cloudflare-workers-ai

name	cloudflare-workers-ai
description	Run LLMs and AI models on Cloudflare's GPU network with Workers AI. Includes Llama 4, Gemma 3, Mistral 3.1, Flux images, BGE embeddings, streaming, and AI Gateway. Handles 2025 breaking changes. Prevents 7 documented errors. Use when: implementing LLM inference, images, RAG, or troubleshooting AI_ERROR, rate limits, max_tokens, BGE pooling, context window, neuron billing, Miniflare AI binding, NSFW filter, num_steps.
user-invocable	true

Cloudflare Workers AI

Status: Production Ready ✅ Last Updated: 2026-01-21 Dependencies: cloudflare-worker-base (for Worker setup) Latest Versions: wrangler@4.58.0, @cloudflare/workers-types@4.20260109.0, workers-ai-provider@3.0.2

Recent Updates (2025):

• April 2025 - Performance: Llama 3.3 70B 2-4x faster (speculative decoding, prefix caching), BGE embeddings 2x faster
• April 2025 - Breaking Changes: max_tokens now correctly defaults to 256 (was not respected), BGE pooling parameter (cls NOT backwards compatible with mean)
• 2025 - New Models (14): Mistral 3.1 24B (vision+tools), Gemma 3 12B (128K context), EmbeddingGemma 300M, Llama 4 Scout, GPT-OSS 120B/20B, Qwen models (QwQ 32B, Coder 32B), Leonardo image gen, Deepgram Aura 2, Whisper v3 Turbo, IBM Granite, Nova 3
• 2025 - Platform: Context windows API change (tokens not chars), unit-based pricing with per-model granularity, workers-ai-provider v3.0.2 (AI SDK v5), LoRA rank up to 32 (was 8), 100 adapters per account
• October 2025: Model deprecations (use Llama 4, GPT-OSS instead)

---

Quick Start (5 Minutes)

// 1. Add AI binding to wrangler.jsonc
{ "ai": { "binding": "AI" } }

// 2. Run model with streaming (recommended)
export default {
  async fetch(request: Request, env: Env): Promise<Response> {
    const stream = await env.AI.run('@cf/meta/llama-3.1-8b-instruct', {
      messages: [{ role: 'user', content: 'Tell me a story' }],
      stream: true, // Always stream for text generation!
    });

    return new Response(stream, {
      headers: { 'content-type': 'text/event-stream' },
    });
  },
};

Why streaming? Prevents buffering in memory, faster time-to-first-token, avoids Worker timeout issues.

---

Known Issues Prevention

This skill prevents 7 documented issues:

Issue #1: Context Window Validation Changed to Tokens (February 2025)

Error: "Exceeded character limit" despite model supporting larger context Source: Cloudflare Changelog Why It Happens: Before February 2025, Workers AI validated prompts using a hard 6144 character limit, even for models with larger token-based context windows (e.g., Mistral with 32K tokens). After the update, validation switched to token-based counting. Prevention: Calculate tokens (not characters) when checking context window limits.

import { encode } from 'gpt-tokenizer'; // or model-specific tokenizer

const tokens = encode(prompt);
const contextWindow = 32768; // Model's max tokens (check docs)
const maxResponseTokens = 2048;

if (tokens.length + maxResponseTokens > contextWindow) {
  throw new Error(`Prompt exceeds context window: ${tokens.length} tokens`);
}

const response = await env.AI.run('@cf/mistral/mistral-7b-instruct-v0.2', {
  messages: [{ role: 'user', content: prompt }],
  max_tokens: maxResponseTokens,
});

Issue #2: Neuron Consumption Discrepancies in Dashboard

Error: Dashboard neuron usage significantly exceeds expected token-based calculations Source: Cloudflare Community Discussion Why It Happens: Users report dashboard showing hundred-million-level neuron consumption for K-level token usage, particularly with AutoRAG features and certain models. The discrepancy between expected neuron consumption (based on pricing docs) and actual dashboard metrics is not fully documented. Prevention: Monitor neuron usage via AI Gateway logs and correlate with requests. File support ticket if consumption significantly exceeds expectations.

