GLM scores more than GPT but how to test if benchmark is right?

I came across this model comparison on a benchmark, and the numbers are pretty interesting: GLM-5.3: 100% task success, 9.3/10 quality, 16.3s median TTFT, $0.28 total run cost GPT-5.5: 100%, 9.3/10, 13.2s TTFT, $1.43 Claude Haiku 4.5: 96%, 8.9/10, 0.9s TTFT, $0.0044/task Kimi K3: 96%, 9.5/10, 26.4s TTFT, $0.55 GPT-5.6 Luna: 79%, 8.3/10, 2.1s TTFT, $0.0023/task The cost difference is especially interesting. GLM-5.3 gets the same 100% task success as GPT-5.5 at roughly 1/5 of the total run cost. Haiku is in a completely different cost and latency category. But the methodology is different, so I’m not sure how much weight to put on these numbers. From what I understand, the benchmark uses 28 predefined tasks across coding, data handling, real-world tasks, security, and tool use. Every model gets the same tasks, and the outputs are evaluated using task-specific criteria rather than simply comparing generated text. The results are then reduced to a few metrics: task pass rate, a 0–10 quality score, time-to-first-token, and estimated inference cost. So I’d treat this as evidence The results are interesting enough to investigate, but I wouldn’t choose a production model from these numbers alone. For context, I build voice agents with an open-source platform, Dograh, using BYOK and kokoro and qwen. One question i am struggling with is how to investigate benchmark methodologies without giving lot of time and resources ?