GPT-4o Mini Vision
Low-cost, fast model surpassing GPT-3.5 Turbo in textual intelligence and multimodal reasoning.
OpenAI Low-cost vision and text reasoning model
GPT-4o Mini Vision is a multimodal language model developed by OpenAI, released in mid-2024. It is a smaller, more cost-efficient variant of the GPT-4o family, designed to process both text and images within a single context window of 128,000 tokens. The model supports the same range of languages as GPT-4o and is optimized for low latency, making it suitable for high-throughput or real-time applications.
The model is well-suited for tasks that require fast responses at scale, such as customer-facing chat interfaces, document analysis with visual content, and pipelines where cost per token is a primary constraint. Its multimodal reasoning capability allows it to interpret images alongside text in the same request. Developers working with large volumes of context or needing to process mixed text-and-image inputs at reduced cost are the primary intended audience.
What GPT-4o Mini Vision supports
Image Understanding
Accepts image inputs alongside text in a single request, enabling the model to describe, analyze, or answer questions about visual content.
Large Context Window
Supports up to 128,000 tokens of context per request, allowing long documents, conversation histories, or multiple images to be passed in one call.
Low Latency Responses
Optimized for fast inference, making it suitable for real-time applications such as customer chat interfaces or interactive tools.
Cost-Efficient Inference
Priced significantly lower per token than larger GPT-4o variants, enabling high-volume deployments without proportional cost increases.
Multilingual Text Processing
Supports the same broad set of languages as GPT-4o, covering text generation, comprehension, and reasoning across multiple languages.
Structured Output
Can return responses in structured formats such as JSON, useful for downstream data processing or API integrations.
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Get Started FreeBenchmark scores
Scores represent accuracy — the percentage of questions answered correctly on each test.
| Benchmark | What it tests | Score |
|---|---|---|
| MMLU-Pro | Expert knowledge across 14 academic disciplines | 74.8% |
| GPQA Diamond | PhD-level science questions (biology, physics, chemistry) | 54.3% |
| MATH-500 | Undergraduate and competition-level math problems | 75.9% |
| AIME 2024 | American math olympiad problems | 15.0% |
| LiveCodeBench | Real-world coding tasks from recent competitions | 30.9% |
| HLE | Questions that challenge frontier models across many domains | 3.3% |
| SciCode | Scientific research coding and numerical methods | 33.3% |
Common questions about GPT-4o Mini Vision
What is the context window size for GPT-4o Mini Vision?
GPT-4o Mini Vision supports a context window of 128,000 tokens, allowing large amounts of text and image content to be included in a single request.
What is the knowledge cutoff date for this model?
The training data cutoff for GPT-4o Mini Vision is October 2024, meaning it does not have knowledge of events that occurred after that date.
Does this model support image inputs?
Yes, GPT-4o Mini Vision is a multimodal model that accepts both text and image inputs within the same request, enabling visual question answering and image-based reasoning.
How does the pricing of GPT-4o Mini compare to other OpenAI models?
GPT-4o Mini is positioned as a low-cost model in OpenAI's lineup. For exact current pricing, refer to the OpenAI pricing page at platform.openai.com/docs/models.
What languages does GPT-4o Mini Vision support?
GPT-4o Mini Vision supports the same range of languages as GPT-4o, making it suitable for multilingual applications.
Documentation & links
Parameters & options
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