{"id":28211,"date":"2026-09-10T14:08:59","date_gmt":"2026-09-10T06:08:59","guid":{"rendered":"https:\/\/monsterblockhk.com\/?p=28211"},"modified":"2026-10-15T12:36:34","modified_gmt":"2026-10-15T04:36:34","slug":"gpt-image-2-5-guide","status":"publish","type":"post","link":"https:\/\/monsterblockhk.com\/en\/gpt-image-2-5-guide\/","title":{"rendered":"A Comprehensive Analysis of GPT Image 2.5: A Hands-On Look at OpenAI\u2019s Latest AI Image Generation Model\u2014Reactions, Features, and Limitations All in One Place"},"content":{"rendered":"
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GPT Image 2.5 is the latest generation of AI image-generation models released by OpenAI on September 8, 2026. According to the company, it offers generation speeds up to 50% faster, sharper details, and ensures that people and products remain consistent even after multiple rounds of editing. The model is available in a consumer version, ChatGPT Images 2.5, as well as two developer API versions: Flare and Sunburst. According to rankings compiled by the third-party website llm-stats, the two API versions claimed the top two spots on LMArena\u2019s image editing blind test rankings the day after their release.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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However, impressive official data doesn\u2019t mean there are no controversies. Initial reactions from the community were mixed: some developers praised it as \u201ca huge step forward\u201d after testing it, while others questioned the lack of transparency regarding token consumption and inconsistent content moderation standards. This article covers everything from the model\u2019s positioning, core features, user feedback from hands-on testing, real-world use cases, and pricing, all the way to the limitations you need to know before using it\u2014helping you decide whether GPT Image 2.5 is worth incorporating into your workflow.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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Instead of reading other people\u2019s reviews, why not open ChatGPT and test the new model\u2019s image-generation capabilities for yourself:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tTry ChatGPT Images 2.5 for free now<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t
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What is GPT Image 2.5? How does it differ from GPT Image 2?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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What is the official positioning of GPT Image 2.5?<\/h3>\t\t\t\t<\/div>\n\t\t\t\t
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GPT Image 2.5 is the latest version in OpenAI\u2019s series of image-generation models, with a focus on \u201creliable editing loops\u201d: starting with a reference image, modifying a single detail while keeping the rest\u2014which is already satisfactory\u2014unchanged, and then continuing to make adjustments without causing image drift. According to Official Announcement from OpenAI<\/a>This generation addresses the three major pain points of the previous generation, GPT Image 2\u2014speed, accuracy in rendering people, and consistency across multiple editing rounds\u2014one by one.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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Compared to GPT Image 2, which was released in April 2026, the officially listed upgrades include: sharper details, more natural lighting and textures, better preservation of the facial and product features in the reference photos, and a reduction in generation latency of up to 50%. According to A report by 9to5Mac<\/a>The new model is being rolled out to all ChatGPT, ChatGPT Work, and Codex users across desktop, mobile, and web platforms.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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How do I choose between the Flare and Sunburst API versions?<\/h3>\t\t\t\t<\/div>\n\t\t\t\t
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At the API level, GPT Image 2.5 is divided into two distinct models. GPT-Image-2.5 Flare is the default option, delivering higher-quality images with half the latency of GPT Image 2, making it suitable for high-volume daily generation, creator content, and rapid prototyping. GPT-Image-2.5 Sunburst, on the other hand, is designed for high-end visual workflows. While it takes longer to generate images, it offers tighter control over details during multi-round revisions.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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A simple way to decide: If you need to produce images quickly, are cost-conscious, or need to generate a large volume of images, choose Flare; if you\u2019re working on brand visuals or product renderings\u2014projects that require iterative fine-tuning without compromising quality\u2014Sunburst\u2019s precision makes the longer wait time worthwhile. Both models now include two new quality levels, \u201cxhigh\u201d and \u201cmax,\u201d allowing developers to adjust settings according to their needs.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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\"GPT<\/p>

Comparison of Generation Results for GPT-Images 2.5 Sunburst Across Three Quality Levels (High, Medium, Low)<\/em><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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Why does the name \u201cGPT Image 2.5\u201d cause confusion?<\/h3>\t\t\t\t<\/div>\n\t\t\t\t
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In the early days of its release, social media was flooded with comparison images and half-true, half-false rumors, partly due to the name itself. According to Namez Crafter Review<\/a>Strictly speaking, \u201cGPT Image 2.5\u201d is not a single official model name: consumers see it as \u201cChatGPT Images 2.5\u201d in ChatGPT, while developers use two separate models\u2014Flare and Sunburst\u2014via the API.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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This practice of distinguishing between \"consumer\" and \"developer\" versions has led many users to believe that the version they are using is the same as the one others are testing, when in fact there may be differences in quality, speed, and pricing. When reading any third-party reviews, the first step to avoiding misinformation is to confirm which variant was tested.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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Once you understand how the model is positioned, try generating a few images using a free account\u2014that\u2019s the best way to experience the differences in this generation:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tTry GPT Image 2.5 for Free to Generate Images<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t
