GPT-4o: Complete Specifications, Pricing, API Access & Use Cases (2026)

GPT-4o is an OpenAI model for text generation, image understanding, coding, multilingual communication, and tool-enabled applications. Its balance of capability, speed, and established API support keeps it relevant to existing production systems, although newer models may offer better context capacity or pricing.

What Is GPT-4o?

OpenAI introduced GPT-4o on May 13, 2024. The "o" means "omni," reflecting its design as a multimodal model rather than a text-only system.

The standard GPT-4o API model accepts text and image inputs and produces text. It supports dialogue, document analysis, visual question answering, coding assistance, Structured Outputs, function calling, streaming, and fine-tuning. OpenAI documents a 128,000-token context window, a 16,384-token maximum output, and an October 2023 knowledge cutoff.

GPT-4o should not be confused with specialized models such as GPT-4o Transcribe, GPT-4o mini TTS, or realtime GPT-4o variants. Those models use different modality combinations, endpoints, context limits, and pricing. The separate chatgpt-4o-latest API alias has been deprecated and removed, while the standard GPT-4o model page remains available.

What Are GPT-4o’s Key Specifications and Pricing?

As per the Gate.AI model-card, GPT-4o is listed under the model ID openai/gpt-4o.

OpenAI’s currently documented prices match the Gate.AI listing supplied for this article.

For example, a request using 100,000 uncached input tokens and generating 10,000 output tokens would cost approximately:

(100,000 ÷ 1,000,000 × $2.50) + (10,000 ÷ 1,000,000 × $10) = $0.35

This is a calculated estimate before retries, additional requests, platform terms, or account-specific charges.

What Can GPT-4o Do That Makes It Useful in Production?

GPT-4o can combine written instructions with image inputs. A support system could submit a screenshot and a user’s description of an error, then ask the model to identify visible interface elements and propose troubleshooting steps. Visual interpretations still require validation because the model may overlook details or infer information not shown.

Its 128K context window can accommodate substantial reports, code files, conversation histories, or retrieved reference material. This supports document summarization, structured extraction, contract comparison, knowledge assistants, and repository-level coding tasks. Very large collections may still require retrieval or chunking rather than placing every document into one prompt.

Function calling and Structured Outputs make GPT-4o useful for workflows that must return predictable fields or request actions from external systems. A model can classify a customer request, populate a defined JSON schema, and ask an application to retrieve an order. These controls improve format consistency but do not guarantee factual accuracy or safe tool execution.

Teams considering more reasoning-intensive workflows may also compare the o3 model’s specifications and API access.

What Are GPT-4o’s Supported Modalities?

The standard GPT-4o model accepts text and images and produces text. Audio and video support found elsewhere in the GPT-4o family should not be attributed to this specific model entry.

OpenAI documents text input and output, image input, and no direct audio or video support for the standard GPT-4o model.

For speech-focused workflows, the GPT-4o Transcribe guide covers a more relevant specialized model.

Where Does GPT-4o Fall Short?

GPT-4o’s October 2023 knowledge cutoff means it cannot independently provide reliable current information. Applications involving recent events, prices, laws, products, or policies need retrieval tools or externally supplied data.

The 128K context window is also smaller than GPT-4.1’s documented 1,047,576-token context window. GPT-4.1 additionally has a 32,768-token maximum output and lower standard token prices of $2 per million input tokens and $8 per million output tokens.

GPT-4o can hallucinate facts, misread images, produce insecure code, or generate outputs that appear more certain than the evidence supports. Human review, access controls, schema validation, retrieval grounding, and output testing remain necessary for consequential applications.

It also does not directly provide speech, transcription, image generation, or video generation through the standard model entry. Those workflows require specialized models and may increase implementation complexity.

What Is GPT-4o Best Used For?

GPT-4o is best suited to established text-and-image systems that value predictable compatibility with an existing integration. Strong fits include screenshot-based support, document extraction, multilingual assistance, coding tools, structured classification, and function-calling workflows.

Choose GPT-4o when:

• an existing product already depends on GPT-4o behavior;
• text and image understanding are both required;
• 128K context is sufficient;
• migration stability matters more than using the newest model.

Consider another model when cost, very long context, advanced reasoning, speech, or media generation is the primary requirement. High-volume focused tasks may be better suited to the lower-cost GPT-4o mini.

How Does GPT-4o Compare to GPT-4o Mini and GPT-4.1?

GPT-4o mini and GPT-4.1 are practical comparisons because all three accept text and image inputs and return text.

GPT-4o mini offers the lowest listed cost but targets smaller, focused workloads. GPT-4.1 provides approximately eight times GPT-4o’s context capacity, twice its maximum output, and lower standard token prices.

GPT-4o therefore makes the clearest sense when compatibility with an established deployment matters. GPT-4o mini is the cost-first choice, while GPT-4.1 is the stronger documented option for long inputs and more demanding instruction-following workflows.

How Do I Access GPT-4o Through Gate.AI?

Gate.AI documents an OpenAI-compatible API using the base URL https://api.gate.ai/openai/v1, bearer-token authentication, and the /chat/completions endpoint. The Gate.AI documentation specifically warns that https://api.gate.ai/v1 is incorrect because it omits the /openai path.

Set the API key as an environment variable:

export GATEAI_API_KEY="your_api_key"

Python

import os
from openai import OpenAI
api_key = os.getenv("GATEAI_API_KEY")
if not api_key:
    raise RuntimeError("Set GATEAI_API_KEY before running.")
client = OpenAI(
    api_key=api_key,
    base_url="https://api.gate.ai/openai/v1",
)
try:
    response = client.chat.completions.create(
        model="openai/gpt-4o",
        messages=[
            {"role": "user", "content": "Explain API latency in two sentences."}
        ],
    )
    print(response.choices[0].message.content)
except Exception as exc:
    print(f"Request failed: {exc}")

curl

curl "https://api.gate.ai/openai/v1/chat/completions" \
  -H "Authorization: Bearer $GATEAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/gpt-4o",
    "messages": [
      {
        "role": "user",
        "content": "Explain API latency in two sentences."
      }
    ]
  }'

Before production deployment, confirm availability, balance, rate limits, supported parameters, and response handling in the current Gate.AI documentation and console.

FAQs

What is GPT-4o’s context window?

GPT-4o supports 128,000 context tokens and up to 16,384 output tokens.

Can GPT-4o process images?

Yes. It accepts image input and can describe, interpret, compare, or extract information from images, but its standard output is text.

Does GPT-4o support audio or video?

Not through the standard GPT-4o model entry. Audio, transcription, realtime speech, and video tasks require separate models.

How much does GPT-4o cost through Gate.AI?

As per the Gate.AI model-card, input costs $2.50 per million tokens, cached input costs $1.25 per million tokens, and output costs $10 per million tokens.

Is GPT-4o or GPT-4.1 better for a new project?

GPT-4o may suit compatibility-sensitive systems. GPT-4.1 has a larger context window, a higher output limit, and lower listed token prices, making it a stronger candidate for many new long-context workflows.