Cloudflare AI Models

Cloudflare Workers AI provides access to a comprehensive collection of AI models running on Cloudflare's global edge network. Through AvalAI, you can access 33 powerful Cloudflare AI models with low latency and high performance, leveraging Cloudflare's distributed infrastructure for optimal AI inference.

Available Models

Cloudflare AI models span multiple categories and providers, offering everything from compact efficient models to large-scale reasoning systems. All models are optimized for edge deployment and provide consistent performance across Cloudflare's global network.

Meta Llama Models

Cloudflare hosts a comprehensive collection of Meta's Llama models, from the latest Llama 4 Scout to various versions of Llama 3.x series.

Model	Parameters	Specialization	Context Window
cf.llama-4-scout-17b-16e-instruct	17B (16 experts)	Multimodal, Text & Image	128K
cf.llama-3.3-70b-instruct-fp8-fast	70B (FP8)	Fast inference, General purpose	128K
cf.llama-3.1-8b-instruct-fast	8B	Fast multilingual dialogue	128K
cf.llama-3.1-8b-instruct-awq	8B (int4)	Efficient inference	128K
cf.llama-3.1-8b-instruct-fp8	8B (FP8)	Balanced performance	128K
cf.llama-3.1-8b-instruct	8B	Standard dialogue	128K
cf.llama-3.1-70b-instruct	70B	Complex reasoning	128K
cf.llama-3.2-1b-instruct	1B	Compact dialogue	128K
cf.llama-3.2-3b-instruct	3B	Agentic tasks	128K
cf.meta-llama-3-8b-instruct	8B	Improved reasoning	8K
cf.llama-3-8b-instruct-awq	8B (int4)	Efficient deployment	8K
cf.llama-3-8b-instruct	8B	Standard instruction	8K
cf.llama-guard-3-8b	8B	Content safety	8K

Llama 4 Scout - Multimodal Excellence

The newest addition to the Llama family, featuring native multimodal capabilities:

from openai import OpenAI

client = OpenAI(api_key="your-avalai-api-key", base_url="https://api.avalai.ir/v1")

# Text and image understanding
completion = client.chat.completions.create(
    model="cf.llama-4-scout-17b-16e-instruct",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What's in this image?"},
                {
                    "type": "image_url",
                    "image_url": {"url": "data:image/jpeg;base64,..."},
                },
            ],
        }
    ],
)

Responses API version This version uses `gpt-5.5` because `cf.llama-4-scout-17b-16e-instruct` may not be enabled for `/v1/responses` in the current AvalAI model data.

Use this version when the selected model supports /v1/responses. messages moves to input, and the final text is read from response.output_text.

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["AVALAI_API_KEY"],
    base_url="https://api.avalai.ir/v1",
)

response = client.responses.create(
    model="gpt-5.5",
    input=[
        {
            "role": "user",
            "content": [
                {"type": "input_text", "text": "Describe this image."},
                {"type": "input_image", "image_url": "https://example.com/image.png"},
            ],
        }
    ],
)

print(response.output_text)

• messages → input
• system message → instructions or a developer item
• choices[0].message.content → response.output_text
• for tools and multimodal output, inspect response.output by item type.

Google Gemma Models

Google's Gemma models offer excellent performance with multilingual capabilities and LoRA support.

Model	Parameters	Specialization	Context Window
cf.gemma-3-12b-it	12B	Multimodal, 140+ languages	128K
cf.gemma-7b-it-lora	7B	LoRA fine-tuning	8K
cf.gemma-2b-it-lora	2B	Compact LoRA	8K
cf.gemma-7b-it	7B	General instruction	8K

Gemma 3 - Advanced Multimodal

# Gemma 3 with extended context
completion = client.chat.completions.create(
    model="cf.gemma-3-12b-it",
    messages=[
        {
            "role": "user",
            "content": "Analyze this document and provide a comprehensive summary.",
        }
    ],
    max_tokens=2048,
)

Responses API version This version uses `gpt-5.5` because `cf.gemma-3-12b-it` may not be enabled for `/v1/responses` in the current AvalAI model data.

Use this version when the selected model supports /v1/responses. messages moves to input, and the final text is read from response.output_text.

