<p>I wrote about this blog's <a href="/ai-generate-blog-images.html">automated image generation</a> previously. The short version: <code>generate-images.mjs</code> runs before each build, finds posts with missing images, builds a prompt from the <code>imgprompt</code> front matter field (or has <code>gpt-4o-mini</code> summarize the post), then calls DALL-E 3 to produce a 1024x1024 image.</p>
<p>It works, but DALL-E 3 is frustrating. It ignores prompt details, can't make a white background to save its life, and the results look more "AI art" than "clean icon." Time to see what else is out there.</p>
<p><em>Lesson Learned</em>: Don't let Cursor/Claude look online to find the most recent model versions. It got it wrong twice. I had to manually go back and update it after noticing that Gemini's 2.0-flash was not the newer Nano Banana (2.5, 3).</p>
<h1 id="top-models-today">Top Models Today</h1>
<p>This space moves fast. As of February 2026, these are the top 3 image generation models on the <a href="https://arena.ai/leaderboard/text-to-image">LM Arena text-to-image leaderboard</a> that have public APIs. These rankings are based on blind human preference voting and will almost certainly be different by the time you read this.</p>
<p><strong>ELO Scores:</strong> ELO is a rating system where users are shown two AI-generated images side by side (without knowing which model made which) and pick the one they prefer. Wins and losses shift each model's score, so a higher ELO means the model's images are consistently preferred by real people.</p>
<table>
<thead>
<tr>
<th>Model</th>
<th>Provider</th>
<th>Released</th>
<th>ELO</th>
<th>Notes</th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>GPT Image 1.5</strong></td>
<td>OpenAI</td>
<td>December 2025</td>
<td>1248</td>
<td>4x faster than GPT Image 1. Token-based pricing, 20% cheaper than its predecessor.</td>
</tr>
<tr>
<td><strong>Nano Banana Pro</strong></td>
<td>Google (Gemini 3 Pro Image)</td>
<td>November 2025</td>
<td>1237</td>
<td>Native Gemini image gen. Strong multimodal integration.</td>
</tr>
<tr>
<td><strong>Flux 2 Max</strong></td>
<td>Black Forest Labs</td>
<td>November 2025</td>
<td>1169</td>
<td>32B parameter model. Megapixel-based pricing. BFL's highest quality tier.</td>
</tr>
</tbody>
</table>
<p>GPT Image 1.5 and Nano Banana Pro are close in quality. Flux 2 Max sits ~80 ELO points behind them but is the strongest non-Google non-OpenAI option available.</p>
<p><strong>What about open source?</strong> Flux 2 Dev is open-weight and Stable Diffusion 3.5 is fully open source, but neither is practical for this use case. Running them on a MacBook Air M2 (8GB unified memory, passive cooling) means throttling after ~90 seconds, a max practical resolution of 512x512, and needing to close basically every other app first. The quality gap vs. the API models is real too. For a build-time script that needs to reliably produce clean images, cloud APIs win.</p>
<h1 id="setup-gpt-image-15">Setup: GPT Image 1.5</h1>
<p>This is the easiest since the blog already uses OpenAI.</p>
<ol>
<li><strong>API Key</strong>: Already set up. Same <code>OPENAI_API_KEY</code> used for DALL-E 3.</li>
<li><strong>Model change</strong>: Update the <code>model</code> parameter from <code>dall-e-3</code> to <code>gpt-image-1.5</code> in the images API call.</li>
<li><strong>API differences</strong>: GPT Image 1.5 supports both the Images API (<code>/v1/images/generations</code>) and the newer Responses API. The Images API is the closest drop-in replacement. Quality can be set to <code>low</code>, <code>medium</code>, <code>high</code>, or <code>auto</code>. It also supports streaming partial renders and up to 16 reference images for editing.</li>
</ol>
<pre><code class="language-bash">export OPENAI_API_KEY="sk-..."
