> ## Documentation Index
> Fetch the complete documentation index at: https://docs.promptguard.co/llms.txt
> Use this file to discover all available pages before exploring further.

# Supported LLM Providers

> Complete list of all LLM providers and models supported by PromptGuard

PromptGuard supports all major LLM providers through a unified OpenAI-compatible API. This page lists all supported providers and their exact model names.

## Overview

PromptGuard automatically routes requests to the correct provider based on the model name. No configuration needed--just use the model name in your request, and PromptGuard handles the rest.

<Info>
  All providers use the same OpenAI-compatible API format. Simply change the `model` parameter in your request to use different providers.
</Info>

## OpenAI Models

OpenAI provides the GPT series of models, including the latest GPT-5.x series.

### GPT-5.x Series (Latest - Dec 2025)

* `gpt-5.2` - Latest GPT-5 model
* `gpt-5.2-pro` - Pro variant
* `gpt-5.2-instant` - Fast variant
* `gpt-5.1` - Previous version

### GPT-4.1 Series (2025)

* `gpt-4.1` - 1M context window
* `gpt-4.1-mini` - Smaller variant
* `gpt-4.1-nano` - Compact variant

### GPT-4o Series (Multimodal)

* `gpt-5-nano` - Multimodal model
* `gpt-5-nano-mini` - Smaller multimodal variant
* `gpt-5-nano-2024-*` - Versioned releases

### GPT-4 Series

* `gpt-4-turbo` - Turbo variant
* `gpt-4-turbo-preview` - Preview version
* `gpt-4-turbo-2024-*` - Versioned releases
* `gpt-4-0314`, `gpt-4-0613`, `gpt-4-1106-preview` - Historical versions
* `gpt-4` - Base model

### GPT-3.5 Series

* `gpt-3.5-turbo` - Latest GPT-3.5
* `gpt-3.5-turbo-16k` - Extended context
* `gpt-3.5-turbo-0125` - Versioned release

### Embeddings Models

* `text-embedding-ada-002`
* `text-embedding-3-small`
* `text-embedding-3-large`

### Specialized Models

* `dall-e-2`, `dall-e-3` - Image generation
* `whisper-1` - Audio transcription
* `tts-1`, `tts-1-hd` - Text-to-speech

## Anthropic Claude Models

Anthropic provides the Claude series of models, known for safety and reliability.

### Claude 4.x Series (Latest - 2025)

* `claude-sonnet-4-5-*` - Latest Sonnet 4.5 models
* `claude-sonnet-4-0-*` - Sonnet 4.0 models
* `claude-opus-4-1-*` - Opus 4.1 models
* `claude-opus-4-0-*` - Opus 4.0 models
* `claude-haiku-4-*` - Haiku 4.5 models
* `claude-haiku-4-0-*` - Haiku 4.0 models

### Claude 3.7 Series (Feb 2025)

* `claude-3-7-sonnet-*` - Hybrid reasoning model

### Claude 3.5 Series (2024)

* `claude-3-5-sonnet-*` - Sonnet variant
* `claude-3-5-haiku-*` - Haiku variant

### Claude 3 Family (March 2024)

* `claude-3-opus-*` - Opus models
* `claude-3-sonnet-*` - Sonnet models
* `claude-3-haiku-*` - Haiku models

### Legacy Models

* `claude-2.1`, `claude-2.0` - Claude 2 series
* `claude-instant-1.2` - Instant variant

## Google Gemini Models

Google's Gemini models provide advanced multimodal capabilities.

