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Why the AI Agent Runtime Matters as Much as the Model You Choose

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A developer experiment using DeepSeek V4 Flash across two different agent runtimes — Codex and Claude Code — produced notably different outcomes on the same long, cross-file coding task. The findings suggest that a model's effectiveness is shaped not just by its raw capability but by the full runtime stack, including protocol, tools, context handling, and how task completion is defined. DeepSeek has publicly acknowledged adapting V4 Flash for Codex and documented Responses API support, signaling a strategic focus on runtime compatibility. The author proposes that effective agent performance equals model potential multiplied by a 'harness realization rate,' which accounts for protocol matching, tool reliability, and recovery quality. The key takeaway is that fair model comparisons require fixing all runtime variables — and results observed without those controls should be treated as runtime-specific observations, not universal benchmarks.

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