OpenAI model
GPT-4o Mini
A small, low-cost multimodal model for focused text-and-image tasks, structured extraction, classification, fine-tuning, and high-volume production workflows.
- Context window
- 128K
- tokens
- Max output
- 16.4K
- tokens
- Input
- $0.15
- per 1M tokens
- Cached input
- $0.075
- per 1M tokens
- Output
- $0.60
- per 1M tokens
01 / Overview
What GPT-4o Mini Is
GPT-4o Mini is OpenAI's small multimodal model for focused workloads where image understanding, structured output, and low per-request cost matter more than maximum general-purpose capability.
A Small Model Built for Repetition
Many AI products do not need a flagship model for every request. A workflow may only need to classify an image, extract fields from a document, generate tags, translate short text, identify user intent, or return a compact JSON object.
GPT-4o Mini is designed for that layer of the application. It accepts text and image input, produces text output, and supports production-oriented features such as function calling, Structured Outputs, streaming, fine-tuning, and predicted outputs.
OpenAI positions the model as fast and affordable for focused tasks. Its 128,000-token context window is smaller than the million-token GPT-4.1 family, but still large enough for substantial conversations, documents, examples, and multimodal context.
- Small multimodal model with text and image input.
- Low token pricing for repeated production calls.
- Supports Structured Outputs and function calling.
- Suitable for fine-tuning and model-distillation workflows.
- Provider
- OpenAI
- Family
- GPT-4o
- Tier
- Mini
- Knowledge cutoff
- Oct 1, 2023
- Input modalities
- Text, Image
- Output modality
- Text
- Model type
- Non-reasoning
02 / Low-cost vision
Low-Cost Image Understanding With GPT-4o Mini
GPT-4o Mini makes image-aware API calls inexpensive enough to consider for workloads that may run thousands or millions of times.
Vision Does Not Always Need the Full GPT-4o Model
A product that analyzes screenshots, receipts, forms, catalog images, charts, or document pages may not require the capability level of full GPT-4o for every request.
GPT-4o Mini can act as the first candidate for focused visual tasks: extract a few fields, classify an image, detect a known state in a screenshot, identify visible attributes, or return a structured summary of a document page.
The economic difference is substantial. Standard GPT-4o input pricing is much higher than GPT-4o Mini, so even modest image-processing volume can make model selection consequential.
The correct choice still depends on accuracy. Small text, unusual layouts, difficult charts, ambiguous visual evidence, or complex reasoning over multiple images may justify a stronger model. The useful production question is therefore not "can Mini see images?" but "does Mini meet the acceptance threshold for this exact visual task?"
- 01
Screenshot classification
Identify known UI states, visible errors, categories, or interface conditions from application screenshots.
- 02
Document extraction
Extract selected fields, labels, entities, or short structured records from scanned and photographed pages.
- 03
Image tagging
Generate categories, attributes, keywords, or metadata for product and content images.
- 04
Visual routing
Use a low-cost first pass to decide whether an image needs a more capable model or specialized workflow.
03 / Use cases
Where GPT-4o Mini Fits Best
GPT-4o Mini is strongest when a task is focused enough for a small model and frequent enough that latency and unit cost compound across the product.
Chain and Parallelize Without Paying Flagship Prices Every Time
OpenAI highlighted chained and parallel model calls as a core GPT-4o Mini use case at launch. That matters because many production AI systems are not a single prompt followed by a single response.
One user action may trigger intent detection, extraction, safety or policy classification, metadata generation, a tool call, and a final answer. Running every stage on a larger model can make the workflow expensive even when most steps are straightforward.
GPT-4o Mini can handle inexpensive intermediate stages while the application reserves a stronger model for cases that fail validation or require more difficult reasoning.
Its low cost also makes it practical to run several independent checks in parallel—for example, classify intent, extract entities, and generate tags from the same input—when that architecture produces a better system than one oversized prompt.
- 01
Classification and routing
Detect intent, category, priority, language, or workflow destination before a more expensive step runs.
- 02
Structured extraction
Turn text or image-aware inputs into compact schema-constrained data for downstream systems.
- 03
Parallel enrichment
Run several inexpensive analyses independently instead of forcing unrelated tasks into one large prompt.
- 04
Customer-facing microtasks
Power translation, tagging, rewriting, short support responses, and other latency-sensitive operations.
