studioglobal
熱門發現
答案已發布13 來源

GPT Image 2 vs Nano Banana Pro：邊個圖像 API 更啱你？

AVB 喺 2026 年 4 月 22 日做嘅 10 prompts 直接測試入面，GPT Image 2 完成 10/10，Nano Banana Pro 完成 9/10；GPT 較強在字體同版面任務，Nano 較強在寫實人像、膚質同光影 [6]。 兩邊 headline 圖像輸出價接近：OpenAI 列出 GPT Image 2 image output 為每 100 萬 tokens 30 美元；Google 亦列出 Gemini image output 為每 100 萬 tokens 30 美元，1024×1024 輸出估算為每張 0.039 美元 [14][25]。

使用 Studio Global AI 搜尋並查核事實從「發現」瀏覽更多內容
17K0
Editorial comparison graphic for GPT Image 2 and Nano Banana Pro image generation APIs
GPT Image 2 vsAI-generated editorial illustration comparing GPT Image 2 and Nano Banana Pro for image API selection.
AI 提示

Create a landscape editorial hero image for this Studio Global article: GPT Image 2 vs. Nano Banana Pro: Benchmarks, Pricing, and Which API to Use. Article summary: No public source here proves a universal winner: GPT Image 2 is the safer default for exact text and structured commercial layouts, while Nano Banana Pro has the stronger direct signal for photoreal lighting and skin.... Topic tags: ai, image generation, openai, gemini, nano banana. Reference image context from search candidates: Reference image 1: visual subject "# 2026 AI Image API Benchmark: GPT Image 2 vs Nano Banana 2/Pro vs Seedream 5.0. Generative AI is no longer judged solely by aesthetic appeal, but by **API reliability, text-render" source context "2026 AI Image API Benchmark: GPT Image 2 vs Nano Banana 2/Pro vs Seedream 5.0 - Atlas Cloud Blog" Reference image 2: visual subject "# GPT Image 2 vs Nano Banana 2 / Pro:

openai.com

揀圖像生成 API，唔應該由「邊個模型最勁」開始，而係問：邊個模型喺你嗰類圖最少甩轆？公開資料指向一個幾實用嘅分工：GPT Image 2 較適合作為精準文字、標籤、餐牌、UI copy、海報同重版面商業素材嘅首輪測試；Nano Banana Pro 喺寫實人像、膚質同光影創作方面有更直接嘅強項訊號 [3][6][10]。

先講結論：按工作量揀，唔好迷信總排名

你主要做嘅圖像工作建議先試點解
英文為主嘅圖中文字、labels、餐牌、signage、UI copy、海報、產品標註GPT Image 2公開比較入面，GPT Image 2 喺精準文字、技術名詞同重排版 prompts 上有較清晰優勢 [3][6]。
結構化廣告、包裝、產品 mockup、品牌版面、商業修圖GPT Image 2Vidguru 10-test 盲測指 GPT-Image 2 對 Nano Banana 2 贏 5 輪、另外 5 輪打和，最大差距出現喺 image-editing fidelity、物料邏輯同重版面商業工作 [10]。
寫實人像、lifestyle 廣告、UGC 風格圖、電影感光影Nano Banana ProAVB 直接測試指 Nano Banana Pro 喺 hyperreal portrait、UGC selfie、athletic ad prompts 勝出，強項係寫實感、膚質同光影 [6]。
中日韓文字（CJK）排版細緻度，或者戲劇化光影早啲測 Nano Banana Pro／Gemini 圖像流程Genspark 見到 Nano Banana 2 喺 CJK typography polish 同 dramatic lighting 有窄位優勢；但呢個係相鄰證據，唔係 Nano Banana Pro 直接結果 [3]。
產品相、電商 mockup、marketing infographic、解剖圖兩個都 benchmarkGenspark 指只要 prompt 得好，呢幾類任務 GPT Image 2 同 Nano Banana 2 基本上打和 [3]。
技術圖、帶標籤 schematic、工程式圖解兩個都 benchmarkAnalytics Vidhya 形容 annotated-diagram 任務非常接近，兩邊都準確畫出要求嘅 labels 同 data points [9]。
OpenAI 為中心嘅 stack、OpenAI tier 限額、batch jobsGPT Image 2OpenAI 有文件列明 GPT Image 2 model、rate limits、token pricing 同 Batch API 經濟效益 [13][14][15]。
Gemini 為中心嘅圖像流程，要 aspect ratio 同 2K 參數Nano Banana Pro／Gemini image workflowGoogle Nano Banana image-generation 文件展示 Gemini API 用 inline image inputs、aspect ratio 同 2K resolution 參數 [26]。