</code></pre>
<p>If you don't already have a key, create one at <a href="https://platform.openai.com/api-keys">platform.openai.com/api-keys</a>.</p>
<h1 id="setup-nano-banana">Setup: Nano Banana</h1>
<p>Nano Banana is Google's image generation feature built on Gemini. You can access it through the official Google Gemini API.</p>
<ol>
<li><strong>Get an API key</strong>: Go to <a href="https://aistudio.google.com/app/apikey">Google AI Studio</a> and create a new API key.</li>
<li><strong>Install the SDK</strong>:
<pre><code class="language-bash">npm install @google/genai
</code></pre>
</li>
<li><strong>Set the environment variable</strong>:
<pre><code class="language-bash">export GEMINI_API_KEY="your-key-here"
</code></pre>
</li>
<li><strong>API usage</strong>: Nano Banana image gen uses the Gemini API with <code>responseModalities: ["TEXT", "IMAGE"]</code>. The model name for the standard tier is <code>gemini-2.5-flash-preview-image</code> and for Pro it's <code>gemini-3-pro-image</code>.</li>
</ol>
<p>Note: the API flow is different from OpenAI. You send a chat completion-style request with image output enabled, and the response includes inline image data.</p>
<h1 id="setup-flux-2-max">Setup: Flux 2 Max</h1>
<p>Black Forest Labs runs their own API platform.</p>
<ol>
<li><strong>Create an account</strong>: Register at <a href="https://dashboard.bfl.ai">dashboard.bfl.ai</a> and confirm your email.</li>
<li><strong>Add credits</strong>: Navigate to API → Credits and add funds via Stripe. Pricing is simple: 1 credit = $0.01 USD.</li>
<li><strong>Create an API key</strong>: Go to API → Keys, click "Add Key", and copy it immediately (it's only shown once).</li>
<li><strong>Set the environment variable</strong>:
<pre><code class="language-bash">export BFL_API_KEY="your-key-here"
</code></pre>
</li>
<li><strong>API usage</strong>: POST to <code>https://api.bfl.ai/v1/flux-2-max</code> with your prompt, width, and height. The API is async — you submit a task and poll for the result using the returned task ID.</li>
</ol>
<h1 id="setup-verification">Setup Verification</h1>
<p>Once you've configured your API keys, you can verify everything is wired up correctly by running:</p>
<pre><code class="language-bash">node apps/blog/blog/scripts/generate-images.mjs --verify
</code></pre>
<p>This will check that each configured API key is valid and can reach its respective service without actually generating any images. We'll implement this flag in a follow-up change.</p>
<h1 id="cost">Cost</h1>
<p>All prices are USD per image at 1024x1024.</p>
<table>
<thead>
<tr>
<th>Model</th>
<th>Cost</th>
<th>Pricing Model</th>
<th>Notes</th>
</tr>
</thead>
<tbody>
<tr>
<td>DALL-E 3 (prior)</td>
<td>~$0.04</td>
<td>Per image</td>
<td>Fixed price, single quality tier</td>
</tr>
<tr>
<td>GPT Image 1.5</td>
<td>$0.009–$0.14</td>
<td>Per token (output)</td>
<td>Varies by quality: low ~$0.009, medium ~$0.03, high ~$0.14. ~20% cheaper than GPT Image 1</td>
</tr>
<tr>
<td>Nano Banana</td>
<td>~$0.02</td>
<td>Per image</td>
<td>Standard tier (Gemini 2.5 Flash). Pro tier is ~$0.10/image</td>
</tr>
<tr>
<td>Flux 2 Max</td>
<td>~$0.07</td>
<td>Per megapixel</td>
<td>Scales with resolution. 1024x1024 ≈ 1MP ≈ 7 credits</td>
</tr>
</tbody>
</table>
<p>The ambiguity here is mostly around GPT Image 1.5 — the token-based pricing means cost depends heavily on the quality setting and prompt length. For a fair comparison we'll use "medium" quality, which lands close to what we're paying for DALL-E 3 today. Flux 2 Max's megapixel pricing is predictable but gets more expensive at higher resolutions.</p>
<h1 id="results">Results</h1>
<p>Each model was given the same prompt, built from <code>imgprompt: "A cute robot wearing a short chefs hat and holding a paint brush"</code>:</p>
<pre><code class="language-text">Subject: "A cute robot wearing a short chefs hat and holding a paint brush".
Style: minimalist flat vector icon, clean lines, crisp edges, simplified geometric shapes.
Colors: black, white, and one primary accent color only. No gradients.
Composition: centered subject, generous negative space, wide landscape 16:9 aspect ratio.