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["AVALAI_API_KEY"],
    base_url="https://api.avalai.ir/v1",
)

response = client.responses.create(
    model="gpt-5.5",
    input=[
        {
            "role": "user",
            "content": [
                {
                    "type": "input_text",
                    "text": "Analyze this document and provide a comprehensive summary.",
                },
                {"type": "input_file", "file_id": "file_abc123"},
            ],
        }
    ],
)

print(response.output_text)

• messages → input
• system message → instructions or a developer item
• choices[0].message.content → response.output_text
• for tools and multimodal output, inspect response.output by item type.

Mistral AI Models

Mistral's latest models with enhanced vision understanding and extended context capabilities.

Model	Parameters	Specialization	Context Window
cf.mistral-small-3.1-24b-instruct	24B	Vision + Text, Function calling	128K

Mistral Small 3.1 - Vision Enhanced

# Function calling with vision
completion = client.chat.completions.create(
    model="cf.mistral-small-3.1-24b-instruct",
    messages=[
        {"role": "user", "content": "Analyze this chart and extract the key metrics"}
    ],
    tools=[
        {
            "type": "function",
            "function": {
                "name": "extract_metrics",
                "description": "Extract numerical metrics from data",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "metrics": {"type": "array", "items": {"type": "number"}}
                    },
                },
            },
        }
    ],
)

Responses API version This version uses `gpt-5.5` because `cf.mistral-small-3.1-24b-instruct` may not be enabled for `/v1/responses` in the current AvalAI model data.

Use this version when the selected model supports /v1/responses. messages moves to input, and the final text is read from response.output_text.

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["AVALAI_API_KEY"],
    base_url="https://api.avalai.ir/v1",
)

tools = [
    {
        "type": "function",
        "name": "get_current_weather",
        "description": "Get the current weather in a given location.",
        "parameters": {
            "type": "object",
            "properties": {"location": {"type": "string"}},
            "required": ["location"],
            "additionalProperties": False,
        },
    }
]

response = client.responses.create(
    model="gpt-5.5",
    input="Analyze this chart and extract the key metrics",
    tools=tools,
)

for item in response.output:
    if item.type == "function_call":
        print(item.name, item.arguments)
print(response.output_text)

• messages → input
• system message → instructions or a developer item
• choices[0].message.content → response.output_text
• for tools and multimodal output, inspect response.output by item type.

Qwen Models

Alibaba's Qwen models specialized for reasoning, code generation, and embeddings.

Model	Parameters	Specialization	Context Window
cf.qwq-32b	32B	Advanced reasoning	32K
cf.qwen2.5-coder-32b-instruct	32B	Code generation	128K
cf.qwen3-30b-a3b-fp8	30B (FP8)	Chat, balanced performance	128K
cf.qwen3-embedding-0.6b	0.6B	Text embeddings	N/A

QwQ - Reasoning Model

# Advanced reasoning tasks
completion = client.chat.completions.create(
    model="cf.qwq-32b",
    messages=[
        {
            "role": "user",
            "content": "Solve this complex math problem step by step: If a train travels 120 km in 2 hours, and then increases its speed by 20%, how long will it take to travel the next 180 km?",
        }
    ],
    temperature=0.1,  # Lower temperature for reasoning tasks
)

Responses API version This version uses `gpt-5.5` because `cf.qwq-32b` may not be enabled for `/v1/responses` in the current AvalAI model data.

Use this version when the selected model supports /v1/responses. messages moves to input, and the final text is read from response.output_text.

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["AVALAI_API_KEY"],
    base_url="https://api.avalai.ir/v1",
)

response = client.responses.create(
    model="gpt-5.5",
    instructions="You are a helpful assistant.",
    input="Solve this complex math problem step by step: If a train travels 120 km in 2 hours, and then increases its speed by 20%, how long will it take to travel the next 180 km?",
)

print(response.output_text)

• messages → input
• system message → instructions or a developer item
• choices[0].message.content → response.output_text
• for tools and multimodal output, inspect response.output by item type.

Qwen2.5-Coder - Programming Specialist

# Code generation and explanation
completion = client.chat.completions.create(
    model="cf.qwen2.5-coder-32b-instruct",
    messages=[
        {
            "role": "user",
            "content": "Write a Python function to implement a binary search tree with insert, search, and delete operations.",
        }
    ],
)

Responses API version This version uses `gpt-5.5` because `cf.qwen2.5-coder-32b-instruct` may not be enabled for `/v1/responses` in the current AvalAI model data.