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What are the key feature upgrades in GPT Image 2.5?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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What improvements have been made in image quality and consistency?<\/h3>\t\t\t\t<\/div>\n\t\t\t\t
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In terms of image quality, GPT Image 2.5 focuses on reducing the \u201cplastic-like\u201d appearance that was often criticized in the previous generation: details such as skin texture, metal reflections, and fabric textures are now closer to real photography, and the transitions between light and shadow are more natural. Text rendering within images has also improved, with a reduced error rate in complex layouts such as poster headlines and product packaging text.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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Consistency is the most practical upgrade in this generation. After uploading a reference photo of a person or pet, the subject\u2019s features remain highly preserved even when the background, clothing, or visual style is changed. For creators who need to maintain a consistent character design, this solves the core pain point of the past, where \u201cevery time an image was edited, the subject\u2019s appearance would become distorted.\u201d During multiple rounds of editing, previously modified parts will not be accidentally overwritten by new commands.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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\"Comparison<\/p>

From left to right: ChatGPT Images 2.0 (High), ChatGPT Images 2.5, ChatGPT Images 2.5 (9:16) \u2014 a comparison of the same reference person generated by different versions<\/em><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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How does the Sketch feature work?<\/h3>\t\t\t\t<\/div>\n\t\t\t\t
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Sketch is a new drawing tool added to the ChatGPT interface that allows users to hand-draw sketches directly within a conversation to serve as composition references for the final image. You can start by sketching the approximate positions of objects and the layout, then use text to specify style, lighting, and other details. The model will generate a complete image based on the spatial structure of your sketch.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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This feature is particularly useful for users who aren\u2019t good at describing layouts in words. In the past, specifying layout requirements such as \u201cplace the title in the top-left corner, the product in the bottom-right corner, and leave white space in the middle\u201d using only text prompts often didn\u2019t yield good results; now, by simply drawing it out, the communication effort is significantly reduced.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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What are the uses of \"Comment\" and \"Templates\"?<\/h3>\t\t\t\t<\/div>\n\t\t\t\t
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Comment-based editing lets you circle a specific area on the generated image and leave a comment; the model will only modify the annotated elements, leaving the rest unchanged. This \u201cpoint-and-edit\u201d approach is far more precise than a text-based request like \u201cPlease edit the third object on the left,\u201d and it is the core experience of what the developers refer to as the \u201creliable editing loop.\u201d<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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\"Templates\" provides ready-made templates for common formats such as posters and product images. After applying a template, users simply need to replace the content, making it ideal for small and medium-sized businesses and creators who need to quickly produce social media graphics or promotional materials. Combined with the consistency of reference images, these two features bring ChatGPT\u2019s image-generation workflow closer than ever to the editing experience of professional design tools.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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What have been the actual reactions of netizens and developers to GPT Image 2.5?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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What are some of the positive reviews of X on the official forum?<\/h3>\t\t\t\t<\/div>\n\t\t\t\t
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In its official post on X, OpenAI immediately summarized the key features of this update:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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ChatGPT Images 2.5\u2014faster, sharper, smarter, with improved creative tools. Faster image generation, more natural and recognizable image fidelity, consistent details across multiple edits, comment-based edits\u22ef\u22ef<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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\u2014 @OpenAI,Post a Message<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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\u5728 Official Announcement Post on the OpenAI Developer Forum<\/a>Following this, Sam Saffron (sam.saffron), co-founder of Discourse, tested Codex\u2019s native support for generating images using a prompt for a complex technical poster and described it as \u201ca big step change.\u201d Other developers, such as safi7yy, called it an \u201cexciting update\u201d and a \u201cmajor advancement.\u201d<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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The real-world tests circulating online also focus on two landmark tests: the first is the scrambled Rubik\u2019s Cube mirror reflection test, in which GPT Image 2.5 correctly renders the reflection in the mirror\u2014a detail where nearly all previous image models have failed; second, in stop-motion-style multi-frame images, the characters\u2019 appearances remain consistent.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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Where are the criticisms and controversies concentrated?<\/h3>\t\t\t\t<\/div>\n\t\t\t\t