睇 benchmark 前，先分清楚證據有幾硬

最貼題嘅直接比較係 AVB 嘅 10-prompt 測試：佢哋喺 2026 年 4 月 22 日用 GPT Image 2.0 對 Nano Banana Pro，當中 Nano Banana Pro 標示為 gemini-3-pro-image [6]。呢個測試入面，GPT Image 2.0 生成晒 10/10 個 prompts；Nano Banana Pro 生成 9/10，並因政策原因拒絕一個涉及知名人物 CV 嘅 prompt [6]。

不過，其他有參考價值嘅公開比較未必係直接測 Nano Banana Pro。Genspark、Analytics Vidhya 同 Vidguru 比較嘅係 GPT Image 2 對 Nano Banana 2，而唔係 Nano Banana Pro [3][9][10]。呢啲結果可以幫你理解 Gemini／Nano Banana 圖像系統嘅行為，但唔應該當成你實際 Nano Banana Pro endpoint 嘅完全替代。

官方文件最可靠嘅地方係 model availability、價格、rate limits 同 API 參數：OpenAI 列出 gpt-image-2-2026-04-21 同 usage-tier rate limits [13]；OpenAI pricing page 列出 GPT Image 2 token pricing [14]；Google pricing page 列出 Gemini image-output pricing [25]；Google image-generation docs 則展示透過 Gemini API 做 Nano Banana generation [26]。至於質素 benchmark，公開資料多數係細 prompt set、review-style 比較，或者特定平台測試，未有一套單一、標準化、獨立嘅 benchmark suite [3][6][9][10]。

亦有比較文章提出好精準嘅排行榜名次或文字準確率百分比，但提供嘅片段未見足夠 methodology，唔適合單靠呢啲數字決定 production vendor [5][8]。

GPT Image 2 較有勝算嘅地方

圖中文字、字體同重版面素材

目前公開比較入面，文字生成係 GPT Image 2 最清楚嘅優勢。Genspark 指 GPT Image 2 喺精準文字同技術術語有窄但實在嘅優勢 [3]。AVB 直接比較 GPT Image 2.0 同 Nano Banana Pro 時，亦指 GPT Image 2.0 喺 in-image typography、漫畫對白格、雙語餐牌同 silkscreen gig poster 上勝出 [6]。

呢點對商業圖好關鍵。如果一個錯字、壞咗嘅 label、走樣 UI 字串，或者產品 callout 寫錯，會令成張圖報廢，GPT Image 2 係較穩陣嘅第一站 [3][6]。

商業修圖同結構化設計

Vidguru 嘅 10-test 盲測指，GPT-Image 2 對 Nano Banana 2 贏 5 輪、另外 5 輪打和；最大差距出現喺 image-editing fidelity、物料邏輯同重版面商業工作 [10]。所以如果你做廣告版面、包裝概念、產品 mockup、品牌圖像，或者任何需要構圖同文字都受控嘅素材，GPT Image 2 值得先測。

Nano Banana Pro 較有勝算嘅地方

寫實人像、膚質同光影

Nano Banana Pro 最強嘅直接訊號係 photoreal creative。AVB 10-prompt 比較入面，Nano Banana Pro 喺 hyperreal portrait、UGC selfie 同 athletic ad prompts 勝出；來源亦特別指出佢嘅寫實感、皮膚質感同光影表現係強項 [6]。

如果你做 editorial portrait、lifestyle campaign、creator-style 廣告，或者需要自然光、氣氛、電影感多過精準文案嘅概念圖，Nano Banana Pro 係合理嘅首選候選 [6]。

Gemini 原生圖像流程

Google Nano Banana image-generation docs 展示 Gemini API 可以用 inline image inputs、aspect ratio 設定同 2K resolution 參數 [26]。如果你嘅產品本身已經依賴 Gemini tooling，或者想圍繞 Google 官方文件入面嘅圖像生成流程設計，ecosystem fit 可能比小型 benchmark 入面一兩分差距更重要。

