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Portrix gives you unified access to more than 400 models from many AI providers, all addressable through a single API. Whether you want OpenAI’s GPT-4o, Anthropic’s Claude, Google’s Gemini, or a Meta Llama model running on Groq, you reach every one of them through the same endpoint using the same request format.

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Model naming convention

Portrix identifies every model using a {provider}/{model-name} format. This convention tells the gateway which provider to route your request to and which model to invoke. You pass this identifier as the model field in your request body, just as you would with any OpenAI-compatible API.

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Supported providers

Portrix aggregates models from a wide range of providers. New providers and models are added continuously.

OpenAI

GPT-4o, GPT-4o mini, GPT-4 Turbo, o1, o3, text-embedding models, and more.

Anthropic

Claude 3.5 Sonnet, Claude 3.5 Haiku, Claude 3 Opus, and the full Claude 3 family.

Google

Gemini 2.0 Flash, Gemini 1.5 Pro, Gemini 1.5 Flash, and text embedding models.

Mistral

Mistral Large, Mistral Small, Mixtral 8x7B, Codestral, and open-weight variants.

Meta (Llama)

Llama 3.1 and Llama 3.2 models in various sizes, served through multiple infrastructure providers.

Cohere

Command R+, Command R, and Embed models for retrieval-augmented generation.

Groq

Ultra-low-latency inference for Llama, Mixtral, and Gemma models.

And more

Perplexity, Together AI, Fireworks, DeepSeek, Qwen, and dozens of additional providers.

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Model capabilities

Different models support different capabilities. Not every model handles vision input, tool calls, or embeddings — check the model’s metadata before building features that depend on specific capabilities. To see all available models and their capabilities programmatically, call the /v1/models endpoint:
Use GET /v1/models to programmatically list all available models and their metadata, including supported capabilities, context window sizes, and pricing information.
You can also list models using the OpenAI SDK pointed at the Portrix base URL:

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Choosing a model

Selecting the right model involves balancing several factors. Here is a quick guide to get you started. Cost vs. quality — Flagship models like openai/gpt-4o and anthropic/claude-3-5-sonnet deliver the highest quality but are more expensive per token. Smaller models like openai/gpt-4o-mini or anthropic/claude-3-haiku are significantly cheaper and still capable for many tasks. Latency — If your use case is interactive (for example, a customer-facing chatbot), prioritize models with low time-to-first-token. Groq-hosted models and mini/flash variants are optimized for speed. Context window — Long-document analysis, large codebases, or multi-turn conversations with extensive history require a large context window. Models like google/gemini-1.5-pro (1M tokens) and anthropic/claude-3-5-sonnet (200K tokens) are strong choices here. Capability requirements — If you need vision input or tool calling, verify the model supports those features using the /v1/models endpoint before committing to it in production. For a comprehensive comparison of models across these dimensions, see the Model Selection guide.