// Use AI Gateway for detailed request logging
const response = await env.AI.run(
  '@cf/meta/llama-3.1-8b-instruct',
  { messages: [{ role: 'user', content: query }] },
  { gateway: { id: 'my-gateway' } }
);

// Monitor dashboard at: https://dash.cloudflare.com → AI → Workers AI
// Compare neuron usage with token counts
// File support ticket with details if discrepancy persists

Issue #3: AI Binding Requires Remote or Latest Tooling in Local Dev

Error: "MiniflareCoreError: wrapped binding module can't be resolved (internal modules only)" Source: GitHub Issue #6796 Why It Happens: When using Workers AI bindings with Miniflare in local development (particularly with custom Vite plugins), the AI binding requires external workers that aren't properly exposed by older unstable_getMiniflareWorkerOptions. The error occurs when Miniflare can't resolve the internal AI worker module. Prevention: Use remote bindings for AI in local dev, or update to latest @cloudflare/vite-plugin.

// wrangler.jsonc - Option 1: Use remote AI binding in local dev
{
  "ai": { "binding": "AI" },
  "dev": {
    "remote": true // Use production AI binding locally
  }
}

# Option 2: Update to latest tooling
npm install -D @cloudflare/vite-plugin@latest

# Option 3: Use wrangler dev instead of custom Miniflare
npm run dev

Issue #4: Flux Image Generation NSFW Filter False Positives

Error: "AiError: Input prompt contains NSFW content (code 3030)" for innocent prompts Source: Cloudflare Community Discussion Why It Happens: Flux image generation models (@cf/black-forest-labs/flux-1-schnell) sometimes trigger false positive NSFW content errors even with innocent single-word prompts like "hamburger". The NSFW filter can be overly sensitive without context. Prevention: Add descriptive context around potential trigger words instead of using single-word prompts.

// ❌ May trigger error 3030
const response = await env.AI.run('@cf/black-forest-labs/flux-1-schnell', {
  prompt: 'hamburger', // Single word triggers filter
});

// ✅ Add context to avoid false positives
const response = await env.AI.run('@cf/black-forest-labs/flux-1-schnell', {
  prompt: 'A photo of a delicious large hamburger on a plate with lettuce and tomato',
  num_steps: 4,
});

Issue #5: Image Generation Error 1000 - Missing num_steps Parameter

Error: "Error: unexpected type 'int32' with value 'undefined' (code 1000)" Source: Cloudflare Community Discussion Why It Happens: Image generation API calls return error code 1000 when the num_steps parameter is not provided, even though documentation suggests it's optional. The parameter is actually required for most Flux models. Prevention: Always include num_steps: 4 for image generation models (typically 4 for Flux Schnell).

// ✅ Always include num_steps for image generation
const image = await env.AI.run('@cf/black-forest-labs/flux-1-schnell', {
  prompt: 'A beautiful sunset over mountains',
  num_steps: 4, // Required - typically 4 for Flux Schnell
});

// Note: FLUX.2 [klein] 4B has fixed steps=4 (cannot be adjusted)

Issue #6: Zod v4 Incompatibility with Structured Output Tools

Error: Syntax errors and failed transpilation when using Stagehand with Zod v4 Source: GitHub Issue #10798 Why It Happens: Stagehand (browser automation) and some structured output examples in Workers AI fail with Zod v4 (now default). The underlying zod-to-json-schema library doesn't yet support Zod v4, causing transpilation failures. Prevention: Pin Zod to v3 until zod-to-json-schema supports v4.

# Install Zod v3 specifically
npm install zod@3

# Or pin in package.json
{
  "dependencies": {
    "zod": "~3.23.8" // Pin to v3 for compatibility
  }
}

Issue #7: AI Gateway Cache Headers for Per-Request Control

Not an error, but important feature: AI Gateway supports per-request cache control via HTTP headers for custom TTL, cache bypass, and custom cache keys beyond dashboard defaults. Source: AI Gateway Caching Documentation Use When: You need different caching behavior for different requests (e.g., 1 hour for expensive queries, skip cache for real-time data). Implementation: See AI Gateway Integration section below for header usage.