Use this version when the selected model supports /v1/responses. messages moves to input, and the final text is read from response.output_text.

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["AVALAI_API_KEY"],
    base_url="https://api.avalai.ir/v1",
)

response = client.responses.create(
    model="gpt-5.5",
    instructions="You are a helpful assistant.",
    input="Write a Python function to implement a binary search tree with insert, search, and delete operations.",
)

print(response.output_text)

• messages → input
• system message → instructions or a developer item
• choices[0].message.content → response.output_text
• for tools and multimodal output, inspect response.output by item type.

DeepSeek Models

DeepSeek's distilled reasoning models offering competitive performance.

Model	Parameters	Specialization	Context Window
cf.deepseek-r1-distill-qwen-32b	32B	Reasoning, outperforms o1-mini	128K

OpenAI Open Source Models

OpenAI's open-weight models available on Cloudflare's edge network.

Model	Parameters	Specialization	Context Window
cf.gpt-oss-120b	120B	Large-scale reasoning	128K
cf.gpt-oss-20b	20B	Efficient general purpose	128K

NVIDIA Nemotron Models

NVIDIA's latest Nemotron models with hybrid LatentMoE architecture.

Model	Parameters	Specialization	Context Window
cf.nemotron-3-120b-a12b	120B (12B active)	Agentic workflows, long-context reasoning	1M

Nemotron-3-120B-A12B - Agentic Excellence

NVIDIA's Nemotron-3-Super-120B-A12B is a large language model designed to deliver strong agentic, reasoning, and conversational capabilities. It employs a hybrid LatentMoE architecture utilizing interleaved Mamba-2 and MoE layers with Multi-Token Prediction (MTP) for faster text generation.

Feature	Details
Total Parameters	120B (12B active)
Architecture	LatentMoE - Mamba-2 + MoE + Attention hybrid
Context window	1,000,000 tokens
Input pricing	$0.50 / 1M tokens
Cached input pricing	$0.05 / 1M tokens (90% cost reduction)
Output pricing	$1.50 / 1M tokens
Supported languages	English, French, German, Italian, Japanese, Spanish, Chinese
Best for	Agentic workflows, long-context reasoning, high-volume workloads, IT ticket automation

Key Features:

• 1M Context Window: Industry-leading context length for extensive documents
• Configurable Reasoning: Enable or disable reasoning mode via chat template
• Multi-Token Prediction: MTP layers for faster text generation
• Tool Use & RAG: Excellent for tool calling and retrieval-augmented generation
• Cost-Effective: Only 12B active parameters for exceptional efficiency

Benchmark Highlights:

• SWE-Bench (OpenHands): 60.47%
• AIME 2025: 90.21%
• HMMT Feb 2025 (with tools): 94.73%
• LiveCodeBench: 81.19%

from openai import OpenAI

client = OpenAI(api_key="your-avalai-api-key", base_url="https://api.avalai.ir/v1")

# Long-context reasoning with Nemotron
completion = client.chat.completions.create(
    model="cf.nemotron-3-120b-a12b",
    messages=[
        {
            "role": "user",
            "content": "Analyze this extensive codebase and suggest architectural improvements for better scalability.",
        }
    ],
    temperature=1.0,  # Recommended setting for Nemotron
    top_p=0.95,
)

print(completion.choices[0].message.content)

Responses API version This version uses `gpt-5.5` because `cf.nemotron-3-120b-a12b` may not be enabled for `/v1/responses` in the current AvalAI model data.

Use this version when the selected model supports /v1/responses. messages moves to input, and the final text is read from response.output_text.

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["AVALAI_API_KEY"],
    base_url="https://api.avalai.ir/v1",
)

response = client.responses.create(
    model="gpt-5.5",
    instructions="You are a helpful assistant.",
    input="Analyze this extensive codebase and suggest architectural improvements for better scalability.",
)

print(response.output_text)

• messages → input
• system message → instructions or a developer item
• choices[0].message.content → response.output_text
• for tools and multimodal output, inspect response.output by item type.

IBM Granite Models

IBM's Granite models for enterprise applications.

Model	Parameters	Specialization	Context Window
cf.granite-4.0-h-micro	Micro	Efficient enterprise chat	128K

Image Generation Models

Cloudflare AI offers powerful image generation models via the v1/images/generations endpoint.