暫時未分勝負：產品圖、infographic、技術圖

對好多常見商業類別，公開資料未顯示穩定大贏家。Genspark 指，只要 prompt 得好，GPT Image 2 同 Nano Banana 2 喺 photorealistic product shots、e-commerce mockups、marketing infographics 同 anatomy diagrams 上基本上打和 [3]。

技術圖亦好接近。Analytics Vidhya 形容 annotated-diagram 任務係佢哋比較入面最接近嘅一場：Nano Banana 2 產出嚴謹嘅 two-view engineering-style diagram；GPT Image 2 則產出視覺上好強嘅 blueprint-style 結果；兩邊都準確畫出要求嘅 labels 同 data points [9]。如果你要精準尺寸、行業專用 notation 或嚴格 schematic 慣例，通用排名唔夠用，要用自己嘅模板測。

價錢：headline output cost 好近，但實際帳單未必一樣

OpenAI 列出 gpt-image-2 image input 為每 100 萬 tokens 8.00 美元、cached image input 2.00 美元、image output 30.00 美元 [14]。OpenAI 資料亦列出 GPT Image 2 text input 為每 100 萬 tokens 5.00 美元、cached text input 1.25 美元、text output 10.00 美元 [14][21]。

Google Gemini pricing page 則列出 image output 為每 1,000,000 tokens 30 美元，並指 1024×1024 或以下嘅輸出圖像消耗 1,290 tokens，即每張約 0.039 美元 [25]。

重點係：表面圖像輸出價差唔多，但實際成本可以差好遠。Prompt 長度、image inputs、reference images、resolution、edit loops、retry 次數、policy refusal、caching 同 routing，都會改變每張合格圖嘅有效成本 [14][25][26]。如果你係大量非同步工作，OpenAI 亦指 Batch API 可以為 inputs 同 outputs 節省 50%，並喺 24 小時內非同步執行任務 [15]。

API 限額同 routing：上線前要逐項核

OpenAI GPT Image 2 model page 列出分 tier rate limits，Free 不支援；較高 tier 由 Tier 1 到 Tier 5 按 TPM 同 IPM 擴大 [13]。文件中列出 Tier 1 為 100,000 TPM、5 IPM，Tier 5 為 8,000,000 TPM、250 IPM [13]。

Google Nano Banana image-generation docs 展示 Gemini API examples 可用 inline images、aspect ratio 同 2K resolution parameters [26]。如果呢啲控制正好配合你嘅產品要求，Nano Banana Pro 對 Gemini-centered workflow 可能較易落地。

如果你經第三方 router 用模型，唔好假設 first-party 限額同尺寸會原封不動。Fal 嘅 GPT Image 2 page 例如列出 custom dimensions 兩邊都要係 16 嘅倍數、單邊最大 3840px、最大 aspect ratio 3:1，總 pixel range 由 655,360 到 8,294,400 [17]。

到底應該用邊個 API？

如果你需要以下場景，先試 GPT Image 2：

  • 精準英文文字、labels、餐牌、UI copy、海報或產品 callouts [3][6]。
  • 重版面商業素材，例如廣告、包裝、產品 mockup 同結構化品牌圖像 [10]。
  • OpenAI API access，並需要清楚嘅 model availability、rate limits 同 token pricing 文件 [13][14]。
  • 大量非同步 image jobs，想用 Batch API 經濟效益 [15]。

如果你需要以下場景，先試 Nano Banana Pro：

  • 寫實人像、UGC-style imagery、lifestyle ads、皮膚質感或電影感光影 [6]。
  • Gemini／Nano Banana workflow，並需要 aspect ratio、2K resolution 等官方文件示範過嘅 image-generation 參數 [26]。
  • 想早啲測中日韓文字（CJK）排版細緻度或 dramatic lighting；但要記住，呢個 CJK 訊號嚟自 Nano Banana 2，而唔係 Nano Banana Pro 直接 benchmark [3]。
  • 預算模型配合 Google 文件入面 1024×1024 估算：1,290 output tokens，即每張 0.039 美元 [25]。

如果你嘅核心工作係 product shots、e-commerce mockups、infographics、anatomy diagrams 或 technical schematics，就應該 兩邊都 benchmark，因為現有比較顯示呢幾類結果相當接近 [3][9]。

點樣做一個真係有用嘅私家 benchmark

正式 standardize 任何一個 API 之前，先用你真實工作流砌一小批測試。唔好淨係測靚圖，要加入真正會令你流程出事嘅素材：產品圖、品牌廣告、UI 畫面、diagram、多語文字、reference-image edits、包裝、社交平台比例，以及可能觸發政策限制嘅邊界情況。