---

API Reference

env.AI.run(
  model: string,
  inputs: ModelInputs,
  options?: { gateway?: { id: string; skipCache?: boolean } }
): Promise<ModelOutput | ReadableStream>

---

Model Selection Guide (Updated 2025)

Text Generation (LLMs)

Model	Best For	Rate Limit	Size	Notes
2025 Models
@cf/meta/llama-4-scout-17b-16e-instruct	Latest Llama, general purpose	300/min	17B	NEW 2025
@cf/openai/gpt-oss-120b	Largest open-source GPT	300/min	120B	NEW 2025
@cf/openai/gpt-oss-20b	Smaller open-source GPT	300/min	20B	NEW 2025
@cf/google/gemma-3-12b-it	128K context, 140+ languages	300/min	12B	NEW 2025, vision
@cf/mistralai/mistral-small-3.1-24b-instruct	Vision + tool calling	300/min	24B	NEW 2025
@cf/qwen/qwq-32b	Reasoning, complex tasks	300/min	32B	NEW 2025
@cf/qwen/qwen2.5-coder-32b-instruct	Coding specialist	300/min	32B	NEW 2025
@cf/qwen/qwen3-30b-a3b-fp8	Fast quantized	300/min	30B	NEW 2025
@cf/ibm-granite/granite-4.0-h-micro	Small, efficient	300/min	Micro	NEW 2025
Performance (2025)
@cf/meta/llama-3.3-70b-instruct-fp8-fast	2-4x faster (2025 update)	300/min	70B	Speculative decoding
@cf/meta/llama-3.1-8b-instruct-fp8-fast	Fast 8B variant	300/min	8B	-
Standard Models
@cf/meta/llama-3.1-8b-instruct	General purpose	300/min	8B	-
@cf/meta/llama-3.2-1b-instruct	Ultra-fast, simple tasks	300/min	1B	-
@cf/deepseek-ai/deepseek-r1-distill-qwen-32b	Coding, technical	300/min	32B	-

Text Embeddings (2x Faster - 2025)

Model	Dimensions	Best For	Rate Limit	Notes
@cf/google/embeddinggemma-300m	768	Best-in-class RAG	3000/min	NEW 2025
@cf/baai/bge-base-en-v1.5	768	General RAG (2x faster)	3000/min	pooling: "cls" recommended
@cf/baai/bge-large-en-v1.5	1024	High accuracy (2x faster)	1500/min	pooling: "cls" recommended
@cf/baai/bge-small-en-v1.5	384	Fast, low storage (2x faster)	3000/min	pooling: "cls" recommended
@cf/qwen/qwen3-embedding-0.6b	768	Qwen embeddings	3000/min	NEW 2025

CRITICAL (2025): BGE models now support pooling: "cls" parameter (recommended) but NOT backwards compatible with pooling: "mean" (default).

Image Generation

Model	Best For	Rate Limit	Notes
@cf/black-forest-labs/flux-1-schnell	High quality, photorealistic	720/min	⚠️ See warnings below
@cf/leonardo/lucid-origin	Leonardo AI style	720/min	NEW 2025, requires num_steps
@cf/leonardo/phoenix-1.0	Leonardo AI variant	720/min	NEW 2025, requires num_steps
@cf/stabilityai/stable-diffusion-xl-base-1.0	General purpose	720/min	Requires num_steps

⚠️ Common Image Generation Issues:

• Error 1000: Always include num_steps: 4 parameter (required despite docs suggesting optional)
• Error 3030 (NSFW filter): Single words like "hamburger" may trigger false positives - add descriptive context to prompts

// ✅ Correct pattern for image generation
const image = await env.AI.run('@cf/black-forest-labs/flux-1-schnell', {
  prompt: 'A photo of a delicious hamburger on a plate with fresh vegetables',
  num_steps: 4, // Required to avoid error 1000
});
// Descriptive context helps avoid NSFW false positives (error 3030)

Vision Models

Model	Best For	Rate Limit	Notes
@cf/meta/llama-3.2-11b-vision-instruct	Image understanding	720/min	-
@cf/google/gemma-3-12b-it	Vision + text (128K context)	300/min	NEW 2025

Audio Models (2025)

Model	Type	Rate Limit	Notes
@cf/deepgram/aura-2-en	Text-to-speech (English)	720/min	NEW 2025
@cf/deepgram/aura-2-es	Text-to-speech (Spanish)	720/min	NEW 2025
@cf/deepgram/nova-3	Speech-to-text (+ WebSocket)	720/min	NEW 2025
@cf/openai/whisper-large-v3-turbo	Speech-to-text (faster)	720/min	NEW 2025

---

Common Patterns

RAG (Retrieval Augmented Generation)

// 1. Generate embeddings
const embeddings = await env.AI.run('@cf/baai/bge-base-en-v1.5', { text: [userQuery] });

