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OpenAI: GPT-5 Image Mini
OpenAI
•
GPT-5 mini (High)
•
Released 
October 2025

OpenAI
GPT-5 mini (High)

Combines GPT-5 Mini text understanding with GPT Image 1 Mini generation for cost-efficient image creation and editing at scale.

Modality:
Text
Image
PDF
model ID
openai/gpt-5-image-mini
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Output Speed *

0.00
tok/s

Intelligence Index *

16.8
/ 100

Context Window *

400000
tokens

Input price

15
Anytoken

Output price

12
Anytoken
GPT-5 Image Mini: Efficient Image Generation and Editing in One Multimodal API Call GPT-5 Image Mini is OpenAI's cost-efficient multimodal model that pairs GPT-5 Mini's language understanding with GPT Image 1 Mini generation in a single endpoint. It sits below the full GPT-5 Image model in OpenAI's lineup, trading peak fidelity for lower latency and cost. It accepts text, images and PDFs and returns both images and text, making it a fit for high-volume applications that need image creation, image editing, and text handling together rather than in separate calls. Start building with GPT-5 Image Mini through the AnyAPI.ai API.

Performance

Why GPT-5 Image Mini Fits High-Volume Visual Workflows

GPT-5 Image Mini's strength is combining image generation and editing with text understanding in one request rather than chaining separate models. It accepts text, images and PDFs and returns images and text, and supports up to 16 reference images for editing in a single call. Because it builds on GPT Image 1 Mini, it prioritizes generation speed and cost over maximum fidelity. For applications generating many images per user session, this makes per-request image generation practical at scale instead of reserving it for premium features, at the cost of some fine detail versus the full-size model.

Benchmarks

How GPT-5 Image Mini's Generation Quality Is Positioned

OpenAI positions GPT Image 1 Mini, the generation component, as roughly 80% less expensive than the full-size image model while handling the same range of subjects and styles. Independent commentary notes the tradeoff explicitly: the mini variant sacrifices some fine detail and photorealism for lower cost and latency, and some developers report noticeably lower fidelity than earlier dedicated models like DALL-E 3. There is no widely published independent quality benchmark isolating GPT-5 Image Mini specifically, so treat its quality as suitable for thumbnails, illustrations, prototyping and iteration rather than premium hero assets.

Output Speed

*
0.00
tok/s

Intelligence Index

*
16.8
/ 100

MMLU *

Broad world knowledge and problem-solving
84
%

GPQA *

PhD-level scientific reasoning across physics, biology, chemistry.
83
%

HLE *

Adherence to multi-step structured instructions.
22
%

LiveCodeBench *

Tool-calling reliability in long agentic loops.
84
%

Technical Specifications

What the model supports

GPT-5 Image Mini provides a 400,000-token context window with up to 128,000 completion tokens. Unlike most GPT-5 family text models, it returns both images and text, and accepts text, image and PDF input. It supports structured outputs via JSON schema in response_format. Notably, the hosted endpoint does not accept tools, so function calling is unavailable, which matters if you were planning to drive it inside a tool-calling agent loop. Image editing supports up to 16 reference images per request, useful for consistency-driven edits.
Verified Specifications — 
GPT-5 mini (High)
*
Input modalities
Text
Image
PDF
output modalities
Text
Image
Context window
400000
 tokens
Maximum output tokens
128000
Reasoning
Yes
Knowledge cutoff
October 2025
Pricing (standard)
15
 AnyTokens in
 / 
12
 AnyTokens out

Comparison

GPT-5 Image Mini vs GPT-5 Image: Which Tier Do You Need?

GPT-5 Image Mini and GPT-5 Image are the two tiers of OpenAI's combined language-plus-image-generation model. Both expose a 400,000-token context window, up to 128,000 output tokens, and both return images and text. The difference is the underlying stack: Mini pairs GPT-5 Mini with GPT Image 1 Mini for efficiency, while GPT-5 Image pairs the full GPT-5 with the full GPT Image 1 for higher reasoning and image fidelity. The practical decision is whether your visual workload needs peak quality or high-volume economics.

Dimension
GPT-5 mini (High)
GPT-5 Image
Context window *
400000
tokens
400000
tokens
Output speed *
0.00
tok/s
N/A
tok/s
Intelligence Index *
16.8
N/A
Input pricing
15
AnyToken
60
AnyToken
Output pricing
12
AnyToken
60
AnyToken
Knowledge cutoff *
October 2025
August 2025

Choose GPT-5 Image Mini when you generate many images per session, run rapid iteration and prototyping, or need cost-efficient per-request image generation where some fidelity tradeoff is acceptable. Choose GPT-5 Image when image quality, detailed rendering, and the stronger reasoning of full GPT-5 matter more than cost — for example premium hero assets or complex multi-step visual reasoning. GPT-5 Image carries materially higher input and output pricing, so reserve it for the requests that genuinely benefit from the higher tier.

