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The Distillery

Optimize claude-opus cost with Claude Code

Opus is 5× more expensive than Sonnet per input token. Teams that use it for complex tasks can hit $3,600/month before realizing how fast sessions compound.

What claude-opus costs per session

Claude Opus pricing (as of April 2026) is $15.00 per million input tokens, five times the cost of Sonnet. The same 30-turn session that costs $4.50 on Sonnet costs $22.50 on Opus. Context accumulation works the same way on both models: every turn re-sends everything that came before it.

30 turns × 50,000 tokens = 1.5M input tokens/session
1.5M × $15.00/M = $22.50 per session
160 sessions/month = $3,600/month
After 20% Distillery reduction: $2,880/month (saving $720/month)

Why claude-opus bills compound

Developers reach for Opus when the task demands it, for complex architecture decisions, subtle debugging, long-horizon reasoning. These tasks produce longer sessions. Longer sessions accumulate more context. More context means more tokens re-sent on every subsequent turn. The premium model gets the hardest sessions, which are also the sessions that accumulate context fastest.

The combination is significant. A team running 160 Opus sessions per month does not have a small API bill. It has a $3,600/month problem. And because Opus is often switched on for specific high-stakes tasks, the cost is invisible until the invoice arrives.

How The Distillery reduces claude-opus spend

Context distillation applies regardless of which model you use. The Distillery intercepts every request, Sonnet, Opus, or Haiku, and strips redundant context before forwarding. Real-world cost reduction is 30-60% depending on session patterns. Because Opus sessions tend to be longer and more agentic, they contain more distillable material. The 20% benchmark figure is the conservative deterministic floor; long Opus sessions often see higher reductions.

At $3,600/month, a 20% reduction (conservative floor) is $720/month in direct API savings — real-world savings of 30-60% would scale proportionally. The proxy intercepts at the network layer, with no prompt changes, no model switching, no workflow adjustment required.

See the exact methodology: benchmark results →