04 / Pricing
GPT-4o Mini Pricing
GPT-4o Mini costs $0.15 per million standard input tokens, $0.075 per million cached input tokens, and $0.60 per million output tokens.
Small Prices Become Important When the Workflow Has Many Calls
A single GPT-4o Mini request can cost a tiny fraction of a cent. The real value appears when an application performs repeated calls for every user action or processes large datasets in the background.
Prompt caching can reduce repeated input cost further when requests share stable prefixes such as system instructions, schema definitions, examples, or recurring context.
Output still costs four times as much as standard input on a per-token basis. That favors tasks where the model returns compact results: classifications, tags, short translations, extracted fields, tool arguments, or concise structured objects.
- $0.15 per 1M standard input tokens.
- $0.075 per 1M cached input tokens.
- $0.60 per 1M output tokens.
- Especially economical for compact, repeated outputs.
1M tokens · USD
- Input
- $0.15
- Cached input
- $0.075
- Output
- $0.60
Example: 12K input + 2K output
- Input cost
- $0.0018
- Output cost
- $0.0012
- Estimated total
- $0.0030
05 / Context
GPT-4o Mini Has a 128K Context Window
GPT-4o Mini supports a 128,000-token context window and up to 16,384 output tokens, providing substantial capacity for a small model without moving into million-token context territory.
Enough Context for Many Focused Production Tasks
A 128K window can hold long conversations, significant document content, examples, retrieved passages, tool results, and image representations.
For classification or extraction, that capacity can be more than enough. A support assistant can receive substantial history. A document workflow can include many pages of text. An image task can combine visual input with detailed instructions and reference material.
The limitation becomes relevant when the application routinely sends entire repositories, very large document collections, or extremely long histories. GPT-4.1 Mini and newer model families provide substantially larger context windows, so a context-heavy workload should be evaluated separately from a typical focused task.
- Context window: 128,000 tokens.
- Maximum output: 16,384 tokens.
- Suitable for many document, chat, extraction, and multimodal workflows.
- Smaller context capacity than GPT-4.1 Mini and newer million-token models.
Context window
128,000
Max output
16,384
Context can include instructions, conversation history, text, image representations, retrieved content, tool results, examples, and generated output.
06 / Fine-tuning
Fine-Tuning and Distillation With GPT-4o Mini
GPT-4o Mini is not only a low-cost inference model; OpenAI also positions it as a practical model for fine-tuning and for distilling behavior from larger models into a cheaper production tier.
Move Repeated Expertise Into the Smaller Model
Prompt engineering is often the first customization step, but some workloads are repetitive enough that specialization becomes attractive.
Fine-tuning can help when the task has stable examples, consistent output expectations, or domain-specific patterns that are difficult to encode efficiently in every prompt. GPT-4o Mini supports OpenAI fine-tuning workflows for these cases.
OpenAI's model documentation also describes a distillation pattern: outputs from a larger model such as GPT-4o can be used to train GPT-4o Mini toward similar behavior at lower cost and latency.
The goal is not to make Mini universally equivalent to a larger model. The useful question is narrower: can a specialized small model reproduce the behavior required by one production task reliably enough to reduce inference cost?
- 01
Establish a baseline
Measure the untuned GPT-4o Mini on a representative evaluation dataset.
- 02
Collect high-quality examples
Use reviewed production examples or outputs from a stronger model where appropriate.
- 03
Fine-tune the focused task
Specialize the model around stable instructions, formats, or domain patterns rather than broad general intelligence.
- 04
Re-run the evaluation
Compare quality, latency, token usage, and total cost against both the base Mini model and the larger reference model.
07 / Capabilities
GPT-4o Mini API Capabilities
GPT-4o Mini supports the API features required for many structured and tool-connected applications, including image input, function calling, Structured Outputs, streaming, fine-tuning, and predicted outputs.
Structured Output Makes a Small Model More Useful in Software
Free-form prose is not always the desired result. Extraction pipelines, automation, form processing, classification systems, and tool-connected products often need data that downstream code can validate.
Structured Outputs can constrain GPT-4o Mini to developer-defined response structures. Function calling can turn model decisions into application actions. Streaming supports responsive interfaces, while predicted outputs can optimize supported transformations where much of the desired response is already known.
The model also has a versioned snapshot, gpt-4o-mini-2024-07-18, for applications that need a fixed model revision rather than the general alias.
- Supported
Text
Accept text input and generate text output.
