Learn how SwimmBench quantifies the cost and time savings of using indexed code context with AI coding assistants. Benchmark tested on ~5M lines of code.

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Swimm published benchmark results from SwimmBench, an internal tool measuring how pre-indexed code context affects Claude Code performance on a ~5M line C++ codebase (ScummVM). Testing compared Swimm's Deep Index (pre-computed code understanding) against Claude Code's native exploration. Results show 75% faster response times and 61% lower token costs when using indexed context, primarily because the AI skips file traversal and grep operations. The benchmark measures cost, latency, quality, and tool usage across architectural questions and explanation tasks. Swimm acknowledges limitations: internal testing with inherent bias, single codebase, and no external validation. The post targets engineering leaders evaluating AI tooling ROI at scale.

75% faster. 61% cheaper. What Swimm does for Claude Code.

The problem: AI coding assistants are expensive and slow on large codebases

What we built: SwimmBench – a benchmarking tool for AI code tasks

The test: ScummVM – 4.87 million lines of production C++