GLM 5.2 costs most in VulcanBench

Raw API pricing is a deceptive metric for reasoning models with 'thinking' steps. While GLM 5.2 has low per-token pricing on paper, its agentic loops generate massive token volumes and high latency that make it less competitive for developer workflows.

• –**Thinking Token Inflation:** Reasoning-focused models like GLM 5.2 generate large numbers of internal thinking tokens, which rapidly accumulate costs during multi-turn coding agent tasks.
• –**Latency Bottleneck:** An average task execution time that is 3.3x slower is a major productivity blocker for developers expecting fast interactive loops in IDEs.
• –**Benchmark Realism:** The comparison on VulcanBench's 52 tasks highlights why real-world agent evaluations are necessary, as paper-thin token pricing does not map linearly to end-user costs.
• –**Enterprise Implications:** Teams standardizing on open-weights reasoning models for agentic pipelines need to monitor total session costs and execution times, not just baseline token input/output rates.