GLM-5.2 detailed benchmark numbers — SWE-Bench Pro 62.1 vs GPT-5.5 58.6, FrontierSWE 74.4% (nearly matching Claude Opus 4.8 at 75.1%), API pricing 6.8x cheaper than GPT-5.5
GLM-5.2's headline benchmark numbers — SWE-Bench Pro 62.1 (vs GPT-5.5 at 58.6), FrontierSWE 74.4% (vs Claude Opus 4.8 at 75.1%, GPT-5.5 at 72.6%), API pricing $1.40 input / $4.40 output per 1M tokens — empirically position the open-weight model at production-coding capability parity with closed frontier models at 6.8x cheaper economics.
The substantive piece is the capability-economics inversion on production coding workloads. GLM-5.2 doesn't just match closed-source frontier capability on SWE-Bench Pro and FrontierSWE — it does so at API pricing that's 6.8x cheaper on output tokens than GPT-5.5. The economics inversion means enterprise coding-procurement decisions that previously defaulted to closed-source for capability-leadership reasons now face a clean alternative on both axes.
The competitive read against this morning's GLM-5.2 announcement coverage is that the detailed numbers compound the strategic significance. The cost-per-task economics of frontier-tier coding capability have shifted enough that closed-source vendors face genuine commodity-pricing pressure on this workload category specifically. Anthropic's data-center financing arrangement reads partly as defense against this exact economics-inversion pressure.
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