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The negative feedback has primarily come from the developer community and has focused on transparency rather than image quality. Forum user merefield questioned why the official team had not published a comparison of token consumption between the old and new models, making it difficult to estimate costs; veteran community member _j reported that when regenerating images in inference mode, the preview image is \u201cirretrievably\u201d replaced, and pointed out that some billing details are not documented.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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Content moderation is another point of contention. Some users have reported that prompts of the same nature are sometimes approved and sometimes rejected, indicating inconsistent standards; Community moderator EricGT suggested that OpenAI provide alternative prompt suggestions when a prompt triggers moderation, rather than rejecting it outright. While these issues do not affect the model\u2019s generative capabilities, they pose practical engineering risks for teams looking to integrate GPT Image 2.5 into their formal product workflows.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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What Do the LMArena Blind Test Rankings Reveal?<\/h3>\t\t\t\t<\/div>\n\t\t\t\t
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Early data from third-party blind testing platforms favors GPT Image 2.5. According to a third-party website Rankings compiled by llm-stats<\/a>(Third-party estimates; not official figures from OpenAI or Google.) As of September 9, 2026, on the Arena image editing leaderboard, Sunburst leads with 1,520 points, followed by Flare with 1,491 points, ahead of Seedream 5.0 Pro (1,394), Google\u2019s Nano Banana Pro (1,390), and Nano Banana 2 (1,387).<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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However, there are two points to note. First, these scores are based on several thousand votes cast shortly after release and should be viewed as early indicators rather than definitive conclusions. Second, as pointed out in Namez Crafter\u2019s review, on the first day of release, \u201call quality metrics were derived from OpenAI\u2019s own data,\u201d and independent verification will take time. According to figures released by the third-party blind testing platform Arena, the previous-generation GPT Image 2 swept its rankings by a record-breaking margin of 242 points when it was released in April (see Official Arena Post<\/a>), It remains to be seen whether GPT Image 2.5 can replicate this dominance\u2014we\u2019ll need to wait for one or two months of voting data to find out.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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Raw images are just one part of the AI workflow. If you want to learn how to set up a 24\/7 online AI assistant to handle more daily tasks:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tRead the Openclaw Lobster AI Beginner's Guide<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t
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What are some practical use cases for GPT Image 2.5?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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How to Create E-commerce Product Images and Advertising Posters?<\/h3>\t\t\t\t<\/div>\n\t\t\t\t
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E-commerce product images are the most direct commercial application of GPT Image 2.5. By uploading real-life product photos as reference images and instructing the model to change the scene, lighting, or color scheme, the product\u2019s appearance remains accurate\u2014which is precisely where older models most often \u201cget the product wrong.\u201d According to Felo's Hands-On Review<\/a>When creating posters, enclosing the title text in quotation marks (e.g., \u201cCITY LIGHTS FESTIVAL\u201d) can significantly improve the accuracy of the text rendering.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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Recommended workflow: Start by using a clear prompt to define the theme, style, mood, lighting, composition, and dimensions. After generating the first draft, describe the elements that need to be modified one by one, rather than rewriting the entire prompt each time. By using templates, the time required to create a set of social media promotional images can be reduced from several hours to just a few tens of minutes.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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How Can Character Consistency and Storyboarding Be Achieved?<\/h3>\t\t\t\t<\/div>\n\t\t\t\t
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Content creators benefit most from character consistency. After uploading character reference images and describing new poses, scenes, or outfits, the character\u2019s facial features and traits remain consistent across images, making it possible for the first time to create multi-panel comics, storyboards, and brand mascot series all in one place within ChatGPT. Taiwanese tech media outlet Grenade\u2019sTutorials<\/a>It also demonstrates the entire process\u2014from character creation and costume modification to action extension\u2014using six prompt examples.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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Using Sketch\u2019s features, you can first sketch out rough compositions for each frame, then have the model fill in the details. For YouTubers or advertising teams that need storyboards before filming, this is much faster than outsourcing to a storyboard artist\u2014and costs an order of magnitude less.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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What can developers do with the API?<\/h3>\t\t\t\t<\/div>\n\t\t\t\t