// 2. Search Vectorize
const matches = await env.VECTORIZE.query(embeddings.data[0], { topK: 3 });
const context = matches.matches.map((m) => m.metadata.text).join('\n\n');

// 3. Generate with context
const response = await env.AI.run('@cf/meta/llama-3.1-8b-instruct', {
  messages: [
    { role: 'system', content: `Answer using this context:\n${context}` },
    { role: 'user', content: userQuery },
  ],
  stream: true,
});

---

Structured Output with Zod

import { z } from 'zod';

const Schema = z.object({ name: z.string(), items: z.array(z.string()) });

const response = await env.AI.run('@cf/meta/llama-3.1-8b-instruct', {
  messages: [{
    role: 'user',
    content: `Generate JSON matching: ${JSON.stringify(Schema.shape)}`
  }],
});

const validated = Schema.parse(JSON.parse(response.response));

---

AI Gateway Integration

Provides caching, logging, cost tracking, and analytics for AI requests.

Basic Gateway Usage

const response = await env.AI.run(
  '@cf/meta/llama-3.1-8b-instruct',
  { prompt: 'Hello' },
  { gateway: { id: 'my-gateway', skipCache: false } }
);

// Access logs and send feedback
const gateway = env.AI.gateway('my-gateway');
await gateway.patchLog(env.AI.aiGatewayLogId, {
  feedback: { rating: 1, comment: 'Great response' },
});

Per-Request Cache Control (Advanced)

Override default cache behavior with HTTP headers for fine-grained control:

// Custom cache TTL (1 hour for expensive queries)
const response = await fetch(
  `https://gateway.ai.cloudflare.com/v1/${accountId}/${gatewayId}/workers-ai/@cf/meta/llama-3.1-8b-instruct`,
  {
    method: 'POST',
    headers: {
      'Authorization': `Bearer ${env.CLOUDFLARE_API_KEY}`,
      'Content-Type': 'application/json',
      'cf-aig-cache-ttl': '3600', // 1 hour in seconds (min: 60, max: 2592000)
    },
    body: JSON.stringify({
      messages: [{ role: 'user', content: prompt }],
    }),
  }
);

// Skip cache for real-time data
const response = await fetch(gatewayUrl, {
  headers: {
    'cf-aig-skip-cache': 'true', // Bypass cache entirely
  },
  // ...
});

// Check if response was cached
const cacheStatus = response.headers.get('cf-aig-cache-status'); // "HIT" or "MISS"

Available Cache Headers:

• cf-aig-cache-ttl: Set custom TTL in seconds (60s to 1 month)
• cf-aig-skip-cache: Bypass cache entirely ('true')
• cf-aig-cache-key: Custom cache key for granular control
• cf-aig-cache-status: Response header showing "HIT" or "MISS"

Benefits: Cost tracking, caching (reduces duplicate inference), logging, rate limiting, analytics, per-request cache customization.

---

Rate Limits & Pricing (Updated 2025)

Rate Limits (per minute)

Task Type	Default Limit	Notes
Text Generation	300/min	Some fast models: 400-1500/min
Text Embeddings	3000/min	BGE-large: 1500/min
Image Generation	720/min	All image models
Vision Models	720/min	Image understanding
Audio (TTS/STT)	720/min	Deepgram, Whisper
Translation	720/min	M2M100, Opus MT
Classification	2000/min	Text classification

Pricing (Unit-Based, Billed in Neurons - 2025)

Free Tier:

• 10,000 neurons per day
• Resets daily at 00:00 UTC

Paid Tier ($0.011 per 1,000 neurons):