Model	Description	Base Price (1MP)
cf.flux-2-klein-9b	FLUX 2 Klein 9B parameter model	$0.015/image
cf.flux-2-klein-4b	FLUX 2 Klein 4B compact model	$0.010/image
cf.flux-2-dev	FLUX 2 Development model	$0.010/image
cf.lucid-origin	Lucid Origin image generation	$0.015/image
cf.phoenix-1.0	Phoenix 1.0 image generation	$0.015/image

Image Generation Example

from openai import OpenAI

client = OpenAI(api_key="your-avalai-api-key", base_url="https://api.avalai.ir/v1")

# Generate an image with FLUX 2 Klein
response = client.images.generate(
    model="cf.flux-2-klein-9b",
    prompt="A futuristic cityscape at sunset with flying cars",
    n=1,
    size="1024x1024",
)

print(response.data[0].url)

Embedding Models

Cloudflare AI provides embedding models for semantic search and text analysis via the v1/embeddings endpoint.

Model	Owner	Description	Input Price ($/1M tokens)
cf.qwen3-embedding-0.6b	Alibaba	Qwen3 Embedding 0.6B	$0.012
cf.plamo-embedding-1b	PFN	PLaMo Embedding 1B	$0.019
cf.embeddinggemma-300m	Google	EmbeddingGemma 300M	$0.012

Embedding Example

from openai import OpenAI

client = OpenAI(api_key="your-avalai-api-key", base_url="https://api.avalai.ir/v1")

# Generate embeddings
response = client.embeddings.create(
    model="cf.qwen3-embedding-0.6b",
    input="Machine learning enables computers to learn from data.",
)

embedding = response.data[0].embedding
print(f"Embedding dimension: {len(embedding)}")

Key Parameters

Cloudflare models support standard OpenAI-compatible parameters:

Parameter	Description	Default	Range
temperature	Controls randomness	0.7	0.0-2.0
max_tokens	Maximum response length	2048	1-4096
top_p	Nucleus sampling	1.0	0.0-1.0
frequency_penalty	Reduces repetition	0.0	-2.0-2.0
presence_penalty	Encourages new topics	0.0	-2.0-2.0

API Endpoints

Cloudflare models are available through multiple AvalAI endpoints depending on model type:

• Chat Completions: v1/chat/completions (full support)
• Responses: v1/responses (partial support)
• Embeddings: v1/embeddings (embedding models)
• Image Generation: v1/images/generations (image models)
• Messages: v1/messages (partial support)

Example Usage

Basic Text Generation

from openai import OpenAI

client = OpenAI(api_key="your-avalai-api-key", base_url="https://api.avalai.ir/v1")

completion = client.chat.completions.create(
    model="cf.llama-3.1-8b-instruct",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain quantum computing in simple terms."},
    ],
    temperature=0.7,
    max_tokens=1024,
)

print(completion.choices[0].message.content)

Responses API version This version uses `gpt-5.5` because `cf.llama-3.1-8b-instruct` may not be enabled for `/v1/responses` in the current AvalAI model data.

Use this version when the selected model supports /v1/responses. messages moves to input, and the final text is read from response.output_text.

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["AVALAI_API_KEY"],
    base_url="https://api.avalai.ir/v1",
)

response = client.responses.create(
    model="gpt-5.5",
    instructions="You are a helpful assistant.",
    input="Explain quantum computing in simple terms.",
)

print(response.output_text)

• messages → input
• system message → instructions or a developer item
• choices[0].message.content → response.output_text
• for tools and multimodal output, inspect response.output by item type.

Streaming Responses

# Streaming for real-time responses
stream = client.chat.completions.create(
    model="cf.llama-3.3-70b-instruct-fp8-fast",
    messages=[
        {"role": "user", "content": "Write a short story about AI and humanity."}
    ],
    stream=True,
)

for chunk in stream:
    if chunk.choices[0].delta.content is not None:
        print(chunk.choices[0].delta.content, end="")

Responses API version This version uses `gpt-5.5` because `cf.llama-3.3-70b-instruct-fp8-fast` may not be enabled for `/v1/responses` in the current AvalAI model data.

Use this version when the selected model supports /v1/responses. messages moves to input, and the final text is read from response.output_text.

