OpenAI model
GPT-4o
A mature multimodal OpenAI model for text-and-image applications, structured workflows, vision tasks, and production systems that still depend on the GPT-4o family.
- Context window
- 128K
- tokens
- Max output
- 16.4K
- tokens
- Input
- $2.50
- per 1M tokens
- Cached input
- $1.25
- per 1M tokens
- Output
- $10.00
- per 1M tokens
01 / Overview
What GPT-4o Is
GPT-4o is OpenAI's original "omni" GPT model for general-purpose text and vision workloads, combining text generation, image understanding, structured outputs, and tool-oriented API features in one mature model family.
A Widely Adopted Baseline for Multimodal Applications
GPT-4o became a common production choice because it brought strong general-purpose language capability and image understanding into one API model. It accepts text and image inputs and returns text, making it useful for applications that need to reason over screenshots, document pages, charts, photos, or other visual material together with written instructions.
The current GPT-4o model page lists a 128,000-token context window, a 16,384-token maximum output, and an October 1, 2023 knowledge cutoff. It is a non-reasoning model: there is no configurable reasoning-effort setting.
GPT-4o also supports streaming, function calling, structured outputs, fine-tuning, and predicted outputs. That makes it relevant not only as a chat model, but also as a foundation for older and mature production integrations that rely on deterministic application contracts.
- Text and image input with text output.
- 128K context window and 16K-class maximum output.
- Supports structured outputs and function calling.
- Available through both Chat Completions and Responses API.
- Provider
- OpenAI
- Family
- GPT-4o
- Model ID
- gpt-4o
- Knowledge cutoff
- Oct 1, 2023
- Input modalities
- Text, Image
- Output modality
- Text
- Model type
- Non-reasoning
02 / Vision
GPT-4o for Image and Screenshot Understanding
One of GPT-4o's defining strengths is that visual input is part of the standard model workflow rather than a separate text-only path.
Combine What the User Sees With What the User Says
A vision-capable application often needs more than image captioning. The model may need to inspect a UI screenshot and answer a support question, extract values from a photographed document, interpret a chart, compare visual states, or use an image as evidence while following detailed text instructions.
GPT-4o can receive those images alongside text in the same request. This makes it a practical baseline for multimodal evaluations where you want to measure whether a newer model actually improves visual accuracy enough to justify migration.
The standard gpt-4o model should not be confused with GPT-4o Audio or GPT-4o Realtime variants. OpenAI's current model documentation lists image input for GPT-4o, but not native audio or video input. Applications that require speech or realtime audio need a model designed for those modalities.
- 01
Screenshot analysis
Interpret interfaces, error states, dashboards, forms, and visual product context alongside a user question.
- 02
Document images
Extract or reason over information visible in scans, photographed pages, receipts, forms, or diagrams.
- 03
Charts and graphics
Use visual evidence from charts, plots, diagrams, and presentation material together with textual instructions.
- 04
Multimodal support
Let users show a problem instead of forcing every detail to be described in text.
03 / Use cases
Where GPT-4o Still Fits in Production
GPT-4o is particularly relevant to existing applications that need dependable vision, structured output, function calling, or a stable comparison point before moving to a newer model family.
A Mature Model Can Be Valuable Even When Newer Models Exist
Production model selection is not the same as choosing the newest release. An existing application may already have prompts, schemas, tool contracts, fine-tunes, and regression tests built around GPT-4o behavior.
In those cases, migration should be evidence-driven. A newer model may reduce cost or improve quality, but the gain needs to be measured against changes in output style, schema reliability, latency, tool selection, and visual understanding.
GPT-4o is also useful as a historical baseline. If a product has months of GPT-4o quality metrics, comparing a new candidate against that baseline can make an upgrade decision much more concrete.
- 01
Vision-enabled assistants
Answer questions about screenshots, images, diagrams, and visual documents while preserving conversational context.
- 02
Structured application workflows
Return schema-shaped results or function calls that downstream code can validate and execute.
- 03
Existing GPT-4o production systems
Maintain a known baseline while testing whether a migration actually improves cost, quality, or latency.
