The 5-10x annual cost reduction is encouraging but it sidesteps one problem. If your provider is silently quantising the model to hit those savings, the capability level you're benchmarking against isn't what you're actually getting. Synthetic open-sourced an eval tool that found a 34% failure rate on model identity checks across competing providers. The cost per capability drops fast on paper, less so when you account for what's actually being served. Wrote about the full incentive structure here: https://sulat.com/p/the-real-cost-of-cheap-ai-inference
This 5-10x annual cost drop tracks with what I'm seeing in practice. I switched my AI agent from Opus to Haiku three months ago - not as a theoretical exercise, but because I hit my weekly spending limit and got charged an extra 50 euros.
The counterintuitive finding: Haiku with well-defined task boundaries actually performed better than Opus on 90% of workflows. The cost dropped 15x. Quality held or improved.
Your point about distillation and algorithmic improvements is the mechanism behind this. But I think there's a practical layer missing from the analysis - most teams overspend because they route everything to the biggest model, not because frontier costs are too high.
The 5-10x annual cost reduction is encouraging but it sidesteps one problem. If your provider is silently quantising the model to hit those savings, the capability level you're benchmarking against isn't what you're actually getting. Synthetic open-sourced an eval tool that found a 34% failure rate on model identity checks across competing providers. The cost per capability drops fast on paper, less so when you account for what's actually being served. Wrote about the full incentive structure here: https://sulat.com/p/the-real-cost-of-cheap-ai-inference
Tao Lin?
This 5-10x annual cost drop tracks with what I'm seeing in practice. I switched my AI agent from Opus to Haiku three months ago - not as a theoretical exercise, but because I hit my weekly spending limit and got charged an extra 50 euros.
The counterintuitive finding: Haiku with well-defined task boundaries actually performed better than Opus on 90% of workflows. The cost dropped 15x. Quality held or improved.
Your point about distillation and algorithmic improvements is the mechanism behind this. But I think there's a practical layer missing from the analysis - most teams overspend because they route everything to the biggest model, not because frontier costs are too high.
Wrote up the full economics of running an agent on different model tiers: https://thoughts.jock.pl/p/claude-model-optimization-opus-haiku-ai-agent-costs-2026