<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>


Kyle Pericak

"It works in my environment"

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