- 04
Fine-tuned workloads
Use supported fine-tuning when a mature workflow depends on specialized GPT-4o behavior.
04 / Pricing
GPT-4o Pricing
GPT-4o costs $2.50 per million standard input tokens, $1.25 per million cached input tokens, and $10.00 per million output tokens.
Output Cost Is the Main Economic Pressure
GPT-4o output tokens cost four times as much as standard input tokens. That means verbose generation can become a larger cost driver than many developers expect, especially in chat products, report generation, or workflows that routinely ask for long explanations.
Prompt caching can reduce the price of reused input prefixes. This is useful for stable system prompts, long instructions, repeated document context, and multi-turn conversations where the beginning of the prompt remains unchanged.
When comparing GPT-4o with a newer model, compare whole workload economics rather than sticker price alone. A cheaper model may require more retries; a more expensive model may produce shorter or more reliable outputs. The relevant number is cost per accepted result.
- $2.50 per 1M standard input tokens.
- $1.25 per 1M cached input tokens.
- $10.00 per 1M output tokens.
1M tokens · USD
- Input
- $2.50
- Cached input
- $1.25
- Output
- $10.00
Example: 8K input + 2K output
- Input cost
- $0.0200
- Output cost
- $0.0200
- Estimated total
- $0.0400
05 / Context
GPT-4o Has a 128,000-Token Context Window
GPT-4o supports 128,000 tokens of total context and up to 16,384 output tokens, which is substantial for conventional chat, document, vision, and tool workflows but much smaller than newer million-token model families.
Context Size Is One of the Clearest Migration Differences
A 128K window can hold long conversations, large documents, multiple examples, tool results, and visual-input tokens. For many existing products, that is enough.
The limitation becomes more visible when an application starts feeding entire repositories, very large document collections, or extensive retrieved context into a single request. Newer model families with million-token windows can reduce truncation pressure, but a larger limit does not automatically mean better retrieval or lower cost.
If GPT-4o is already in production, inspect actual context utilization before migrating solely for window size. An application using only 20K tokens per request may gain little from a 1M-token ceiling unless the additional context improves task outcomes.
- Context window: 128,000 tokens.
- Maximum output: 16,384 tokens.
- Suitable for substantial conversations and document workflows.
- Smaller than current million-token OpenAI model families.
Context window
128,000
Max output
16,384
The context window covers tokens used by instructions, user messages, conversation history, image representations, source material, and generated output within a request.
06 / Capabilities
GPT-4o API Capabilities
GPT-4o supports the core integration features required by many production applications: image input, streaming, function calling, structured outputs, fine-tuning, and predicted outputs.
Mature API Features Reduce Migration Urgency for Some Workloads
Function calling lets the model select application-defined actions. Structured outputs help constrain free-form generation into predictable machine-readable shapes. Streaming improves perceived latency in interactive interfaces.
Fine-tuning matters for teams that have invested in specialized behavior rather than prompt-only customization. Predicted outputs can improve supported editing-style workloads when much of the desired response is already known.
GPT-4o is available through Chat Completions and Responses API. That gives existing applications more migration flexibility than models tied to a narrow interface.
- Supported
Text
Accept text input and produce text output.
- Supported
Image input
Analyze screenshots, photos, diagrams, document images, and other supported visuals.
- Supported
Streaming
Receive generated output progressively.
- Supported
Function calling
Generate calls to application-defined functions.
- Supported
Structured outputs
Constrain model responses to a defined machine-readable structure.
- Supported
Fine-tuning
Customize supported GPT-4o behavior for specialized tasks.
- Supported
Predicted outputs
Optimize supported generation where much of the expected output is already known.
07 / Snapshots & migration
GPT-4o Snapshots and Migration Strategy
The gpt-4o alias is part of a model family with dated snapshots, which makes it possible to separate model-version stability from the decision to upgrade to a different model family.
Treat Migration as an Evaluation Problem
OpenAI lists dated GPT-4o snapshots including gpt-4o-2024-08-06 and gpt-4o-2024-11-20. Snapshot IDs can be useful when an application needs predictable behavior across releases.