每張輸出可以用以下準則打分：

  • 文字準確度同可讀性。
  • Prompt adherence。
  • Layout 同空間邏輯。
  • Reference-image fidelity。
  • 寫實感或 style match。
  • 後續 prompt 修改嘅 editability。
  • Artifact rate。
  • Refusal rate。
  • 喺你自己 stack 入面嘅 latency。
  • 每張合格圖嘅實際成本。

Vidguru benchmark 提供咗一個幾實用嘅測試模式：first-take generations、相同 prompts、相關時用相同 references，評分亦集中喺 prompt adherence、commercial usability、text accuracy、physical logic 同 reference fidelity，而唔係純粹睇藝術喜好 [10]。

一句收尾

如果張圖嘅價值在於文字、標籤、排版同商業結構，GPT Image 2 係較好嘅第一個 API。 如果張圖嘅價值在於寫實光影、人像、膚質同 Gemini 原生工作流，Nano Banana Pro 係較好嘅第一個 API。至於產品圖、diagram 同 infographic，公開證據太接近，最可靠做法係用你自己嘅 prompts、限制同收貨標準做私家 benchmark [3][6][9][10]。

Studio Global AI

Search, cite, and publish your own answer

Use this topic as a starting point for a fresh source-backed answer, then compare citations before you share it.

使用 Studio Global AI 搜尋並查核事實

重點

  • AVB 喺 2026 年 4 月 22 日做嘅 10 prompts 直接測試入面，GPT Image 2 完成 10/10，Nano Banana Pro 完成 9/10；GPT 較強在字體同版面任務，Nano 較強在寫實人像、膚質同光影 [6]。
  • 兩邊 headline 圖像輸出價接近：OpenAI 列出 GPT Image 2 image output 為每 100 萬 tokens 30 美元；Google 亦列出 Gemini image output 為每 100 萬 tokens 30 美元，1024×1024 輸出估算為每張 0.039 美元 [14][25]。
  • 文字、標籤、UI、海報、包裝同結構化商業素材先試 GPT Image 2；寫實 lifestyle、人像、UGC 風格同 Gemini 原生流程先試 Nano Banana Pro；產品圖、infographic 同技術圖就應該兩個都測 [3][6][9][10][26]。

人們還問

「GPT Image 2 vs Nano Banana Pro：邊個圖像 API 更啱你？」的簡短答案是什麼？

AVB 喺 2026 年 4 月 22 日做嘅 10 prompts 直接測試入面，GPT Image 2 完成 10/10，Nano Banana Pro 完成 9/10；GPT 較強在字體同版面任務，Nano 較強在寫實人像、膚質同光影 [6]。

首先要驗證的關鍵點是什麼？

AVB 喺 2026 年 4 月 22 日做嘅 10 prompts 直接測試入面，GPT Image 2 完成 10/10，Nano Banana Pro 完成 9/10；GPT 較強在字體同版面任務，Nano 較強在寫實人像、膚質同光影 [6]。 兩邊 headline 圖像輸出價接近：OpenAI 列出 GPT Image 2 image output 為每 100 萬 tokens 30 美元；Google 亦列出 Gemini image output 為每 100 萬 tokens 30 美元，1024×1024 輸出估算為每張 0.039 美元 [14][25]。

接下來在實務上我該做什麼？

文字、標籤、UI、海報、包裝同結構化商業素材先試 GPT Image 2；寫實 lifestyle、人像、UGC 風格同 Gemini 原生流程先試 Nano Banana Pro；產品圖、infographic 同技術圖就應該兩個都測 [3][6][9][10][26]。

接下來我應該探索哪個相關主題？

繼續“Claude Security 公測版：Anthropic 點樣用 AI 幫企業掃 code 漏洞”以獲得另一個角度和額外的引用。

開啟相關頁面

我應該將其與什麼進行比較？

對照「xAI Grok 4.3 API 解讀：1M context、低 token 價與語音平台野心」交叉檢查此答案。

開啟相關頁面

繼續你的研究

IT Consulting Orange County CA, IT Services, IT Outsourcing - Technijian. # Anthropic Claude Security for Enterprise: What You Need to Know. Anthropic has officially opened Claude