### Gemini 3.x Series (Latest - Nov-Dec 2025)

* `gemini-3-pro` - Latest Pro model
* `gemini-3-flash` - Fast variant
* `gemini-3-deep-think` - Reasoning variant

### Gemini 2.5 Series (2025)

* `gemini-2.5-pro-latest` - Pro variant
* `gemini-2.5-flash-latest` - Flash variant

### Gemini 2.0 Series (Experimental)

* `gemini-2.0-flash-exp` - Experimental flash
* `gemini-2.0-flash-thinking-exp` - Thinking variant

### Gemini 1.5 Series

* `gemini-1.5-pro` - Pro model
* `gemini-1.5-flash` - Flash variant
* `gemini-1.5-pro-latest` - Latest Pro

### Legacy Models

* `gemini-pro` - Original Pro model
* `gemini-pro-vision` - Vision variant

<Warning>
  Gemini models require API key authentication. Use your Google API key in the `Authorization` header.
</Warning>

## Mistral AI Models

Mistral AI provides high-performance open models optimized for various use cases.

### Frontier Models (Latest - 2025-2026)

#### Generalist Models

* `mistral-large-3-25-12` / `mistral-large-2512` - Latest large multimodal model (v25.12)
* `mistral-medium-3-1-25-08` / `mistral-medium-2508` - Frontier-class multimodal model (v25.08)
* `mistral-small-3-2-25-06` / `mistral-small-2506` - Latest small model (v25.06)
* `ministral-3-14b-25-12` - Powerful 14B model with text and vision (v25.12)
* `ministral-3-8b-25-12` - Efficient 8B model with text and vision (v25.12)
* `ministral-3-3b-25-12` - Compact 3B model with text and vision (v25.12)

#### Reasoning Models

* `magistral-medium-1-2-25-09` / `magistral-medium-2509` - Frontier-class multimodal reasoning (v25.09)
* `magistral-small-1-2-25-09` / `magistral-small-2509` - Small multimodal reasoning (v25.09)

#### Specialist Models

* `codestral-25-08` - Code completion model (v25.08)
* `devstral-2-25-12` - Code agents model (v25.12)
* `voxtral-mini-transcribe-25-07` - Audio transcription model (v25.07)
* `voxtral-mini-25-07` - Mini audio model (v25.07)
* `voxtral-small-25-07` - Small audio model (v25.07)
* `ocr-3-25-12` - OCR service for Document AI (v25.12)

### Legacy Models (Still Supported)

* `mistral-large-latest` - Alias for latest large model
* `mistral-small-latest` - Alias for latest small model
* `mistral-tiny-latest` - Compact model
* `mistral-medium-latest` - Medium variant
* `pixtral-12b-2409` - Legacy multimodal model

## DeepSeek Models

DeepSeek provides competitive open-weight LLMs with strong performance.

### Available Models

* `deepseek-chat` - DeepSeek-V3.2 (Non-thinking Mode), 128K context, supports JSON output, tool calls, and FIM completion
* `deepseek-reasoner` - DeepSeek-V3.2 (Thinking Mode), 128K context, supports JSON output and tool calls, optimized for reasoning tasks

## Cohere Models

Cohere specializes in enterprise-focused models optimized for RAG and agentic AI.

### Latest Models

* `command-r7b-12-2024` - Small, fast model for RAG, tool use, and agents
* `command-r-plus-08-2024` - Latest for RAG and agents
* `command-r-08-2024` - Optimized for RAG tasks
* `command-a-03-2025` - Most performant model for tool use, agents, and RAG

### Multilingual Models

* `aya-8b` - 8B parameter multilingual model
* `aya-23b` - 23B parameter multilingual model

## Groq Models

Groq provides ultra-fast inference for various open-source models.

### Meta Llama Models

* `llama-3.1-8b-instant` - 8B instant model
* `llama-3.3-70b-versatile` - 70B versatile model
* `llama-4-maverick-17b-128e-instruct` - Maverick variant
* `llama-4-scout-17b-16e-instruct` - Scout variant
* `meta-llama/llama-guard-4-12b` - Guard model

### Alibaba Qwen Models

* `qwen/qwen3-32b` - 32B Qwen model

### OpenAI Models on Groq

* `openai/gpt-oss-120b` - 120B open-source GPT
* `openai/gpt-oss-20b` - 20B open-source GPT

### Audio Models

* `whisper-large-v3` - Large Whisper model
* `whisper-large-v3-turbo` - Turbo variant

## Azure OpenAI

Azure OpenAI provides the same models as OpenAI, hosted on Microsoft Azure infrastructure.

