Comparing token economics across 42 AI models

Neon compared 42 AI Gateway models on a synthetic workload of 100 customer-support tickets, measuring cost, token use, completion time, and whether responses met seven structured quality requirements involving JSON validity, classification, policy compliance, escalation, wording, and prohibited claims. GPT-5 Nano delivered the lowest cost per usable response and completed the workload in about three minutes, while GPT-5.3 Codex achieved the highest pass rate at 82%; Llama 3.1 8B Instruct was fastest at 1 minute 18 seconds but passed only 34 tickets, and GPT-5.5 Pro was the most expensive at roughly $8.34 and took 57 minutes. Results also showed that Qwen3.5 122B-A10B consumed the most tokens, while open-weight models had substantially lower median costs than proprietary models but somewhat lower median pass rates. The benchmark used isolated Neon branches for each model, a Python runner to submit and grade requests, Postgres for results, and a Next.js web application for reporting, with scheduled reruns planned. The authors emphasize that findings apply only to this workload and reflect real-world variability, but estimate that model selection could create an 86-fold difference in agent inference spending for organizations operating at large token volumes.

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