Background: pure solid white (#FFFFFF), filling the entire image.
No text, no shadows, no textures, no transparency, no background elements.
</code></pre>
<p>All images were center-cropped to 533x300 (16:9) after generation.</p>
<h2 id="gpt-image-15-openai">GPT Image 1.5 (OpenAI)</h2>
<p>Model: <code>gpt-image-1.5</code>, size: 1024x1024, quality: medium. Estimated cost: ~$0.03.</p>
<p><img src="/images/ai-image-gen-bakeoff-openai.png" alt="GPT Image 1.5 result"></p>
<h2 id="gemini-20-flash-google">Gemini 2.0 Flash (Google)</h2>
<p>Model: <code>gemini-2.0-flash-exp-image-generation</code>. Estimated cost: ~$0.02 (free tier may apply).</p>
<p><img src="/images/ai-image-gen-bakeoff-gemini-2.0.png" alt="Nano Banana 2.0 result"></p>
<h2 id="nano-banana-gemini-25-flash">Nano Banana (Gemini 2.5 Flash)</h2>
<p>Model: <code>gemini-2.5-flash-image</code>. Estimated cost: ~$0.02.</p>
<p><img src="/images/ai-image-gen-bakeoff-gemini-2.5.png" alt="Nano Banana 2.5 Flash result"></p>
<h2 id="nano-banana-pro-gemini-3-pro">Nano Banana Pro (Gemini 3 Pro)</h2>
<p>Model: <code>gemini-3-pro-image-preview</code>. Estimated cost: ~$0.10.</p>
<p><img src="/images/ai-image-gen-bakeoff-gemini-3-pro.png" alt="Nano Banana 3 Pro result"></p>
<h2 id="flux-2-max-bfl">Flux 2 Max (BFL)</h2>
<p>Model: <code>flux-2-max</code>, size: 1024x576. Estimated cost: ~$0.04 (~0.6MP at $0.07/MP).</p>
<p><img src="/images/ai-image-gen-bakeoff-flux.png" alt="Flux 2 Max result"></p>
<h1 id="conclusion">Conclusion</h1>
<p>It's pretty close between OpenAI and Nano Banana 2.5. I'll likely switch back and forth between the two, but to pick a flat winner as default I'm going with OpenAI for now.</p>
<p>OpenAI has that annoying same-y generic feel that lets you know it's AI art, and I don't love that, but it's also definitely more cute.</p>
<p>Nano Banana's ones are pretty original. I like 3-pro the most but not the price. 2.5-flash is fun, a bit cleaner than OpenAI's art, just kind of <em>busy</em>. I'll probably pick it when I'm aiming for cleaner images than cute ones.</p>
<p>Flux's was more artsy. It's just kind of... weird? It kept drawing unique stuff but just sort of unsettling.</p>


<p>Making blog thumbnails and images is tedious. I don't want to fight with Gimp or whatever and I don't think anyone cares what the image really is.</p>
<p>To make publishing easier, this site now checks for the image in each markdown post during the build to see if it already exists. If not, a prompt is built from the post title and content and sent to OpenAI's image API. The returned art is saved alongside the post and a 70×70 thumbnail is produced using Sharp.</p>
<p>To make the image for this post it cost me about $0.12 CAD in OpenAI credits. Entertainingly that's probably more than what hosting this site will cost for like a week due to the setup I'm using to host this.</p>
<p>This automation was initially coded with Codex, but it didn't work quite right. I fixed it up with Cursor, then a bit manually to get it across the finish line. This was as much about playing with the AI dev tools as building the feature.</p>
<p>Check it out here <a href="https://github.com/kylep/multi/blob/main/apps/blog/blog/scripts/generate-images.mjs">generate-images.mjs</a>.</p>
<h1 id="how-it-works">How It Works</h1>
<p>The image generation system is integrated into the build pipeline through the <code>package.json</code> build script:</p>
<pre><code class="language-json">"build": "node scripts/generate-images.mjs &#x26;&#x26; next build"
</code></pre>
<p>Before Next.js builds the static site, the <code>generate-images.mjs</code> script runs and:</p>
<ol>
<li><strong>Scans all markdown files</strong> in <code>markdown/posts/</code> for front matter</li>
<li><strong>Checks for missing images</strong> referenced in the <code>image</code> and <code>thumbnail</code> fields</li>
<li><strong>Generates missing images</strong> using OpenAI's DALL·E API if <code>OPENAI_API_KEY</code> is set</li>
<li><strong>Creates thumbnails</strong> by resizing generated images to 70×70 pixels using Sharp</li>
</ol>
<h2 id="the-generation-process">The Generation Process</h2>
