在禁用第三方库和最小化提示词的测试条件下，Claude Opus 5和GPT-5.6的性能差异得以显现。[1]一项代码bug检测测试中，两个模型都成功找到了6个预设的bug，包括比较运算符错误、浮点精度污染、折扣优惠规则混淆、重复优惠券叠加、缺少价格下限保护和运费计算错误。[1]但Opus 5的表现更为深入——它额外发现了运费计算中的浮点精度问题，即3.0000000000000004经过math.ceil取整后的溢出风险。[1]
在第二项测试中，两个模型被要求从4秒、44100Hz采样率、16位PCM格式的单声道WAV文件（包含176400个采样点）生成波形图，且禁用所有外部库。[1]两个模型都超预期完成了任务，但方案效率存在差异——Opus 5采用了struct.unpack的批量解包方法，相比GPT-5.6逐个调用unpack_from的方式更加高效。[1]此外，Opus 5在解决方案中展现了更强的产品思维。[1]
A comparative evaluation of Claude Opus 5 and GPT-5.6 reveals notable performance differences when both models operate without third-party tools and with minimal system prompting.[1]
In the first test, both models were tasked with independently identifying six bugs in e-commerce discount logic without explicit hints.[1] While both models successfully detected all six primary issues—including a threshold comparison error (greater-than versus greater-than-or-equal), a discount stacking vulnerability, a missing price floor protection, and inconsistencies between original and discounted prices in fulfillment calculations—Opus 5 demonstrated superior analytical depth.[1] Specifically, Opus 5 uncovered an additional floating-point precision error in the shipping fee calculation, where the value 3.0000000000000004 was incorrectly rounded up by the math.ceil function.[1]
In the second test, both models were required to generate waveform visualizations from a WAV audio file with all external libraries disabled.[1] The test used a 4-second audio file sampled at 44,100 Hz with 16-bit PCM encoding in mono, containing 176,400 sample points.[1] Both models successfully completed the task despite constraints, but Opus 5 employed a more efficient approach using struct.unpack for batch unpacking, whereas GPT-5.6 relied on sequential unpack_from operations.[1] This choice reflected stronger product-oriented thinking in Opus 5's implementation.[1]
The results suggest that after significant streamlining of its system prompt—reducing the Claude Code instructions by 80 percent—Opus 5 has become a more capable model.[1]