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["AVALAI_API_KEY"],
    base_url="https://api.avalai.ir/v1",
)

response = client.responses.create(
    model="gpt-5.5",
    instructions="You are a helpful assistant.",
    input="Write a short story about AI and humanity.",
)

print(response.output_text)

• messages → input
• system message → instructions or a developer item
• choices[0].message.content → response.output_text
• for tools and multimodal output, inspect response.output by item type.

Function Calling

# Function calling with supported models
completion = client.chat.completions.create(
    model="cf.mistral-small-3.1-24b-instruct",
    messages=[{"role": "user", "content": "What's the weather like in Paris?"}],
    tools=[
        {
            "type": "function",
            "function": {
                "name": "get_weather",
                "description": "Get current weather for a city",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "city": {"type": "string", "description": "City name"}
                    },
                    "required": ["city"],
                },
            },
        }
    ],
)

Responses API version This version uses `gpt-5.5` because `cf.mistral-small-3.1-24b-instruct` may not be enabled for `/v1/responses` in the current AvalAI model data.

Use this version when the selected model supports /v1/responses. messages moves to input, and the final text is read from response.output_text.

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["AVALAI_API_KEY"],
    base_url="https://api.avalai.ir/v1",
)

tools = [
    {
        "type": "function",
        "name": "get_current_weather",
        "description": "Get the current weather in a given location.",
        "parameters": {
            "type": "object",
            "properties": {"location": {"type": "string"}},
            "required": ["location"],
            "additionalProperties": False,
        },
    }
]

response = client.responses.create(
    model="gpt-5.5",
    input="What",
    tools=tools,
)

for item in response.output:
    if item.type == "function_call":
        print(item.name, item.arguments)
print(response.output_text)

• messages → input
• system message → instructions or a developer item
• choices[0].message.content → response.output_text
• for tools and multimodal output, inspect response.output by item type.

Best Practices

Model Selection

• For speed: Use FP8 or AWQ quantized models like cf.llama-3.3-70b-instruct-fp8-fast
• For reasoning: Choose cf.qwq-32bor cf.deepseek-r1-distill-qwen-32b
• For code: Use cf.qwen2.5-coder-32b-instruct
• For multimodal: Select cf.llama-4-scout-17b-16e-instruct or cf.gemma-3-12b-it
• For efficiency: Try compact models like cf.llama-3.2-1b-instruct

Performance Optimization

1. Use appropriate quantization: FP8 models offer good balance of speed and quality
2. Leverage edge deployment: Models run on Cloudflare's global network for low latency
3. Context management: Utilize extended context windows (up to 128K) efficiently
4. Streaming: Enable streaming for better user experience in interactive applications

Content Safety

Implement cf.llama-guard-3-8bfor content moderation:

# Content safety check
safety_check = client.chat.completions.create(
    model="cf.llama-guard-3-8b",
    messages=[{"role": "user", "content": "User message to check for safety"}],
)

Responses API version This version uses `gpt-5.5` because `cf.llama-guard-3-8b` may not be enabled for `/v1/responses` in the current AvalAI model data.

Use this version when the selected model supports /v1/responses. messages moves to input, and the final text is read from response.output_text.

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["AVALAI_API_KEY"],
    base_url="https://api.avalai.ir/v1",
)

response = client.responses.create(
    model="gpt-5.5",
    instructions="You are a helpful assistant.",
    input="User message to check for safety",
)

print(response.output_text)

• messages → input
• system message → instructions or a developer item
• choices[0].message.content → response.output_text
• for tools and multimodal output, inspect response.output by item type.

Using Cloudflare Models via AvalAI

Cloudflare AI models are seamlessly integrated into AvalAI's unified API. All models use the standard OpenAI-compatible interface, making it easy to switch between different Cloudflare models or combine them with other providers.

Authentication

client = OpenAI(
    api_key="your-avalai-api-key",  # Your AvalAI API key
    base_url="https://api.avalai.ir/v1",  # AvalAI endpoint
)

Rate Limits

Cloudflare models follow AvalAI's standard rate limiting policies. Check your tier limits in the Rate Limits Guide.

Related Resources

• Chat Completions API Reference
• Model Selection Guide
• Function Calling Guide
• Vision Capabilities Guide
• Streaming Responses Guide
• Rate Limits Guide
• Best Practices Guide