Claude Security 公測版：Anthropic 點樣用 AI 幫企業掃 code 漏洞

Claude Security 公測版：Anthropic 點樣用 AI 幫企業掃 code 漏洞

抽象 AI 介面顯示 Grok 4.3 API、1M context、token 價格與語音波形

xAI Grok 4.3 API 解讀：1M context、低 token 價與語音平台野心

xAI Grok 4.3 API：1M context、$1.25/$2.50 定價如何改變模型與語音競爭

小米 Q5、MIX 5 与小米 18 系列传闻的概念手机图

小米 Q5 係咪 MIX 5？最新風向更似小米 18 系列

小米 Q5 更可能唔係 MIX 5：最新線索指向小米 18 系列

Metas KI-Altersprüfung: Neue Regeln für Jugendliche in EU, USA und Brasilien

Meta 用 AI 查年齡：Instagram、Facebook 青少年規則點變？

Meta 新 AI 年齡核實：Instagram 同 Facebook 對青少年有咩新規？

研究對話

你

研究問題

Search for GPT Image 2 vs. Nano Banana Pro benchmarks and compare them as comprehensively as possible.

Studio Global AI36 來源

引用的答案

GPT Image 2 and Nano Banana Pro appear very close overall, with GPT Image 2 showing the stronger pattern for exact text, structured layouts, and prompt compliance, while Nano Banana Pro/Gemini 3 Pro Image tends to be stronger for speed, 2K/4K workflows, CJK typography polish, lighting, and Google ecosystem integration. There is no single independent, standardized benchmark suite I found, so the comparison below is based on official API/pricing docs plus several small hands-on prompt tests; treat winner claims as directional, not definitive.

Evidence quality caveat

  • Official docs are strongest for model availability, pricing, and API capabilities: OpenAI lists gpt-image-2-2026-04-21 and pricing for gpt-image-2 in its API docs [1][2].
  • Google’s Gemini API docs cover Nano Banana image generation and pricing, including image output priced at $30 per 1,000,000 tokens and 1024×1024 output equivalent to $0.039 per image [4][5].
  • The benchmark evidence is weaker: most public comparisons are blog-style, small-N prompt tests rather than reproducible third-party benchmarks, and some are vendor/SEO sites [6][13][14].
  • Insufficient evidence for a statistically reliable “overall winner.”

Head-to-head summary

DimensionGPT Image 2Nano Banana Pro / Gemini 3 Pro ImagePractical read
Text renderingUsually favored in hands-on comparisons for precise text and technical terminology [14]Often strong, with some reports favoring it for CJK typography polish [14]Pick GPT Image 2 for English-heavy posters, labels, UI mockups, diagrams.
Prompt adherencePublic tests report strong compliance; one 10-prompt test said GPT Image 2 rendered all 10 prompts [13]Same test said Nano Banana Pro rendered 9/10 and refused one prominent-person CV prompt [13]GPT may be less refusal-prone in some commercial prompt sets, but policy behavior varies.
Technical diagramsComparisons describe GPT Image 2 as strong for spatial logic and technical/diagram-like outputs [15]Some tests describe Nano Banana as producing rigorous annotated diagrams [9]Close; test your exact diagram style.
Photorealism/product shotsHands-on comparisons describe both as very close for product shots, mockups, infographics, and anatomy diagrams [14]Same: very close, with no universal winner in many commercial categories [14]Either can work; choose by ecosystem and cost.
Lighting / cinematic styleCompetitive, but less consistently singled outHands-on comparison gives Nano Banana 2 a narrow edge for dramatic lighting [14]Nano Banana Pro may be preferable for cinematic/editorial imagery.
Resolution / productionThird-party access page for GPT Image 2 lists custom dimensions with max single edge 3840 px and total pixels up to 8,294,400 [10]Google’s image docs show Nano Banana generation options including aspect ratio and 2K resolution [5]Both are production-capable; Nano Banana is more visibly marketed around 2K/4K workflows.
SpeedNot consistently established from official docs in the search resultsSome non-official comparisons claim very fast 3–5 second generation for Nano Banana 2 [13]Insufficient evidence from official sources; benchmark in your stack.
API pricingOpenAI docs list gpt-image-2 image input at $8.00 per 1M tokens, cached image input at $2.00, image output at $30.00, text input at $5.00, cached text input at $1.25, and text output at $10.00 [2]Google docs list image output at $30 per 1M tokens, with 1024×1024 images using 1,290 tokens, equivalent to $0.039 per image [4]Similar output-token headline; actual per-image cost depends on resolution, inputs, routing, and batch use.
EcosystemBest if your workflow is already OpenAI/ChatGPT/API-centered [1][2]Best if your workflow is Gemini, Google AI Studio/API, Search/Workspace/Vertex-style tooling [4][5]Ecosystem fit may matter more than small quality deltas.