<p>It's all pretty straightforward:</p>
<p>High level script logic:</p>
<ol>
<li>If there's no image defined, do nothing</li>
<li>If the image is defined in the front matter but doesn't exist, generate it.</li>
<li>If the image does exist but thumbnail doesn't, resize it and save that too.</li>
</ol>
<p>Generating the image is also simple:</p>
<ol>
<li>If the front matter has <code>imgprompt</code> in it, use that as the <code>summary</code> of the post.</li>
<li>If <code>imgprompt</code> is not present get <code>gpt-4o-mini'</code> to write a <code>summary</code> of the post.</li>
<li>Use <code>dall-e-3</code> to create the image.</li>
<li>Complain that it didn't look right, tweak the prompt, repeat until sufficiently frustrated, move on.</li>
</ol>
<h3 id="prompts">Prompts</h3>
<p>At time of writing (I won't keep this updated, check the code), I used these for the prompts:</p>
<p>Making the summary:</p>
<pre><code class="language-javascript">async function summarizeBlogContent(data, content) {
  const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });
  const prompt = await openai.chat.completions.create({
    model: 'gpt-4o-mini',
    messages: [{ role: 'user', content: `In max 20 words, describe a simple logo image that would represent this blog post: ${content}` }],
  });
  const summary = prompt.choices[0].message.content;
  console.log(`✨📝 Created summary of "${data.title}" → ${summary}`);
  return summary;
}
</code></pre>
<p>Making the image</p>
<pre><code class="language-javascript">async function getPromptFromContent(data, content) {
  const subject = data.imgprompt
    ? data.imgprompt
    : await summarizeBlogContent(data, content);
  const prompt = [
    "Simple minimalist flat vector icon.",
    `Subject: "${subject}".`,
    "Style: clean flat SVG-style vector, crisp edges, no gradients, no shadows, no textures, no lighting effects.",
    "Colors: limited palette, high contrast, modern minimal. Black, white, and primary colors only.",
    "Composition: centered subject with generous whitespace, 1:1 icon framing.",
    "Background: pure solid white (#FFFFFF) and completely filled. Nothing around the logo.",
    "No transparency. No checkerboard. No grid. No background elements.",
    "No text. No words. Just a simple image centered in white space."
  ].join("\n");
  return prompt;
}
</code></pre>
<h3 id="environment-setup">Environment Setup</h3>
<p>Set your OpenAI API key</p>
<pre><code class="language-bash">export OPENAI_API_KEY="your-api-key-here"
</code></pre>
<p>Without this key the build just skips image generation with a warning.</p>
<hr>
<h1 id="outstanding-problem">Outstanding Problem</h1>
<p>Dalle-3 is super frustrating when it comes to just making a white background. I spent a couple hours trying different prompts and it just refuses. Here's what the code came up with as a prompt for this page:</p>
<pre><code class="language-text">Simple minimalist flat vector icon.
Subject: "A simple logo featuring a stylized laptop and a paintbrush, symbolizing effortless content creation and automated design.".                     
Style: clean flat SVG-style vector, crisp edges, no gradients, no shadows, no textures, no lighting effects.                                                                                                
Colors: limited palette, high contrast, modern minimal. Black, white, and primary colors only.   
Composition: centered subject with generous whitespace, 1:1 icon framing.                             
Background: pure solid white (#FFFFFF) and completely filled. Nothing around the logo.                
No transparency. No checkerboard. No grid. No background elements.                                    
No text. No words. Just a simple image centered in white space. 
</code></pre>
<p>It does not seem to care for my preference. If anyone has any tricks for this I'd love to hear about it.</p>
<p><em>Update: The white background frustration eventually led to a <a href="/ai-image-gen-bakeoff.html">bakeoff comparing the best image generation APIs</a>.</em></p>


Kyle Pericak

"It works in my environment"

Categories

Tags