<Info>
  Azure OpenAI works in passthrough mode. Specify your Azure deployment name as `azure/deployment-name` and include your Azure resource credentials in headers.
</Info>

### Supported Models

All OpenAI models are available through Azure OpenAI:

* GPT-5.x, GPT-4.x, GPT-3.5 series
* Embeddings models
* Specialized models (DALL-E, Whisper, TTS)

## Ollama (Local Models)

Ollama lets you run open-source LLMs locally. PromptGuard proxies requests to your Ollama instance, applying full threat detection to local model traffic.

### Model Naming

Use the `ollama/` prefix followed by your local model name:

* `ollama/llama3` - Meta Llama 3
* `ollama/llama3:70b` - Llama 3 70B variant
* `ollama/mistral` - Mistral 7B
* `ollama/mixtral` - Mixtral 8x7B
* `ollama/codellama` - Code Llama
* `ollama/phi3` - Microsoft Phi-3
* `ollama/gemma2` - Google Gemma 2
* `ollama/qwen2` - Alibaba Qwen 2

### Environment Variables

```bash theme={"system"}
OLLAMA_BASE_URL=http://localhost:11434   # Custom Ollama endpoint (default: localhost:11434)
```

<Info>
  Any model available in your local Ollama instance can be used. Run `ollama list` to see available models.
</Info>

## vLLM (High-Throughput Inference)

vLLM is a high-throughput inference engine for self-hosted LLMs. PromptGuard adds \~30ms of security scanning overhead to vLLM's fast inference pipeline.

### Model Naming

Use the `vllm/` prefix followed by the model identifier loaded in your vLLM server:

* `vllm/meta-llama/Llama-3-70B-Instruct` - Llama 3 70B
* `vllm/meta-llama/Llama-3-8B-Instruct` - Llama 3 8B
* `vllm/mistralai/Mistral-7B-Instruct-v0.3` - Mistral 7B
* `vllm/Qwen/Qwen2-72B-Instruct` - Qwen 2 72B
* `vllm/microsoft/Phi-3-medium-128k-instruct` - Phi-3 Medium

### Environment Variables

```bash theme={"system"}
VLLM_BASE_URL=http://localhost:8000    # Custom vLLM endpoint (default: localhost:8000)
```

<Info>
  The model name after `vllm/` must match the `--model` argument used when starting your vLLM server.
</Info>

## AWS Bedrock

AWS Bedrock provides access to foundation models from multiple providers through a unified AWS API.

### Model Naming

Use the `bedrock/` prefix followed by the Bedrock model ID:

* `bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0` - Claude 3.5 Sonnet
* `bedrock/anthropic.claude-3-haiku-20240307-v1:0` - Claude 3 Haiku
* `bedrock/meta.llama3-70b-instruct-v1:0` - Llama 3 70B
* `bedrock/amazon.titan-text-premier-v2:0` - Amazon Titan Text Premier
* `bedrock/amazon.nova-pro-v1:0` - Amazon Nova Pro
* `bedrock/mistral.mistral-large-2407-v1:0` - Mistral Large
* `bedrock/cohere.command-r-plus-v1:0` - Cohere Command R+

### Environment Variables

```bash theme={"system"}
AWS_ACCESS_KEY_ID=your-aws-access-key
AWS_SECRET_ACCESS_KEY=your-aws-secret-key
AWS_REGION=us-east-1                    # AWS region for Bedrock
```

<Info>
  PromptGuard uses your AWS credentials to authenticate with Bedrock. Ensure your IAM role has `bedrock:InvokeModel` permissions.
</Info>