• 10,000 neurons/day included
• Unlimited usage above free allocation

2025 Model Costs (per 1M tokens):

Model	Input	Output	Notes
2025 Models
Llama 4 Scout 17B	$0.270	$0.850	NEW 2025
GPT-OSS 120B	$0.350	$0.750	NEW 2025
GPT-OSS 20B	$0.200	$0.300	NEW 2025
Gemma 3 12B	$0.345	$0.556	NEW 2025
Mistral 3.1 24B	$0.351	$0.555	NEW 2025
Qwen QwQ 32B	$0.660	$1.000	NEW 2025
Qwen Coder 32B	$0.660	$1.000	NEW 2025
IBM Granite Micro	$0.017	$0.112	NEW 2025
EmbeddingGemma 300M	$0.012	N/A	NEW 2025
Qwen3 Embedding 0.6B	$0.012	N/A	NEW 2025
Performance (2025)
Llama 3.3 70B Fast	$0.293	$2.253	2-4x faster
Llama 3.1 8B FP8 Fast	$0.045	$0.384	Fast variant
Standard Models
Llama 3.2 1B	$0.027	$0.201	-
Llama 3.1 8B	$0.282	$0.827	-
Deepseek R1 32B	$0.497	$4.881	-
BGE-base (2x faster)	$0.067	N/A	2025 speedup
BGE-large (2x faster)	$0.204	N/A	2025 speedup
Image Models (2025)
Flux 1 Schnell	$0.0000528 per 512x512 tile	-
Leonardo Lucid	$0.006996 per 512x512 tile	NEW 2025
Leonardo Phoenix	$0.005830 per 512x512 tile	NEW 2025
Audio Models (2025)
Deepgram Aura 2	$0.030 per 1k chars	NEW 2025
Deepgram Nova 3	$0.0052 per audio min	NEW 2025
Whisper v3 Turbo	$0.0005 per audio min	NEW 2025

---

Error Handling with Retry

async function runAIWithRetry(
  env: Env,
  model: string,
  inputs: any,
  maxRetries = 3
): Promise<any> {
  let lastError: Error;

  for (let i = 0; i < maxRetries; i++) {
    try {
      return await env.AI.run(model, inputs);
    } catch (error) {
      lastError = error as Error;

      // Rate limit - retry with exponential backoff
      if (lastError.message.toLowerCase().includes('rate limit')) {
        await new Promise((resolve) => setTimeout(resolve, Math.pow(2, i) * 1000));
        continue;
      }

      throw error; // Other errors - fail immediately
    }
  }

  throw lastError!;
}

---

OpenAI Compatibility

import OpenAI from 'openai';

const openai = new OpenAI({
  apiKey: env.CLOUDFLARE_API_KEY,
  baseURL: `https://api.cloudflare.com/client/v4/accounts/${env.ACCOUNT_ID}/ai/v1`,
});

// Chat completions
await openai.chat.completions.create({
  model: '@cf/meta/llama-3.1-8b-instruct',
  messages: [{ role: 'user', content: 'Hello!' }],
});

Endpoints: /v1/chat/completions, /v1/embeddings

---

Vercel AI SDK Integration (workers-ai-provider v3.0.2)

import { createWorkersAI } from 'workers-ai-provider'; // v3.0.2 with AI SDK v5
import { generateText, streamText } from 'ai';

const workersai = createWorkersAI({ binding: env.AI });

// Generate or stream
await generateText({
  model: workersai('@cf/meta/llama-3.1-8b-instruct'),
  prompt: 'Write a poem',
});

---

Community Tips

Note: These tips come from community discussions and production experience.

Hono Framework Streaming Pattern

When using Workers AI streaming with Hono, return the stream directly as a Response (not through Hono's streaming utilities):

import { Hono } from 'hono';

type Bindings = { AI: Ai };
const app = new Hono<{ Bindings: Bindings }>();

app.post('/chat', async (c) => {
  const { prompt } = await c.req.json();

  const stream = await c.env.AI.run('@cf/meta/llama-3.1-8b-instruct', {
    messages: [{ role: 'user', content: prompt }],
    stream: true,
  });

  // Return stream directly (not c.stream())
  return new Response(stream, {
    headers: {
      'content-type': 'text/event-stream',
      'cache-control': 'no-cache',
      'connection': 'keep-alive',
    },
  });
});

Source: Hono Discussion #2409

Troubleshooting Unexplained AI Binding Failures

If experiencing unexplained Workers AI failures:

# 1. Check wrangler version
npx wrangler --version

# 2. Clear wrangler cache
rm -rf ~/.wrangler

# 3. Update to latest stable
npm install -D wrangler@latest

# 4. Check local network/firewall settings
# Some corporate firewalls block Workers AI endpoints

Note: Most "version incompatibility" issues turn out to be network configuration problems.

---

References

• Workers AI Docs
• Models Catalog
• AI Gateway
• Pricing
• Changelog
• LoRA Adapters
• MCP Tool: Use mcp__cloudflare-docs__search_cloudflare_documentation for latest docs