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For image-only workloads, use openai/gpt-image-1-mini to call OpenAI's Images API directly without LLM overhead.

Favicon for openai

OpenAI: GPT-5 Image Mini

openai/gpt-5-image-mini

BenchmarksCompare

GPT-5 Image Mini combines OpenAI's advanced language capabilities, powered by GPT-5 MiniOpens in new tab, with GPT Image 1 Mini for efficient image generation. This natively multimodal model features superior instruction following, text rendering, and detailed image editing with reduced latency and cost. It excels at high-quality visual creation while maintaining strong text understanding, making it ideal for applications that require both efficient image generation and text processing at scale.

Modalities
In / out price
$2.50 / $8per 1M
Context
400K
Released
Oct 16, 2025
BenchmarksCompare
PlaygroundBenchmarksProvidersPricingPerformanceUptimeAppsActivityDocumentationFAQExplore

Playground

Benchmarks

Scores on standardized evaluations and this model’s own outputs on shared benchmark prompts. Higher percentages are better — and each rank shows where this model lands among the models evaluated on that source.

Benchmark score summary for OpenAI: GPT-5 Image Mini (Artificial Analysis and Design Arena)
SourceBenchmarkScore
Artificial AnalysisGPT Image 1 Mini (medium) Elo934
Design ArenaGPT-Image-1 Mini Models Arena Graphic Design Elo1165
Design ArenaGPT-Image-1 Mini Models Arena Image Elo1181
Design ArenaGPT-Image-1 Mini Models Arena Logo Elo1209

