Technical Specifications of `gpt-4o-mini-search-preview`

Specification	Details
Model ID	`gpt-4o-mini-search-preview`
Model family	GPT-4o mini
Primary modality	Multimodal
Supported inputs	Text, image
Core strengths	Search-oriented interactions, query understanding, concise answer synthesis, retrieval workflow support
Instruction following	Strong support for guided prompting and task formatting
Structured outputs	Suitable for JSON and other schema-based response formats
Tool use	Designed to work well with external search and function/tool calling
Typical latency/cost profile	Compact model optimized for lighter-weight deployments and high-throughput use cases
Common use cases	In-product search assistants, knowledge-base QA, e-commerce discovery, ranking/routing query understanding, RAG pipelines

What is `gpt-4o-mini-search-preview`?

gpt-4o-mini-search-preview is a compact multimodal model in the GPT-4o family built for search-centric experiences and retrieval-enhanced applications. It is well suited for systems that need to interpret user intent, rewrite or decompose queries, synthesize concise responses from retrieved information, and support grounded workflows through external search integration.

Because it accepts both text and image inputs, the model can participate in broader discovery and assistance experiences beyond plain text search. It is especially useful in applications where fast query understanding, controlled response formatting, and tool-enabled orchestration matter more than long-form generation. Common examples include customer-facing search copilots, internal knowledge assistants, product discovery flows, and retrieval pipelines that depend on query classification, ranking assistance, and answer generation.

Main features of `gpt-4o-mini-search-preview`

Search-oriented reasoning: Helps interpret ambiguous user intent, reformulate queries, and support retrieval-focused interactions.
Multimodal input support: Accepts both text and image inputs, enabling richer search and discovery workflows.
Concise answer synthesis: Produces short, useful summaries and direct responses appropriate for search-style UX.
Tool integration readiness: Works effectively with function calling and external tools for search, browsing, and RAG orchestration.
Structured output compatibility: Can generate responses in organized formats such as JSON for downstream systems.
Instruction-following behavior: Handles guided prompts reliably for classification, routing, extraction, and answer formatting tasks.
Knowledge-base QA support: Fits well in systems that retrieve documents first and then ask the model to produce grounded answers.
E-commerce and catalog discovery: Useful for interpreting shopping intent, refining filters, and improving product search interactions.
Ranking and routing assistance: Can help classify queries and prepare them for retrieval, ranking, or workflow branching logic.
Efficient deployment profile: As a compact model, it is appropriate for scalable, cost-aware integrations that still need multimodal and tool-aware behavior.

How to access and integrate `gpt-4o-mini-search-preview`

To get started, create an account on CometAPI and generate your API key from the dashboard. After that, store the key securely and use it in the Authorization header for all requests.

Step 2: Send Requests to `gpt-4o-mini-search-preview` API

Use CometAPI’s OpenAI-compatible endpoint and specify the model as gpt-4o-mini-search-preview.

curl https://api.cometapi.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $COMETAPI_API_KEY" \
  -d '{
    "model": "gpt-4o-mini-search-preview",
    "messages": [
      {
        "role": "user",
        "content": "Summarize the main intent behind this search query: best running shoes for flat feet"
      }
    ]
  }'

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_COMETAPI_API_KEY",
    base_url="https://api.cometapi.com/v1"
)

response = client.chat.completions.create(
    model="gpt-4o-mini-search-preview",
    messages=[
        {
            "role": "user",
            "content": "Summarize the main intent behind this search query: best running shoes for flat feet"
        }
    ]
)

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

Step 3: Retrieve and Verify Results