13},{"name":9,"slug":8,"type":13},"2026-04-06T18:55:56.374433",{"slug":7074,"name":7074,"fn":7075,"description":7076,"org":7077,"tags":7078,"stars":7002,"repoUrl":6898,"updatedAt":7082},"develop-ai-functions-example","develop AI SDK function examples","Develop examples for AI SDK functions. Use when creating, running, or modifying examples under examples\u002Fai-functions\u002Fsrc to validate provider support, demonstrate features, or create test fixtures.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[7079,7080,7081],{"name":6997,"slug":79,"type":13},{"name":6999,"slug":7000,"type":13},{"name":9,"slug":8,"type":13},"2026-04-06T18:55:55.088956",68,{"items":7085,"total":7173},[7086,7093,7111,7124,7136,7149,7162],{"slug":4,"name":4,"fn":5,"description":6,"org":7087,"tags":7088,"stars":23,"repoUrl":24,"updatedAt":25},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[7089,7090,7091,7092],{"name":21,"slug":22,"type":13},{"name":18,"slug":19,"type":13},{"name":15,"slug":16,"type":13},{"name":9,"slug":8,"type":13},{"slug":7094,"name":7094,"fn":7095,"description":7096,"org":7097,"tags":7098,"stars":23,"repoUrl":24,"updatedAt":7110},"auth","integrate authentication in Next.js apps","Authentication integration guidance — Clerk (native Vercel Marketplace), Descope, and Auth0 setup for Next.js applications. Covers middleware auth patterns, sign-in\u002Fsign-up flows, and Marketplace provisioning. Use when implementing user authentication.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[7099,7102,7105,7106,7109],{"name":7100,"slug":7101,"type":13},"Auth0","auth0",{"name":7103,"slug":7104,"type":13},"Authentication","authentication",{"name":6927,"slug":6928,"type":13},{"name":7107,"slug":7108,"type":13},"Security","security",{"name":9,"slug":8,"type":13},"2026-04-06T18:56:17.050565",{"slug":7112,"name":7112,"fn":7113,"description":7114,"org":7115,"tags":7116,"stars":23,"repoUrl":24,"updatedAt":7123},"bootstrap","bootstrap Vercel-linked repositories","Project bootstrapping orchestrator for repos that depend on Vercel-linked resources (databases, auth, and managed integrations). Use when setting up or repairing a repository so linking, environment provisioning, env pulls, and first-run db\u002Fdev commands happen in the correct safe order.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[7117,7118,7121,7122],{"name":5618,"slug":5615,"type":13},{"name":7119,"slug":7120,"type":13},"Deployment","deployment",{"name":1070,"slug":1067,"type":13},{"name":9,"slug":8,"type":13},"2026-04-06T18:56:18.297868",{"slug":7125,"name":7125,"fn":7126,"description":7127,"org":7128,"tags":7129,"stars":23,"repoUrl":24,"updatedAt":7135},"cdn-caching","debug Vercel CDN caching behavior","Debug Vercel CDN caching — cache hit rate, stale content, revalidation behavior, ISR + PPR, per-request cache reasons (cacheReason) and PPR state (ppr_state), and costs.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[7130,7131,7133,7134],{"name":6918,"slug":6919,"type":13},{"name":6715,"slug":7132,"type":13},"observability",{"name":6943,"slug":6944,"type":13},{"name":9,"slug":8,"type":13},"2026-07-30T05:31:34.628944",{"slug":7137,"name":7137,"fn":7138,"description":7139,"org":7140,"tags":7141,"stars":23,"repoUrl":24,"updatedAt":7148},"chat-sdk","build multi-platform chatbots with Vercel","Vercel Chat SDK expert guidance. Use when building multi-platform chat bots — Slack, Telegram, Microsoft Teams, Discord, Google Chat, GitHub, Linear — with a single codebase. Covers the Chat class, adapters, threads, messages, cards, modals, streaming, state management, and webhook setup.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[7142,7143,7146,7147],{"name":7011,"slug":7012,"type":13},{"name":7144,"slug":7145,"type":13},"Messaging","messaging",{"name":7018,"slug":7019,"type":13},{"name":9,"slug":8,"type":13},"2026-04-06T18:56:26.921901",{"slug":7150,"name":7150,"fn":7151,"description":7152,"org":7153,"tags":7154,"stars":23,"repoUrl":24,"updatedAt":7161},"deployments-cicd","manage Vercel deployments and CI\u002FCD","Vercel deployment and CI\u002FCD expert guidance. Use when deploying, promoting, rolling back, inspecting deployments, building with --prebuilt, or configuring CI workflow files for Vercel.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[7155,7156,7157,7160],{"name":6982,"slug":6983,"type":13},{"name":7119,"slug":7120,"type":13},{"name":7158,"slug":7159,"type":13},"Operations","operations",{"name":9,"slug":8,"type":13},"2026-04-06T18:56:22.054263",{"slug":7163,"name":7163,"fn":7164,"description":7165,"org":7166,"tags":7167,"stars":23,"repoUrl":24,"updatedAt":7172},"env-vars","manage Vercel environment variables","Vercel environment variable expert guidance. Use when working with .env files, vercel env commands, OIDC tokens, or managing environment-specific configuration.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[7168,7169,7170,7171],{"name":5618,"slug":5615,"type":13},{"name":7158,"slug":7159,"type":13},{"name":7107,"slug":7108,"type":13},{"name":9,"slug":8,"type":13},"2026-04-06T18:56:28.150777",29]