Benchmark outputs summary for OpenAI: GPT-5 Image Mini

  • Full wine glass: A wine glass filled to the brim with white wine, the surface level with the rim of the glass, standing upright on a plain wooden table. Straight-on eye-level shot, glass upright and not tilted, no other objects in the frame. (Tied 10th of 60 on this prompt)
  • Closed umbrellas: Wide street-level photograph of about twelve people crossing a city street in heavy rain. Every person is carrying a closed, furled umbrella held down at their side. Not a single umbrella is open. Each umbrella is clearly visible. They are all getting soaked. No other people in the frame. (Tied 17th of 60 on this prompt)
  • Three fingers: Close straight-on photograph of an open right hand raised palm towards the camera, with exactly three fingers extended straight up and the other two folded down into the palm. Plain background, nothing else in the frame. (Tied 33rd of 60 on this prompt)
  • Cards & dice: Exactly four playing cards face up in a single straight row on a polished marble counter, evenly spaced, all the same size, showing the seven of hearts, the eight of spades, the nine of diamonds and the ten of clubs from left to right. No other objects in the frame. Straight-on eye-level shot, soft even light. (Tied 17th of 60 on this prompt)
  • Long exact string: A white A4 poster on a brick wall reading exactly "SPRING CLEARANCE - EVERYTHING 40% OFF - SATURDAY 14 JUNE, 9AM TO 6PM". No other text in the frame. (Tied 29th of 60 on this prompt)
  • Multiple languages: Straight-on photograph of a white overhead directional sign in an international airport terminal, with a black arrow pointing right at the left end. The sign carries the same word in four scripts on four stacked lines, in this order from top to bottom: "DEPARTURES", then the Japanese 出発, then the Korean 출발, then the Arabic المغادرة. Clean sans-serif lettering, sharp focus, no other text anywhere in the frame. (Tied 1st of 60 on this prompt)
  • Occlusion: Straight-on eye-level photograph of a plain wooden desk. On the left, a closed hardcover book with a dark red cover stands upright, its front cover facing the camera. A blue ceramic mug stands on the desk to the right of the book and clearly further back, about 20 cm behind it, positioned so that the right edge of the book overlaps and hides the left third of the mug. The mug's handle points to the right and the whole rim of the mug is visible. Bare desk surface is visible between the book and the mug. Nothing else on the desk. Plain wall behind, soft even light. (Tied 39th of 60 on this prompt)
  • Mirror: Straight-on eye-level photograph of a bathroom vanity. A large rectangular mirror covers the wall behind the counter and its bottom edge meets the countertop, so both the objects and their reflections are visible. On the counter, a tall red toothbrush cup on the left and a tall green glass bottle on the right, both roughly 20 cm tall and well apart from each other. The mirror reflects the counter and the objects on it accurately. Nothing else in the frame. (Tied 42nd of 60 on this prompt)
  • Plain zebra: Photograph of a zebra standing side-on in short grass. Its coat is a uniform solid grey with no stripes, no bands and no markings of any kind anywhere on its body. The whole animal is visible. Sharp focus. (Tied 19th of 60 on this prompt)
  • Times Square: Wide street-level photograph of Times Square in New York at night, wet asphalt reflecting the lights. Every billboard, screen, storefront window, awning, taxi door and road marking is completely blank — lit with plain colour fields and soft gradients only. No letters, no words, no numbers, no logos, no lettering of any kind anywhere in the frame. Sharp focus, straight-on shot. (Tied 1st of 60 on this prompt)
  • Minimal diff: From the reference photo, change only the sign text to "ROUTE 7 ESPRESSO - OPEN TILL 8". Keep the sign, wall, lighting and framing identical. (Tied 5th of 60 on this prompt)
  • Object removal: Remove only the second cup from the left in the reference photo. Leave the other four cups exactly as they are, in the same positions, and leave the shelf, towel, wall, plant and lighting untouched. (Tied 45th of 60 on this prompt)
  • Person removal: In the reference photo, remove only the person in the yellow coat. Their shadow on the pavement and their reflection in the shop window must be removed as well. Reconstruct the pavement and the window glass behind them. The person in the red coat and the person in the green coat must stay completely unchanged, including their own shadows and their own reflections. Change nothing else in the frame. (Tied 31st of 60 on this prompt)
  • Product placement: Product photograph of the ceramic mug from the reference image, standing upright on a café table. Show the same side of the mug, from the same side-on eye-level angle as the reference, with the handle on the right. The shape, proportions, colours, moulded relief and every painted detail must match the reference exactly. Only the setting and lighting change: soft daylight in a café interior, shallow depth of field. (Tied 36th of 60 on this prompt)
  • Four references: Place the person from the portrait reference at the desk in the scene reference, wearing the rust orange canvas jacket from its reference and holding the navy blue travel mug from its reference in one hand. Keep the face, the jacket and the mug exactly as they appear in the references — same colours, same details, same proportions. Do not change the room. (Tied 45th of 60 on this prompt)
  • Portraits: Studio headshot of a woman in a charcoal blazer against a mid-grey seamless backdrop, lit by a large softbox slightly above and to the left with a soft fill from the right, catchlights in both eyes. An 85mm look with shallow depth of field, skin texture and flyaway hairs kept rather than smoothed.
  • People in scenes: Photograph of four cooks working the pass in a restaurant kitchen at service, plating three dishes at once, several hands in frame and some of them overlapping. One cook partly hidden behind another, steam rising into hard overhead light, each face clearly a different person.
  • Animals: Photograph of a tabby cat asleep on a sunlit windowsill, fur lit from behind so the individual hairs glow at the edges, one ear turned toward the camera. Shallow depth of field, a houseplant soft in the background, quiet morning light.
  • Landscape and nature: Photograph of an old-growth temperate rainforest after rain, moss on every trunk and fallen log, ferns dense across the floor, thin fog between the far trees. Overcast light with no direct sun, everything from the nearest fern to the last trunk in focus.

Providers

This model is hosted by one provider. OpenRouter forwards every request to it directly — no routing decisions to make.

Pricing

The average price customers actually pay for this model, next to the prices providers post. Caching and discounts mean the price actually paid is often well below the listed one.

Performance

Throughput is how fast the model writes (tokens per second — higher is better). Latency is total round-trip time (lower is better). TTFT is time-to-first-token — how long before you see anything appear (lower is better).

Uptime

Uptime is the percentage of the past 3 days that at least one provider was responding to requests. Availability is the percentage of time that inference was successfully served. OpenRouter continuously monitors and uses the next-best provider when one returns an error.

Apps

Public apps that send the most traffic to this model. Good signal for what real production workloads look like — and a hint at which use cases this model is best suited for.

Activity

Token volume and request traffic to this model over time.