For teams considering a move away from GPT-4o, the useful comparison is not simply "old versus new." Build an evaluation set from actual production traffic: image-heavy requests, JSON schemas, function calls, long conversations, difficult instructions, and failure cases.
Then compare the candidate model against the GPT-4o baseline. Measure quality, schema validity, escalation rate, latency, token usage, and total cost. This prevents a migration from becoming a blind version upgrade.
- 01
Capture the GPT-4o baseline
Measure quality, cost, latency, context usage, tool behavior, and structured-output reliability on real prompts.
- 02
Test a newer candidate
Run the same evaluation set against the replacement model under equivalent application conditions.
- 03
Inspect regressions
Look for changes in vision accuracy, output format, tool selection, prompt sensitivity, and response length.
- 04
Migrate when the evidence wins
Switch production traffic only when the quality and economics justify the operational change.
08 / Evaluation
GPT-4o Strengths and Limitations
GPT-4o remains a useful benchmark and production model for text-and-image applications, but newer model families can offer larger context windows, newer knowledge, stronger reasoning, or different cost profiles.
Strengths
Mature vision workflow
Text and image input are integrated into a well-established general-purpose model.
Broad API feature support
Streaming, function calling, structured outputs, fine-tuning, and predicted outputs cover many production patterns.
Useful migration baseline
Existing GPT-4o systems can use historical quality and cost data to evaluate newer models objectively.
Snapshot availability
Dated model versions help teams preserve known behavior when repeatability matters.
What to consider
128K context ceiling
The window is much smaller than newer OpenAI models with million-token context capacity.
Older knowledge cutoff
The documented October 2023 cutoff is substantially older than current-generation model families.
No configurable reasoning effort
GPT-4o is a non-reasoning model and does not expose modern reasoning-effort controls.
Standard GPT-4o is not an audio model
Native audio and realtime voice use separate GPT-4o-derived model variants rather than the standard gpt-4o model entry.
Evaluate before changing models
Test GPT-4o against your current alternatives
Run the prompts, screenshots, documents, structured outputs, and tool calls your application actually uses, then compare response quality, token cost, and context usage before migrating.
Start FreeConnect your own OpenAI API key and use GPT-4o as a measurable baseline for existing applications or model migration decisions.
Common Questions
What is GPT-4o?
GPT-4o is an OpenAI general-purpose multimodal model. The standard API model accepts text and image input and generates text output, with support for structured outputs, function calling, streaming, fine-tuning, and predicted outputs.
How much does GPT-4o cost?
GPT-4o costs $2.50 per 1M standard input tokens, $1.25 per 1M cached input tokens, and $10.00 per 1M output tokens.
What is the GPT-4o context window?
GPT-4o has a 128,000-token context window and supports up to 16,384 output tokens.
Does GPT-4o support image input?
Yes. GPT-4o accepts both text and image input, which makes it useful for screenshot analysis, document images, charts, visual support workflows, and other multimodal applications.
Does the standard GPT-4o model support audio?
No. The standard gpt-4o model entry lists text and image input with text output. OpenAI provides separate GPT-4o Audio and GPT-4o Realtime variants for native audio and realtime use cases.
Does GPT-4o support function calling and structured outputs?
Yes. GPT-4o supports both function calling and structured outputs, as well as streaming, fine-tuning, and predicted outputs.
Can GPT-4o be fine-tuned?
Yes. OpenAI lists fine-tuning as a supported GPT-4o feature.
Is GPT-4o a reasoning model?
No. GPT-4o is a non-reasoning model and does not expose adjustable reasoning-effort settings.
Should I migrate from GPT-4o to a newer model?
Migration should be based on your workload. Compare a newer model with GPT-4o using the same vision prompts, tool calls, structured outputs, long-context requests, latency targets, and cost measurements before changing production traffic.
Model information
Last updated
Specifications, pricing, modalities, endpoints, supported features, and snapshot information on this page are based on the official OpenAI GPT-4o model documentation. Provider behavior, availability, pricing, and recommended replacement models may change over time.