Benchmark findings from public comparisons

  • A 10-prompt hands-on test reported GPT Image 2 completed 10/10 prompts, while Nano Banana Pro completed 9/10 and refused a prompt involving Elon Musk’s CV due to prominent-person policy concerns [13].
  • A hands-on comparison found GPT Image 2 had a narrow edge on precise text and technical terminology, while Nano Banana 2 had a narrow edge on CJK typography polish and dramatic lighting [14].
  • The same comparison said the two models were close on photorealistic product shots, e-commerce mockups, marketing infographics, and anatomy diagrams [14].
  • Another comparison source characterizes GPT Image 2 as stronger for spatial relationships and Nano Banana 2 as Google DeepMind’s image model combining generation/editing strengths, but it is a software-comparison listing rather than a rigorous benchmark [15].
  • Some SEO-style comparison pages make very specific claims such as GPT Image 2 “99.2% text accuracy,” but I would not treat those as reliable without methodology, dataset, and reproducible examples [11].

Where GPT Image 2 looks better

  • Use GPT Image 2 when your image depends on exact English text, labels, UI copy, branded layouts, technical captions, or instruction-heavy prompts, because public hands-on comparisons give it an edge on precise text and technical terminology [14].
  • Use GPT Image 2 for workflows where OpenAI API integration and token pricing are already understood, since OpenAI’s official docs list the model and pricing directly [1][2].
  • Use GPT Image 2 if policy refusal sensitivity is a concern for benign commercial tests, since one small benchmark reported GPT Image 2 completed all 10 prompts while Nano Banana Pro refused one [13].

Where Nano Banana Pro looks better

  • Use Nano Banana Pro when you need Google/Gemini-native workflows, because Google’s official docs cover Nano Banana image generation through the Gemini API [5].
  • Use Nano Banana Pro for 2K-oriented generation workflows, since Google’s docs show Nano Banana image generation parameters including aspect ratio and 2K resolution [5].
  • Use Nano Banana Pro when CJK typography polish, cinematic lighting, or dramatic style matter most, because one hands-on comparison found a narrow edge for Nano Banana in those areas [14].
  • Use Nano Banana Pro when simple per-image pricing around 1024×1024 is attractive, because Google documents 1024×1024 output as 1,290 tokens, equivalent to $0.039 per image [4].

Pricing comparison

  • OpenAI’s official pricing for gpt-image-2 is token-based: image input $8.00 per 1M tokens, cached image input $2.00, image output $30.00, text input $5.00, cached text input $1.25, and text output $10.00 [2].
  • Google’s official Gemini pricing says image output is $30 per 1M tokens, and an output image up to 1024×1024 consumes 1,290 tokens, equivalent to $0.039 per image [4].
  • The headline output price is similar at $30 per 1M image-output tokens, but real job cost can diverge because prompts, reference images, resolution, editing steps, retries, and provider routing all affect total cost [2][4][5].

Recommendation

  • Choose GPT Image 2 if your top priorities are text accuracy, structured designs, instruction following, technical diagrams, brand layouts, and OpenAI integration.
  • Choose Nano Banana Pro if your top priorities are Gemini integration, fast creative iteration, 2K/4K-style production workflows, cinematic lighting, and multilingual/CJK visual text.
  • For production, run a private benchmark with 30–50 prompts from your actual workload and score: text accuracy, prompt adherence, editability, artifact rate, latency, refusal rate, and cost per accepted image. Public benchmark evidence is too limited to replace that.

來源

  • [3] GPT Image 2 vs Nano Banana 2: Hands-On 2026 Comparisongenspark.ai

    If you only read one paragraph: GPT Image 2 has a narrow, real edge on precise text and technical terminology. Nano Banana 2 has a narrow, real edge on CJK typography polish and dramatic lighting. On photorealistic product shots, e-commerce mockups, marketi...