API

Drop-in code to call this model. OpenRouter's API is OpenAI-compatible — most SDKs work by just swapping the base URL. The only thing that changes between models is the model slug below.

Not currently available through EU in-region routing. In-region routing docs

Documentation

Guides, cookbooks, blog posts, and tools for building with OpenAI: GPT-5 Image Mini on OpenRouter.

  • GPT-5 is now live Blog post

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AI Models with Vision: Multimodal LLMs for Image UnderstandingCollectionImage Generation ModelsCollectionAI Model RankingsRankingImage Model RankingsRanking
End-to-end latency
$2.50$8.00$2.00$0.2550.77s
100.00%
ProviderImage input / Image output /M
End-to-end latencyUptime
  • Favicon for OpenAI
    Image input $2.50 / Image output $8.00
    End-to-end latency 50.77sUptime 100.00%
End-to-end latency
50.77s

P50, best provider

Uptime (3d)The model was reachable. Request routed to a provider.
100.00%
Availability (3d)The model returned inference from any provider. Errors and empty responses count against it.
99.73%

Availability over the last 3 days

Last 72 hours
Availability 99.73%
3 Days Ago2 Days AgoYesterdayNow

Availability over the last 24 hours

OpenRouter Availability
99.71%

When an error occurs in an upstream provider, we can recover by routing to another healthy provider, if your request filters allow it. You can access per-provider uptime data programmatically through the Endpoints API. Learn more about our load balancing and provider routing options.

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HighLevel
new
284Mtokens
2.
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Your site name
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109Mtokens
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48.9Mtokens
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Frequently asked questions

GPT-5 Image Mini combines OpenAI's advanced language capabilities, powered by GPT-5 Mini, with GPT Image 1 Mini for efficient image generation. This natively multimodal model features superior instruction following, text rendering, and detailed image editing with reduced latency and cost.

The GPT-5 Image Mini documentation on OpenRouter includes the GPT-5 is now live blog post. The quickstart covers API keys and your first request, and the API reference lists every parameter.

GPT-5 Image Mini costs $2.50/M input tokens and $2.00/M output tokens, with separate rates for Cache Read at $0.25/M tokens, Image Output at $8.00/M tokens, and Web Search at $10.00/1K calls.

GPT-5 Image Mini has a 400,000 token context window. It supports up to 128,000 completion tokens.

No. GPT-5 Image Mini does not accept tools, so function calling is unavailable. It also supports structured outputs via a JSON schema in response_format.

GPT-5 Image Mini accepts files such as PDFs, images, and text as input and returns images and text. Up to 16 reference images can be attached to one request for image editing.

GPT-6.1 Terra Pro, GPT-6.1 Terra, GPT-6 Luna Decisions and 58 more are other text models from OpenAI.

GPT-5 Image Mini was released on October 16, 2025.

More models from OpenAI

GPT-6.1 Terra Pro

GPT-6.1 Terra Pro is the same underlying model as GPT-6.1 Terra, served with reasoning.mode set to pro for higher-quality responses on complex tasks.

Cost note: pro mode spends far more reasoning tokens per request, so a typical request costs several times more than the same request on GPT-6.1 Terra and takes much longer to complete. It is intended for hard, high-stakes problems where the extra accuracy justifies the cost. For everyday coding, agentic, and chat workloads, use GPT-6.1 Terra instead.

Learn more in OpenAI's docs: https://developers.openai.com/api/docs/guides/reasoning#reasoning-mode

Text1.1M context$1 / $5
GPT-6.1 Terra Pro (batch)

GPT-6.1 Terra Pro is the same underlying model as GPT-6.1 Terra, served with reasoning.mode set to pro for higher-quality responses on complex tasks.

Cost note: pro mode spends far more reasoning tokens per request, so a typical request costs several times more than the same request on GPT-6.1 Terra and takes much longer to complete. It is intended for hard, high-stakes problems where the extra accuracy justifies the cost. For everyday coding, agentic, and chat workloads, use GPT-6.1 Terra instead.

Learn more in OpenAI's docs: https://developers.openai.com/api/docs/guides/reasoning#reasoning-mode

Text1.1M context$0.50 / $2.50
GPT-6.1 Terra

GPT-6.1 Terra is the mid-tier model in OpenAI's GPT-6.1 series, priced below GPT-6.1 Sol. It is suited for everyday agentic, coding, and writing work, is particularly strong at understanding screenshots and on-screen content, and is efficient enough for high-volume workloads.

