The Spring Prompt Blog

Gemini 3.6 Flash Wins the Minute. Gemini 3.5 Flash-Lite Wins the Second.

Google's two new fast models split BulletBench's hardest latency tests: Gemini 3.6 Flash tops the 60-second board, while Gemini 3.5 Flash-Lite nearly wins Lightning without losing a single game on time.

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The AI That Won Our World Cup Benchmark Didn't Predict Best. It Read the Rules.

GLM-5.2 won World Cup Bench by two points while spending the fewest reasoning tokens and posting an ordinary hit rate. It found the one free lunch in our scoring rules and ate it 34 times. A story about Goodhart's law, benchmark design, and what AI optimisers actually optimise.

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Six Models, One Mind: What the World Cup Revealed About How AIs Actually Think

Across 34 World Cup fixtures, six frontier AI models from five different labs picked the same winner 31 times. Then we gave them memory of the tournament and they changed zero of 96 picks. The herd is real, and it has consequences.

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AI Is Now a Better Football Pundit Than the Pundit. It Still Lost to One Line of Code.

We had six frontier AI models predict all 32 knockout matches of the 2026 World Cup, blind. They out-called a top BBC pundit. Then a one-line rule, always back the higher-ranked team, beat every single one of them on the core calls.

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How to Continue a Claude Code Conversation in Codex After Hitting Your Usage Limit

Ran out of Claude Code allowance halfway through a task? Here is how to move the conversation into Codex and continue without losing the decisions behind the code.

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GPT-5.6 Luna Was Meant to Be Fast. It Still Lost to the Clock.

Luna arrived as the fastest GPT-5.6. Across 96 BulletBench games, it lost 71 on time. The result exposes the model the market still has not built: genuinely smart, sub-second and predictable.

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BulletBench: we made 23 AI models play speed chess, and the clock was the judge

Every leaderboard measures how smart a model is. None measure how smart it is per second. So we built one: AI models play speed chess on a real clock, and slow genius loses on time.

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Sonnet 5 gave the sharpest marketing analysis we've ever tested. It also lost money every single time.

A ROASBench case study in why fluent reasoning isn't the same as good decisions, and why for this model more thinking made it worse.

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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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