GPT-5.6, Grok 4.5, Claude, and Muse Spark Build the Same 4 Apps Head-to-Head

Benchmark scores rarely predict how a model behaves when given a non-trivial product spec and told to ship. The build-off documented at tryai.dev does something more useful: it hands the same four app briefs to 12 models — including GPT-5.6, Grok 4.5, Claude, and Muse Spark — and records what comes out.

The format matters. Identical prompts, identical constraints, side-by-side output. That eliminates the usual confounds when engineers argue about which model to wire into their stack. You stop comparing marketing claims and start comparing artifacts.

For engineers choosing a code-generation backend, this kind of comparative output is more signal than most published evals. Evals measure what a model knows. A build-off measures whether it can follow a product brief, make defensible architecture choices, handle edge cases in generated code, and produce something that actually runs. Those are different skills, and models diverge sharply on them.

GPT-5.6 and Grok 4.5 are both recent releases, so their inclusion puts current frontier capability on the same canvas as Claude's established coding strengths. Muse Spark is the less-known entry; its presence in a 12-model field suggests the evaluation is covering a broader tier range than typical comparisons, which tend to cluster at the top.

For solo founders and small teams building on LLM APIs, the practical read is straightforward: pick the app category closest to your use case, look at what each model produced, and weight that over any single-number leaderboard score. Composition quality, error handling, and how gracefully a model fails on ambiguous specs will matter more than token throughput once you are in production.

The full comparison is available at tryai.dev. It is worth reading the methodology before drawing conclusions about any individual model.

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