Limitations & Trade-offs

Where GPT-5 mini (High) falls short

1
No tool calling on the endpoint. The hosted GPT-5 Image Mini endpoint does not accept tools, so function calling is unavailable. This matters if you intended to run the model inside an agent loop that calls external functions or APIs between image steps. Teams needing tool-driven orchestration should place a tool-capable text model (such as GPT-5 Mini) in the agent loop and call GPT-5 Image Mini only for the generation step.
2
Quality tradeoff versus larger image models. Because generation is powered by GPT Image 1 Mini, the model prioritizes speed and cost over maximum fidelity and photorealism. Independent reports note noticeably lower fine detail than the full-size model and, in some cases, than earlier dedicated models. For premium hero imagery, brand-critical assets, or highly detailed photorealistic output, the full GPT-5 Image or a dedicated high-quality image model is preferable.
3
No verified reasoning mode. Unlike GPT-5 family thinking models, GPT-5 Image Mini does not expose a verified extended-reasoning configuration or reasoning-effort controls on this endpoint. Workloads that depend on deep step-by-step reasoning before generation should route the reasoning stage to a thinking-capable model and use GPT-5 Image Mini for the visual output only.
4
Generation latency is parameter-sensitive. Image generation time depends heavily on quality setting, image size, and number of images per request. Developers moving from older image endpoints have reported meaningfully higher latency at high quality settings. For latency-sensitive production, keep quality lower, generate one image per call where possible, and avoid stacking multiple edit passes, since each edit is a separate generation step.

Best-Fit Workloads

Where this model earns its place

01

High-volume image generation

‍
For applications producing many images per user session — content illustrations, thumbnails, social assets — GPT-5 Image Mini's cost and speed profile makes per-request generation viable at scale rather than a premium-only feature. The generation stack is positioned as roughly 80% cheaper than the full image model, so the economics support embedding generation directly into everyday product flows where moderate quality is sufficient.

02

Image editing with references

‍
The model supports attaching up to 16 reference images in a single request for detailed image editing. This suits consistency-driven workflows — editing product shots, maintaining a subject across variations, or applying instruction-based modifications — where multiple references anchor the output. Combined with strong instruction following inherited from the GPT Image line, it fits interactive editing tools that iterate on user-supplied imagery.

03

Combined text-and-image responses

‍
Because GPT-5 Image Mini returns both images and text and accepts text, image and PDF input, it fits use cases where a single response needs a generated visual plus accompanying explanation or metadata — for example generating an illustration alongside a caption, or producing a visual from a PDF brief. This removes the need to orchestrate a separate image model and text model for each request.

04

Rapid prototyping and iteration

‍
For design exploration and concept iteration where teams generate many candidate images quickly, the mini tier's lower cost per image makes wide exploration affordable. Use lower quality settings for iteration and reserve higher-fidelity models for final production assets. The 400,000-token context also allows detailed briefs and multiple reference images to inform each iteration.

Pricing in anytokens via AnyAPI
Input
15
₳
Output
12
₳
Cache write
—
₳
Cache read
—
₳
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PerformanceBenchmarksTechnical SpecificationsComparisonLimitations & Trade-offsBest-Fit WorkloadsIntegrationFAQ

Integration

Access GPT-5 mini (High) via AnyAPI.ai

Access GPT-5 mini (High) through AnyAPI.ai using a unified API built for multi-model AI applications. Integrate GPT-5 mini (High) without maintaining a separate provider-specific connection, and keep the flexibility to test, switch, or combine models as your application requirements evolve.

01

One API integration

Access GPT-5 mini (High) and other AI models through the same API workflow instead of maintaining separate integrations for every provider.

02

Easy model switching

Test GPT-5 mini (High) against alternative models or switch models as your performance, capability, or cost requirements change without rebuilding your application around another provider API.

03

Flexible for production

Use GPT-5 mini (High) from experimentation through production while keeping your AI stack flexible as workloads, traffic, and model requirements evolve.

04

Multi-model applications

Use GPT-5 mini (High) for the workloads where it performs best and combine it with other models for tasks that require different capabilities, performance, or efficiency.

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Frequently Asked Questions

Answers to common questions about integrating and using this AI model via AnyAPI.ai

GPT-5 Image Mini is an OpenAI multimodal model that combines GPT-5 Mini's language capabilities with GPT Image 1 Mini for efficient image generation. It accepts text, images and PDFs as input and returns both images and text, targeting cost-efficient image creation, editing, and combined text-and-image responses at scale.

GPT-5 Image Mini has a 400,000-token context window and supports up to 128,000 completion tokens. This is the shared budget for text, image and PDF input, allowing detailed briefs and multiple reference images to inform a single generation or edit request.

No. The GPT-5 Image Mini endpoint does not accept tools, so function calling is unavailable. It does support structured outputs via a JSON schema in response_format. If your workflow needs tool-driven orchestration, place a tool-capable model such as GPT-5 Mini in the agent loop and call GPT-5 Image Mini only for generation.

Both share a 400,000-token context and return images and text. GPT-5 Image Mini uses GPT-5 Mini plus GPT Image 1 Mini for lower cost and latency, while GPT-5 Image uses full GPT-5 and GPT Image 1 for higher fidelity and reasoning. Choose Mini for high-volume, cost-sensitive generation and the full model for premium quality.

GPT-5 Image Mini fits high-volume image generation, image editing with up to 16 reference images, combined text-and-image responses, and rapid prototyping. It suits thumbnails, illustrations, and iterative creative tools where cost and speed outweigh maximum fidelity. For premium or highly photorealistic assets, a higher-quality image model is preferable.

* Benchmark data source: Artificial Analysis artificialanalysis.ai