- Supported
Image input
Analyze screenshots, documents, photos, charts, and other supported visual inputs.
- Supported
Streaming
Receive generated output progressively.
- Supported
Function calling
Generate calls to application-defined functions.
- Supported
Structured outputs
Return schema-constrained machine-readable responses.
- Supported
Fine-tuning
Customize supported behavior for focused production workloads.
- Supported
Predicted outputs
Optimize supported generation when much of the desired output is already known.
08 / Evaluation
GPT-4o Mini Strengths and Limitations
GPT-4o Mini is best evaluated as a small multimodal production model: inexpensive enough for high-volume use and capable enough for many focused tasks, but not a replacement for larger models on every difficult workload.
Strengths
Very low multimodal cost
Low token pricing makes text-and-image processing practical at volumes where full GPT-4o can become expensive.
Structured application support
Function calling and Structured Outputs make the model useful for extraction, automation, and downstream software integration.
Fine-tuning and distillation
Focused workloads can be specialized, including workflows that transfer behavior from a stronger model into a cheaper inference tier.
128K context
The context window is large enough for many conversations, documents, examples, and multimodal prompts.
What to consider
Lower capability ceiling than GPT-4o
Difficult visual reasoning, complex instructions, coding, or ambiguous tasks may benefit from a stronger model.
Smaller context than GPT-4.1 Mini
128K is substantial, but it is far below the one-million-token context available in the GPT-4.1 family.
No adjustable reasoning effort
GPT-4o Mini is a non-reasoning model and does not expose configurable reasoning levels.
No native audio or video
The standard model accepts text and image input and produces text output; native audio and video are not supported.
Find the smallest model that works
Test GPT-4o Mini on real text and image workloads
Compare extraction accuracy, image understanding, schema validity, latency, token usage, and total cost using the same inputs your application will process in production.
Start FreeConnect your own OpenAI API key and evaluate whether GPT-4o Mini meets the quality threshold before paying for a larger model.
Common Questions
What is GPT-4o Mini?
GPT-4o Mini is a fast, affordable small OpenAI model for focused tasks. It accepts text and image input and generates text output, including Structured Outputs.
How much does GPT-4o Mini cost?
GPT-4o Mini costs $0.15 per 1M standard input tokens, $0.075 per 1M cached input tokens, and $0.60 per 1M output tokens.
What is the GPT-4o Mini context window?
GPT-4o Mini has a 128,000-token context window and supports up to 16,384 output tokens.
Does GPT-4o Mini support image input?
Yes. GPT-4o Mini accepts both text and image input and produces text output, making it suitable for low-cost screenshot, document-image, classification, and extraction workflows.
Does GPT-4o Mini support Structured Outputs?
Yes. GPT-4o Mini supports Structured Outputs as well as function calling, streaming, fine-tuning, and predicted outputs.
Can GPT-4o Mini be fine-tuned?
Yes. OpenAI lists GPT-4o Mini as supporting fine-tuning and describes it as a strong candidate for focused specialization.
Can GPT-4o Mini be used for model distillation?
Yes. OpenAI's model documentation notes that outputs from a larger model such as GPT-4o can be distilled to GPT-4o Mini to target similar task behavior at lower cost and latency.
Is GPT-4o Mini a reasoning model?
No. GPT-4o Mini is a non-reasoning model and does not provide adjustable reasoning-effort controls.
Should I choose GPT-4o Mini or GPT-4o?
Choose GPT-4o Mini when the workload is focused and high-volume and your evaluation shows that the smaller model meets the required text or vision quality. Use GPT-4o or another stronger model when difficult reasoning, image interpretation, or instruction following produces meaningfully better results.
Should I choose GPT-4o Mini or GPT-4.1 Mini?
GPT-4o Mini is substantially cheaper and is a strong candidate for focused multimodal and structured tasks. GPT-4.1 Mini has a much larger context window and stronger positioning for instruction following and long-context workloads. Test both on the same production examples when those tradeoffs matter.
Does GPT-4o Mini support native audio or video?
No. The standard gpt-4o-mini model supports text and image input with text output. Native audio and video are not supported by this model entry.
Model information
Last updated
Specifications, pricing, modalities, snapshots, and supported features on this page are based on the official OpenAI GPT-4o Mini model documentation and OpenAI launch materials. Provider pricing, access, endpoints, and capabilities may change over time.