## Model Detection

PromptGuard automatically detects which provider to use based on the model name prefix:

1. **Azure OpenAI**: Models starting with `azure/`
2. **Ollama**: Models starting with `ollama/`
3. **vLLM**: Models starting with `vllm/`
4. **AWS Bedrock**: Models starting with `bedrock/`
5. **Groq**: Models starting with `llama-`, `qwen/`, `openai/`, `whisper-`, etc.
6. **Anthropic**: Models starting with `claude-`
7. **Gemini**: Models starting with `gemini-`
8. **Mistral**: Models starting with `mistral-` or `pixtral-`
9. **DeepSeek**: Models starting with `deepseek-`
10. **Cohere**: Models starting with `command-`, `aya-`, or `cohere-`
11. **OpenAI**: All other models (fallback)

<Info>
  Provider selection is automatic. Just use the model name, and PromptGuard routes it to the correct provider.
</Info>

## Code Examples

### OpenAI

```python theme={"system"}
from openai import OpenAI

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

response = client.chat.completions.create(
    model="gpt-5.2",
    messages=[{"role": "user", "content": "Hello!"}]
)
```

### Anthropic Claude

```python theme={"system"}
from openai import OpenAI

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

response = client.chat.completions.create(
    model="claude-haiku-4-5",
    messages=[{"role": "user", "content": "Hello!"}]
)
```

### Google Gemini

```python theme={"system"}
from openai import OpenAI

client = OpenAI(
    base_url="https://api.promptguard.co/api/v1",
    api_key="your-google-api-key"  # Use your Google API key
)

response = client.chat.completions.create(
    model="gemini-2.5-flash-lite",
    messages=[{"role": "user", "content": "Hello!"}]
)
```

### Mistral AI

```python theme={"system"}
from openai import OpenAI

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

response = client.chat.completions.create(
    model="ministral-3b-latest",
    messages=[{"role": "user", "content": "Hello!"}]
)
```

### DeepSeek

```python theme={"system"}
from openai import OpenAI

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

response = client.chat.completions.create(
    model="deepseek-chat",
    messages=[{"role": "user", "content": "Hello!"}]
)
```

### Cohere

```python theme={"system"}
from openai import OpenAI

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

response = client.chat.completions.create(
    model="command-r7b-12-2024",
    messages=[{"role": "user", "content": "Hello!"}]
)
```

### Groq (Llama)

```python theme={"system"}
from openai import OpenAI

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

response = client.chat.completions.create(
    model="llama-3.1-8b-instant",
    messages=[{"role": "user", "content": "Hello!"}]
)
```

### Ollama (Local)

```python theme={"system"}
from openai import OpenAI

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

response = client.chat.completions.create(
    model="ollama/llama3",
    messages=[{"role": "user", "content": "Hello!"}]
)
```

### vLLM

```python theme={"system"}
from openai import OpenAI

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

response = client.chat.completions.create(
    model="vllm/meta-llama/Llama-3-70B-Instruct",
    messages=[{"role": "user", "content": "Hello!"}]
)
```

### AWS Bedrock

```python theme={"system"}
from openai import OpenAI

client = OpenAI(
    base_url="https://api.promptguard.co/api/v1",
    api_key="your-promptguard-key",
    default_headers={
        "X-AWS-Access-Key": "your-aws-access-key",
        "X-AWS-Secret-Key": "your-aws-secret-key",
        "X-AWS-Region": "us-east-1",
    }
)

response = client.chat.completions.create(
    model="bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0",
    messages=[{"role": "user", "content": "Hello!"}]
)
```

## Getting Help

If you encounter issues with a specific model:

1. **Check model name**: Ensure you're using the exact model name from this list
2. **Verify API key**: Make sure your API key is valid for the provider
3. **Check provider status**: Some providers may have temporary outages
4. **See troubleshooting**: Visit our [troubleshooting guide](/production/troubleshooting)

For the most up-to-date model list, check each provider's official documentation.