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// 2h agoBENCHMARK RESULT
GLM-5.2 benchmark reveals over-thinking issue
An overnight benchmark run comparing GLM-5.2, GPT-5.5, and Opus 4.8 suggests GLM-5.2 faces an over-thinking problem. The model consumes far more tokens than competitors to complete similar tasks while achieving lower accuracy, raising concerns about its cost-effectiveness.
// ANALYSIS
Hot take: Over-thinking is a hidden tax on reasoning models that offsets the pricing benefits of lower base rates.
- –Token Bloat: Excessive reasoning steps generate significantly higher token volume, increasing total cost per API call.
- –Lower Accuracy: The extra compute and token usage did not result in better output quality, underperforming relative to the comparison models.
- –True Cost Metric: Cost-effectiveness should be evaluated by cost-per-successful-task rather than base token pricing.
// TAGS
glm-5.2gpt-5.5opus-4.8benchmarksllm-costreasoning-models
DISCOVERED
2h ago
2026-06-28
PUBLISHED
2h ago
2026-06-28
RELEVANCE
8/ 10
AUTHOR
morganlinton