[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-slug-gpt-image-2-openai-agentic-2k-reasoning":3,"news-related-18df2ab9-affd-4ba8-adaa-03cc7e3a2317":36},{"id":4,"title":5,"summary":6,"content":6,"original_url":7,"source_id":8,"tags":9,"translations":23,"news_slug":29,"published_at":30,"created_at":31,"modified_at":32,"is_published":33,"publish_type":34,"image_url":13,"view_count":35},"18df2ab9-affd-4ba8-adaa-03cc7e3a2317","GPT Image 2 发布：OpenAI 首次将推理能力注入图像生成","4月21日，OpenAI 正式发布 GPT Image 2（ChatGPT Images 2.0），这是 DALL-E 3 的继任者，也是业界首款真正将 O 系列推理能力融入图像生成的模型。与传统扩散模型不同，GPT Image 2 在渲染像素之前，会先研究提示中的实体关系、规划图像布局、推理细节约束，然后才输出——这意味着它真正先思考再作画。\n核心技术突破体现在三个方面。第一，Agentic 架构：模型不再遵循提示→直接渲染的直线路径，而是加入了研究、规划、验证四阶段流程，显著提升了杂志排版、多格漫画、复杂信息图等场景的一次成功率。第二，多语言文字渲染：支持拉丁、日韩 CJK、印地语、孟加拉语等，字符级准确率达 99%，解决了图像模型长期难以准确渲染文字的顽疾。第三，原生 2K（2048 像素）分辨率输出，满足商业印刷级别需求。\nbenchmark 方面，GPT Image 2 以 +242 分优势登顶 Image Arena 排行榜。背后由 GPT-5.4 主干网络驱动。API 定价：图像输入 tokens 8美元\u002F百万（缓存命中仅2美元），输出 tokens 30美元\u002F百万。ChatGPT 和 Codex 用户4月22日起全面开放，开发者 API 预计5月上线。\nGPT Image 2 的意义不仅是又一款高质量图像模型，更重要的是它重新定义了多模态生成的竞争逻辑。当业界还在优化扩散模型的步数与调度时，OpenAI 已将竞争焦点从生成质量转向生成可靠性——让复杂 prompt 不再翻车，才是下一代创作工具的核心壁垒。这一路线一旦被验证跟进，多模态模型的 Agentic 化将成为下一阶段的主战场。","https:\u002F\u002Fopenai.com\u002Findex\u002Fintroducing-chatgpt-images-2-0\u002F","15975962-b5fe-49e5-ae68-687ba6cb7015",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"40269b40-7942-4650-9672-ed2e6524d37a","ai-technology",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"7e89b5cc-57db-4f37-bc6d-28919a73931c","model-release",{"id":18,"name":19,"slug":19,"description":13,"color":13},"499f4b56-819d-49a3-9609-33e775143b86","multimodal",{"id":21,"name":22,"slug":22,"description":13,"color":13},"c883fd20-1d66-4fb7-9fc7-320fa7f87023","text-to-image",[24],{"id":25,"lang":26,"title":27,"summary":28,"content":13},"0e67c2ab-20df-4b78-8296-b8cbc851ea62","en","GPT Image 2: OpenAI brings reasoning into image generation","On April 21, OpenAI officially released GPT Image 2 (ChatGPT Images 2.0), the successor to DALL-E 3 and the industry's first model that truly integrates O-series reasoning capability into image generation. Unlike traditional diffusion models, GPT Image 2 first studies entity relationships in the prompt, plans image layout, reasons about detail constraints, before outputting — meaning it truly thinks first, then paints.\n\nCore technical breakthroughs manifest in three aspects. First, Agentic architecture: the model no longer follows a straight line from prompt to direct rendering, but introduces four stages of research, planning, and verification, significantly improving first-pass success rates for magazine layouts, multi-panel comics, and complex infographics. Second, multilingual text rendering: supports Latin, Japanese\u002FKorean CJK, Hindi, Bengali, etc., with character-level accuracy reaching 99%, solving the long-standing problem of image models being unable to render text accurately. Third, native 2K (2048 pixel) resolution output, meeting commercial print-level requirements.\n\nOn benchmarks, GPT Image 2 tops the Image Arena leaderboard with a +242 score advantage. Driven by the GPT-5.4 backbone network. API pricing: $8\u002Fmillion image input tokens ($2 with cache hit), $30\u002Fmillion output tokens. ChatGPT and Codex users gain full access from April 22, developer API expected in May.\n\nGPT Image 2's significance isn't just being another high-quality image model, but more importantly that it redefines multimodal generation's competitive logic. While the industry is still optimizing diffusion model steps and scheduling, OpenAI has shifted the competitive focus from generation quality to generation reliability — making complex prompts stop failing is the core moat of the next generation of creative tools. Once this path is validated and followed, multimodal models' Agentic-ization will become the next main battlefield.","gpt-image-2-openai-agentic-2k-reasoning","2026-04-28T10:00:00Z","2026-04-28T10:08:34.914772Z","2026-08-19T02:08:40.142862Z",true,"agent",135,{"items":37},[38,43,48,53,58,63],{"id":39,"title":40,"news_slug":41,"published_at":42},"619ad304-0d2a-4dba-b91e-19414d036746","Grok Imagine Image 2.0：文生图 Arena 双榜第二","grok-imagine-image-2-0-arena-second","2026-08-13T02:00:00+00:00",{"id":44,"title":45,"news_slug":46,"published_at":47},"f1397080-206a-469f-846c-932a4b3ab8f9","京东开源 JoyAI-Image：统一多模态基础模型，把「理解-生成-编辑」拧成一个闭环","jd-joyai-image","2026-07-20T06:00:00+00:00",{"id":49,"title":50,"news_slug":51,"published_at":52},"aeb60d9a-6639-4669-96a4-951aadad40cb","AI 视频工具进入「全场景」分化期:6 款主流产品的技术路线对比","ai-video-tools-comparison","2026-07-08T08:00:00+00:00",{"id":54,"title":55,"news_slug":56,"published_at":57},"19566223-1b02-4e48-8c44-518694edb049","Meta Muse Image 落地：Superintelligence Labs 把多模态推理与图生能力拧成一股","meta-muse-image","2026-07-07T20:01:00+00:00",{"id":59,"title":60,"news_slug":61,"published_at":62},"4c951e48-08c9-4d5d-b9a0-3dfdd1b04bed","Visics 把 Object Trajectory 做成统一中间表征：通用具身大模型有了自己的 Token","visics-vloa-object-trajectory-embodied","2026-06-25T12:00:00+00:00",{"id":64,"title":65,"news_slug":66,"published_at":67},"e97b45e2-01b1-44f8-97d7-8a80765245ec","Seedream 5.0 接力 Seedance 2.5：字节把「图像→视频」拼成一条产线","seedream-5-0-bytedance-image-to-video","2026-06-23T08:00:00+00:00"]