Text1.1M context$1 / $5
GPT-6.1 Terra (batch)

GPT-6.1 Terra is the mid-tier model in OpenAI's GPT-6.1 series, priced below GPT-6.1 Sol. It is suited for everyday agentic, coding, and writing work, is particularly strong at understanding screenshots and on-screen content, and is efficient enough for high-volume workloads.

Text1.1M context$0.50 / $2.50
GPT-6 Luna Decisions

GPT-6 Luna Decisions is GPT-6 Luna served through OpenAI's Decisions API. Instead of generating text, it reads the content passed as state (text, JSON, or images) and returns typed, probabilistic answers to named questions in a single request: the probability of yes for a yes/no question (noul), a probability for every option plus the most likely one (choice), or a probability for every level of an ordered rubric plus the expected score (score).

It is built for classification, routing, moderation, and rubric grading where application code thresholds the returned numbers rather than parsing a chat reply. A request can carry up to 200 questions about the same content.

Decisions$0.10 / $0
GPT-6.1 Sol Pro

GPT-6.1 Sol Pro is the same underlying model as GPT-6.1 Sol, served with reasoning.mode set to pro for higher-quality responses on complex tasks.

Cost note: pro mode spends far more reasoning tokens per request, so a typical request costs several times more than the same request on GPT-6.1 Sol and takes much longer to complete. It is intended for hard, high-stakes problems where the extra accuracy justifies the cost. For everyday coding, agentic, and chat workloads, use GPT-6.1 Sol instead.

Learn more in OpenAI's docs: https://developers.openai.com/api/docs/guides/reasoning#reasoning-mode

Text1.1M context$2 / $10
GPT-6.1 Sol Pro (batch)

GPT-6.1 Sol Pro is the same underlying model as GPT-6.1 Sol, served with reasoning.mode set to pro for higher-quality responses on complex tasks.

Cost note: pro mode spends far more reasoning tokens per request, so a typical request costs several times more than the same request on GPT-6.1 Sol and takes much longer to complete. It is intended for hard, high-stakes problems where the extra accuracy justifies the cost. For everyday coding, agentic, and chat workloads, use GPT-6.1 Sol instead.

Learn more in OpenAI's docs: https://developers.openai.com/api/docs/guides/reasoning#reasoning-mode

Text1.1M context$1 / $5
GPT-6.1 Sol

GPT-6.1 Sol is an upgrade to GPT-6 Sol from OpenAI, positioned below the flagship GPT-6 Astra in the GPT-6 series. It is suited for agentic coding, computer use, document-heavy professional work, and multi-step business workflow automation, and approaches Astra-level results on these tasks at a much lower cost. Compared with GPT-6 Sol, it makes fewer factual errors and is more reliable at respecting explicit restrictions and user intent during agentic tasks.

Text1.1M context$2 / $10
GPT-6.1 Sol (batch)

GPT-6.1 Sol is an upgrade to GPT-6 Sol from OpenAI, positioned below the flagship GPT-6 Astra in the GPT-6 series. It is suited for agentic coding, computer use, document-heavy professional work, and multi-step business workflow automation, and approaches Astra-level results on these tasks at a much lower cost. Compared with GPT-6 Sol, it makes fewer factual errors and is more reliable at respecting explicit restrictions and user intent during agentic tasks.

Text1.1M context$1 / $5
GPT-6 Luna Pro

GPT-6 Luna Pro is the same underlying model as GPT-6 Luna, served with reasoning.mode set to pro for higher-quality responses on complex tasks.

Cost note: pro mode spends far more reasoning tokens per request, so a typical request costs several times more than the same request on GPT-6 Luna and takes much longer to complete. It is intended for hard, high-stakes problems where the extra accuracy justifies the cost. For everyday coding, agentic, and chat workloads, use GPT-6 Luna instead.

Learn more in OpenAI's docs: https://developers.openai.com/api/docs/guides/reasoning#reasoning-mode

Text1.1M context$0.10 / $0.50
GPT-6 Luna Pro (batch)

GPT-6 Luna Pro is the same underlying model as GPT-6 Luna, served with reasoning.mode set to pro for higher-quality responses on complex tasks.

