The Spring Prompt Blog

How We Built ROASBench to Feel Like Real Growth Work

ROASBench was built from operator experience, not benchmark theater. We designed it to feel like real performance marketing: stateful, constrained, path-dependent, and economically unforgiving.

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Claude vs Gemini vs GPT in a 12-Month Marketing Simulation

ROAS Bench is one of the clearest examples of where frontier models diverge in practice: not on prose quality, but on economic compounding.

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LiteLLM alternatives for 2026

If you’re looking for LiteLLM alternatives, you’re usually trying to solve one of two problems: * you need a Python library that makes it easy to switch between LLM providers * you need an AI gateway / routing layer that handles fallbacks, caching, observability, and control That split matters, because the best LiteLLM alternative depends on which problem you actually have. Recent context: On March 24, 2026, LiteLLM disclosed a supply-chain incident affecting malicious PyPI releases 1.82.7 

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Why Most LLMs Still Can't Run Growth

ROAS Bench shows that growth is not a copywriting problem. It is a compounding systems problem, and most models still break the system faster than they improve it.

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The AI Marketing Benchmark That Punishes Plausible-Sounding Strategy

Most LLMs can sound like a competent growth lead for one turn. ROAS Bench is interesting because it makes them live with the consequences for twelve months.

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Gemini Embedding 2 Just Launched - So We Benchmarked It

Google launched gemini-embedding-2-preview on March 10, 2026 as its first multimodal embedding model, with one shared embedding space for text, images, video, audio, and PDFs. Google specifically positions it for cross-modal semantic search, document retrieval, and recommendation-style similarity tasks. That made it a pretty obvious model to test on two things we care about a lot at Spring Prompt: 1. RAG over mixed-media documents 2. Search flows where users combine text and images So inste

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The Great AI Gifting Showdown: Which Model Should You Trust for Christmas Shopping?

It’s that time of year again. You’re out and about, the clock is ticking, and you still haven't found the perfect gift for your partner, your roommate, or that difficult-to-shop-for in-law. Naturally, many of us are turning to AI chatbots to brainstorm ideas. But not all AIs are created equal when it comes to the nuances of gift-giving. Does ChatGPT understand "thoughtfulness"? Can Claude actually predict what your brother wants, or just what he needs? We ran a rigorous test using Spring Promp

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Google Gemini 3 Review: The Benchmarks Actually Match the Hype 🤯

So, on Tuesday Google launched Gemini 3. The hype was massive leading up to this, and honestly? It is justified. It is really, really good. Trying to explain how good is difficult without getting bogged down in technical jargon, but the general consensus is pretty clear. Even Sam Altman tweeted his congratulations last night, calling it a "great model." When the head of the competition is being that humble, you know something big just happened. If you watched the GPT 5.1 launch last week, you

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GPT-5.1 First Look: Smarter, Warmer… But Not a Breakthrough

2025’s flagship model season kicked off yesterday with the unexpected arrival of GPT-5.1, with OpenAI getting their release out before Gemini 3. While we’re still waiting for API access (and therefore can’t run proper, high-volume benchmark testing yet), we can take a close look at the release notes, early examples, and some small-scale hands-on tests within ChatGPT. Here are my early impressions - what actually improved, how it compares to the wider market, and whether I think most teams shoul

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How to prepare for Gemini 3 + GPT 5.1

Here we go again: new-flagship season. Google’s Gemini 3 has been peeking through A/B tests in AI Studio and docs watchers have noticed model lifecycle shuffles, while OpenAI is lining up a GPT-5.1 family (base, Reasoning, and Pro). None of this is a formal launch note you can pin your roadmap to—but it’s enough signal to prepare. Treat it like a weather alert rather than a calendar invite.  What should you actually expect? Broadly: bigger context, stronger multimodality (esp. vision + code), a

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