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Typical use cases at the API level include: using Flare for high-volume user-generated content (such as avatar generators and product mockup tools), and using Sunburst for design SaaS that requires fine-grained control. GPT Image 2.5 is also natively integrated into Codex, allowing developers to generate technical diagrams, product posters, or UI assets within the same environment where they write code, without having to switch tools.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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It is worth noting the role of third-party aggregation platforms. Through OpenRouter<\/a> Through API gateways, developers can use a unified interface to simultaneously call both GPT Image 2.5 and competing models for A\/B testing, and then decide which one to use in production based on quality and cost.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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What are the pricing details for GPT Image 2.5? Can free users use it?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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What are the image generation permissions for each ChatGPT subscription plan?<\/h3>\t\t\t\t<\/div>\n\t\t\t\t
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For consumers, GPT Image 2.5 has been rolled out to all ChatGPT users, including those on the free plan\u2014the difference lies in generation quotas and speed prioritization. Currently, ChatGPT subscriptions are divided into Free, Go ($8 per month), Plus ($20 per month), Pro ($200 per month), and Business tiers. The higher the paid tier, the larger the image generation quota and the shorter the wait times during peak hours.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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For general users, the free version is sufficient for trying out Sketch and the comment editing features; for users with regular graphic design needs (such as creating daily social media graphics), the Plus plan offers the best value for the money.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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What are the API fees, and what is the approximate cost per image?<\/h3>\t\t\t\t<\/div>\n\t\t\t\t
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API pricing is identical to that of the previous-generation GPT Image 2: $5 per million tokens for text input, $8 per million tokens for image input, $30 per million tokens for image output, and $2 per million tokens for cache reads. OpenAI has not yet announced the per-image pricing for GPT Image 2.5; actual costs are calculated based on token usage. Please refer to OpenAI\u2019s official pricing page for the most up-to-date rates:OpenAI API Pricing Page<\/a><\/u>\u3002<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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\u201cMore for the same price\u201d is the key message of this release: higher quality and half the latency at the same cost. However, be aware of the issues already pointed out by the developer community\u2014there is no comprehensive documentation regarding the actual token consumption for the newly added xhigh and max quality tiers. Before going live, you should test the costs using your own real-world workloads rather than relying solely on estimates.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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MB will continue to track the latest hands-on reviews of AI and Web3 tools. If you don't want to miss future updates, bookmark this page:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tStay Up to Date on MB's Latest AI and Web3 Tool Reviews<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t
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Comparison of GPT Image 2.5 and Nano Banana 2<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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Nano Banana 2 is Google's image generation model; its official name is Gemini 3.1 Flash Image. It was released as a preview on February 26, 2026, and officially launched on May 28 (Official Google Introduction<\/a>\u3001Gemini API Release Notes<\/a>). Google subsequently released Nano Banana 2.1 on October 6, 2026, and recommended that developers switch from Nano Banana 2 to the new version; therefore, the table below also includes 2.1 for reference.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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Specifications and Pricing Comparison<\/h3>\t\t\t\t<\/div>\n\t\t\t\t
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Project<\/th>GPT Image 2.5<\/th>Nano Banana 2<\/th>Nano Banana 2.1<\/th><\/tr>
Developer<\/td>OpenAI<\/td>Google<\/td>Google<\/td><\/tr>
Release Date<\/td>September 8, 2026<\/td>February 26, 2026 (Preview); May 28, 2026 (Official Release)<\/td>October 6, 2026<\/td><\/tr>
Version<\/td>Flare (fast, for everyday use), Sunburst (most powerful, slower)<\/td>Single version; a Lite version is also available<\/td>Single Version<\/td><\/tr>
Maximum Resolution<\/td>Longest side: 3,840 pixels (approximately 4K); resolutions exceeding 2,560 \u00d7 1,440 are experimental<\/td>4K<\/td>4K<\/td><\/tr>
General User Portal<\/td>ChatGPT: All Plans, Including the Free Version<\/td>Gemini App (Free Version)<\/td>Gemini App Paid Plans (AI Plus, Pro, Ultra)<\/td><\/tr>
API Fees<\/td>Per token: Image output costs $30 per million tokens; the official price per image has not been disclosed.<\/td>$0.067 (1K) to $0.151 (4K) per image<\/td>$0.0336 (1K) to $0.113 (4K) per image<\/td><\/tr>
Can it be used directly in Hong Kong?<\/td>No (Neither ChatGPT nor the OpenAI API supports Hong Kong.)<\/td>The Gemini app works; the Gemini API does not.<\/td>The Gemini app works; the Gemini API does not.<\/td><\/tr><\/table>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t
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Source:OpenAI GPT Image 2.5 Flare Model Page<\/a>\u3001OpenAI Image Generation Guide<\/a>\u3001OpenAI API Pricing<\/a>\u3001Gemini API Pricing<\/a>\u3001Gemini Raw Image Description<\/a>. Prices are listed in U.S. dollars and are subject to change at any time; the prices displayed in real time on the official website are final.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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\"Gemini
Gemini API Pricing Page: Nano Banana 2.1 costs approximately $0.034 per 1K image, which is cheaper than Nano Banana 2 (verified on October 14, 2026) | Image source:ai.google.dev<\/a><\/figcaption><\/figure>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t
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What are the differences in functional orientation?<\/h3>\t\t\t\t<\/div>\n\t\t\t\t
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