  • [5] GPT Image 2 vs. Nano Banana 2: The Ultimate 2026 AI ... - GlobalGPTglbgpt.com

    logo GPT Image 2 vs. Nano Banana 2: The Ultimate 2026 AI Image Comparison Guide avatar GPT Image 2 vs. Nano Banana 2: The Ultimate 2026 AI Image Comparison Guide GPT Image 2 leads in spatial logic and 99.2% text accuracy, while Nano Banana 2 excels in 4K pr...

  • [6] GPT Image 2.0 vs Nano Banana Pro: 10 Prompts Tested 2026 | AVBaivideobootcamp.com

    TL;DR: We ran the same 10 prompts through GPT Image 2.0 (gpt-image-2) and Nano Banana Pro (gemini-3-pro-image) on April 22, 2026. GPT 2.0 rendered 10 of 10. Nano Banana Pro rendered 9 of 10 and refused the Elon Musk CV prompt with the message "This prompt m...

  • [8] GPT-Image-2 vs Nano Banana Pro: Which is stronger? 7 ...help.apiyi.com

    Skip to content Apiyi.com Blog Apiyi.com Blog Best AI API Router Services Apiyi.com Blog Apiyi.com Blog Best AI API Router Services Image Generation API Model Selection & Comparison GPT-Image-2 vs Nano Banana Pro: Which is stronger? 7-dimensional deep showd...

  • [9] Is GPT Image 2 the Best Image Generation Model? - Analytics Vidhyaanalyticsvidhya.com

    Image 14: Annotated Diagrams Observation: Task 5 was the closest contest of the comparison. Nano Banana 2 produced a technically rigorous two-view engineering diagram with bold annotation lines, precise measurement callouts, and a detailed Wing Warp schemat...

  • [10] Nano Banana 2 vs GPT-Image 2: Our 10-Test Blind Benchmark After OpenAI's API Launch | Vidguruvidguru.ai

    About This Test This benchmark was conducted by Vidguru AI Lab on April 23, 2026 using the Vidguru web platform. All generations were first-take only, with identical prompts and identical references where relevant. Scores focused on prompt adherence, commer...

  • [13] GPT Image 2 Model | OpenAI APIdevelopers.openai.com

    gpt-image-2-2026-04-21 Rate limits Rate limits ensure fair and reliable access to the API by placing specific caps on requests or tokens used within a given time period. Your usage tier determines how high these limits are set and automatically increases as...

  • [14] Pricing | OpenAI APIdevelopers.openai.com

    Model Modality Input Cached input Output --- --- gpt-image-2 Image $8.00 $2.00 $30.00 Text $5.00 $1.25 gpt-image-1.5 Image $8.00 $2.00 $32.00 Text $5.00 $1.25 $10.00 gpt-image-1-mini Image $2.50 $0.25 $8.00 Text $2.00 $0.20 All models Batch For image genera...

  • [15] API Pricing - OpenAIopenai.com

    Price $10.00 / 1k calls Search content tokens are free. Containers Run code and tools in secure, scalable environments alongside your models. Price Now: 1 GB for $0.03 / 64GB for $1.92 per container Starting March 31, 2026: 1 GB for $0.03 / 64GB for $1.92 p...

  • [17] GPT Image 2 API | Text to Image - Fal.aifal.ai

    // Use the returned URL in your request []( Custom image dimensions must be multiples of 16 on both edges Maximum single edge is 3840px; maximum aspect ratio is 3:1 Total pixel count must be between 655,360 and 8,294,400 When running client-side code, never...

  • [21] Introducing gpt-image-2 - available today in the API and Codexcommunity.openai.com

    Modality Input Cached Input Output --- --- Image $8.00 $2.00 $30.00 Text $5.00 $1.25 $10.00 Full details and rate limits are available on the model page. Use gpt-image-2 in the API for production image generation workflows, or in Codex when you want to crea...

  • [25] Gemini Developer API pricingai.google.dev

    [] Image output is priced at $30 per 1,000,000 tokens. Output images up to 1024x1024px consume 1290 tokens and are equivalent to $0.039 per image. Gemini 2.0 Flash-Lite gemini-2.0-flash-lite Warning: Gemini 2.0 Flash-Lite is deprecated and will be shut down...

  • [26] Nano Banana image generation - Google AI for Developersai.google.dev

    import { GoogleGenAI } from "@google/genai"; import as fs from "node:fs"; async function main() { const ai = new GoogleGenAI({}); const prompt = 'An office group photo of these people, they are making funny faces.'; const aspectRatio = '5:4'; const resoluti...