Choosing the Right Model for Your Portrix Workload

Choosing the right model is about balancing cost, latency, capability, and context window size for your specific workload. With 400+ models available on Portrix, the options can feel overwhelming — this guide cuts through the noise so you can make a confident decision and start iterating fast.

Model ID format

Every model on Portrix is identified by a {provider}/{model-name} string. You pass this string as the model parameter in any request:

openai/gpt-4o
anthropic/claude-3-5-sonnet
google/gemini-2.0-flash
mistral/mistral-large

The provider prefix routes the request to the correct backend automatically — no additional configuration required.

Specifying the model via header

If your HTTP client or framework does not support modifying the request body, you can pass the model ID in the x-portrix-model header instead of the model field. The header takes precedence when both are present:

curl https://api.portrix.ai/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -H "x-portrix-model: anthropic/claude-3-5-sonnet" \
  -d '{"messages": [{"role": "user", "content": "Hello!"}]}'

This is particularly useful when proxying requests through middleware that cannot rewrite the body, or when building multi-tenant systems where the calling layer selects the model on behalf of the application.

By use case

Chat & Reasoning
Long Context
Embeddings
Vision
Coding

These models excel at general-purpose conversation, instruction following, and multi-step reasoning tasks.

Model	Strengths
`openai/gpt-4o`	Versatile, fast, strong reasoning and instruction following
`anthropic/claude-3-5-sonnet`	Nuanced writing, long-form reasoning, low hallucination rate
`google/gemini-2.0-flash`	Very low latency, cost-efficient for high-volume workloads
`mistral/mistral-large`	Strong multilingual support, competitive European data residency

response = client.chat.completions.create(
    model="openai/gpt-4o",
    messages=[{"role": "user", "content": "Explain transformer attention in simple terms."}],
)

Use these models when your input — documents, codebases, transcripts — exceeds typical context limits.

Model	Context Window
`anthropic/claude-3-5-sonnet`	200,000 tokens
`google/gemini-1.5-pro`	1,000,000 tokens

with open("long_document.txt") as f:
    document = f.read()

response = client.chat.completions.create(
    model="google/gemini-1.5-pro",
    messages=[
        {"role": "user", "content": f"Summarise this document:\n\n{document}"}
    ],
)

Embeddings convert text into dense vectors for semantic search, clustering, and retrieval-augmented generation (RAG).

Model	Dimensions	Best For
`openai/text-embedding-3-small`	1,536	Cost-efficient, high-throughput indexing
`openai/text-embedding-3-large`	3,072	Highest accuracy retrieval tasks

response = client.embeddings.create(
    model="openai/text-embedding-3-small",
    input="Portrix is a unified AI gateway.",
)

vector = response.data[0].embedding

Vision models accept image inputs alongside text, enabling document understanding, screenshot analysis, and multimodal reasoning.

Model	Notes
`openai/gpt-4o`	Strong OCR, diagram interpretation, and image Q&A
`google/gemini-2.0-flash`	Fast and cost-efficient for image classification at scale

response = client.chat.completions.create(
    model="openai/gpt-4o",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What is in this image?"},
                {"type": "image_url", "image_url": {"url": "https://example.com/chart.png"}},
            ],
        }
    ],
)

These models perform strongly on code generation, debugging, and technical explanation tasks.

Model	Strengths
`openai/o3-mini`	Deep reasoning for hard algorithmic and engineering problems
`anthropic/claude-3-5-sonnet`	Clean, well-commented code with excellent test generation

response = client.chat.completions.create(
    model="anthropic/claude-3-5-sonnet",
    messages=[
        {"role": "user", "content": "Write a Python function to binary-search a sorted list."}
    ],
)

Cost vs quality tradeoffs

Use this table as a starting point — actual pricing is available in your Portrix dashboard.

Model	Provider	Relative Cost	Relative Speed	Best For
`google/gemini-2.0-flash`	Google	$	⚡⚡⚡	High-volume, latency-sensitive apps
`mistral/mistral-large`	Mistral	$	⚡⚡⚡	Multilingual and EU-resident workloads
`openai/gpt-4o`	OpenAI	$$	⚡⚡	General-purpose production workloads
`google/gemini-1.5-pro`	Google	$$	⚡⚡	Very long document processing
`anthropic/claude-3-5-sonnet`	Anthropic	$$	⚡⚡	Writing, reasoning, low hallucination
`openai/o3-mini`	OpenAI	$$$	⚡	Complex coding and math reasoning
`openai/text-embedding-3-small`	OpenAI	$	⚡⚡⚡	Embedding at scale
`openai/text-embedding-3-large`	OpenAI	$$	⚡⚡	High-precision semantic retrieval

Listing all available models

Retrieve the full, live list of models available to your account at any time:

models = client.models.list()

for model in models.data:
    print(model.id)

curl https://api.portrix.ai/v1/models \
  -H "Authorization: Bearer YOUR_API_KEY"

The response is a standard OpenAI-compatible ModelList object. Each entry includes the model ID, provider, and any capability flags your account has access to.

Using model routing

If you want Portrix to select the best model automatically based on a routing strategy, set the x-portrix-route header instead of specifying a model directly:

curl https://api.portrix.ai/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "x-portrix-route: cheapest" \
  -H "Content-Type: application/json" \
  -d '{"messages": [{"role": "user", "content": "Hello!"}]}'

Supported routing strategies include cheapest, fastest, and balanced. See the Fallbacks & Load Balancing guide for the full routing reference.

Start with a cheaper, faster model like google/gemini-2.0-flash during development and evaluation. Once you’ve validated your prompts and logic, benchmark against a more capable model to decide whether the quality improvement justifies the cost increase.

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Choosing the Right Model for Your Portrix Workload

Model ID format

Specifying the model via header

By use case

Cost vs quality tradeoffs

Listing all available models

Using model routing

​Model ID format

​Specifying the model via header

​By use case

​Cost vs quality tradeoffs

​Listing all available models

​Using model routing

Model ID format

Specifying the model via header

By use case

Cost vs quality tradeoffs

Listing all available models

Using model routing