Cost note: pro mode spends far more reasoning tokens per request, so a typical request costs several times more than the same request on GPT-6 Luna and takes much longer to complete. It is intended for hard, high-stakes problems where the extra accuracy justifies the cost. For everyday coding, agentic, and chat workloads, use GPT-6 Luna instead.

Learn more in OpenAI's docs: https://developers.openai.com/api/docs/guides/reasoning#reasoning-mode

Text1.1M context$0.05 / $0.25
GPT-6 Luna

GPT-6 Luna is the fast, cost-efficient model in OpenAI's GPT-6 series, positioned below GPT-6 Sol. It is suited for high-volume and latency-sensitive workloads such as chat, classification, and lightweight agentic tasks, and at higher reasoning effort it can take on complex software engineering and computer-use tasks that previously called for a Sol-tier model. It shares the GPT-6 family's gains in factual reliability and its clearer, more concise communication style.

Text1.1M context$0.10 / $0.50
GPT-6 Luna (batch)

GPT-6 Luna is the fast, cost-efficient model in OpenAI's GPT-6 series, positioned below GPT-6 Sol. It is suited for high-volume and latency-sensitive workloads such as chat, classification, and lightweight agentic tasks, and at higher reasoning effort it can take on complex software engineering and computer-use tasks that previously called for a Sol-tier model. It shares the GPT-6 family's gains in factual reliability and its clearer, more concise communication style.

Text1.1M context$0.05 / $0.25
GPT-6 Sol Pro

GPT-6 Sol Pro is the same underlying model as GPT-6 Sol, served with reasoning.mode set to pro for higher-quality responses on complex tasks.

Cost note: pro mode spends far more reasoning tokens per request, so a typical request costs several times more than the same request on GPT-6 Sol and takes much longer to complete. It is intended for hard, high-stakes problems where the extra accuracy justifies the cost. For everyday coding, agentic, and chat workloads, use GPT-6 Sol instead.

Learn more in OpenAI's docs: https://developers.openai.com/api/docs/guides/reasoning#reasoning-mode

Text1.1M context$2 / $10
GPT-6 Sol Pro (batch)

GPT-6 Sol Pro is the same underlying model as GPT-6 Sol, served with reasoning.mode set to pro for higher-quality responses on complex tasks.

Cost note: pro mode spends far more reasoning tokens per request, so a typical request costs several times more than the same request on GPT-6 Sol and takes much longer to complete. It is intended for hard, high-stakes problems where the extra accuracy justifies the cost. For everyday coding, agentic, and chat workloads, use GPT-6 Sol instead.

Learn more in OpenAI's docs: https://developers.openai.com/api/docs/guides/reasoning#reasoning-mode

Text1.1M context$1 / $5
GPT-6 Sol

GPT-6 Sol is the cost-efficient high-end model in OpenAI's GPT-6 series, positioned below the flagship GPT-6 Astra and above the fast GPT-6 Luna tier. It is suited for demanding professional work, agentic coding, business workflow automation, and computer use, and is particularly strong at long-horizon software engineering tasks in real codebases. It approaches Astra-level factual reliability at a much lower cost and shares Astra's clearer, more concise communication style in technical and coding conversations.

Text1.1M context$2 / $10
GPT-6 Sol (batch)

GPT-6 Sol is the cost-efficient high-end model in OpenAI's GPT-6 series, positioned below the flagship GPT-6 Astra and above the fast GPT-6 Luna tier. It is suited for demanding professional work, agentic coding, business workflow automation, and computer use, and is particularly strong at long-horizon software engineering tasks in real codebases. It approaches Astra-level factual reliability at a much lower cost and shares Astra's clearer, more concise communication style in technical and coding conversations.

Text1.1M context$1 / $5
GPT Astra Latest

This model always redirects to the latest model in the GPT Astra family.

Text1.1M context
GPT Sol Latest

This model always redirects to the latest model in the GPT Sol family.

Text1.1M context
GPT Terra Latest

This model always redirects to the latest model in the GPT Terra family.

Text1.1M context