When Does Prompt Caching Break Even? Token Math for Multi-Turn AI Coding
By Eric Bush · July 29, 2026 · 5 min read
Prompt caching can save 78% on multi-turn agent workflows. But it's not always worth it. Here's the exact break-even math.
Both Anthropic and OpenAI offer prompt caching: store a repeated prefix (system prompt, few-shot examples, codebase context) so subsequent turns read it from cache instead of reprocessing. Cache hits cost 10% of the base input price. But cache writes cost extra — so when does it actually save money?
The Basic Economics
| Operation | Cost (vs base input) | Opus 5 Rate |
|---|---|---|
| Normal input | 1.0x | $5.00/M |
| 5-min cache write | 1.25x | $6.25/M |
| 1-hour cache write | 2.0x | $10.00/M |
| Cache hit (read) | 0.1x | $0.50/M |
Worked Example: 50K Token System Prompt, 10 Turns
Scenario: AI coding agent with a 50K-token system prompt (project rules, codebase context, coding standards). 10 turns of interaction.
Without caching (Opus 5):
50K tokens × 10 turns × $5/M = $2.50 just for the repeated prefix.
With 5-min caching:
- Turn 1 (cache write): 50K × $6.25/M = $0.31
- Turns 2-10 (cache hits): 50K × 9 × $0.50/M = $0.23
- Total: $0.54
Savings: $2.50 - $0.54 = $1.96 (78% reduction)
Break-Even Points
| Cache Type | Write Premium | Break-Even | Best For |
|---|---|---|---|
| 5-minute TTL | +25% | 2 turns | Interactive coding sessions |
| 1-hour TTL | +100% | 4 turns | Long autonomous agent runs |
The math: 5-min cache costs 1.25x on turn 1 but saves 0.9x on every subsequent turn. After just 2 turns, you're ahead. The 1-hour cache costs 2.0x upfront but gives you a full hour of $0.50/M reads — you need 4 turns within that hour to break even.
Rules of Thumb
- Cache anything over 5K tokens that repeats across turns
- 5-min cache: profitable after 2 turns (use for interactive sessions)
- 1-hour cache: profitable after 4 turns (use for autonomous agents)
- Batch API + cache: stacks — a batched Opus 5 cache hit is just $0.25/M
- Don't cache if your prefix changes every turn (dynamic context that isn't stable)
When Caching Doesn't Help
- Single-turn requests (no repetition)
- Small prefixes under 2K tokens (savings are negligible)
- Rapidly changing context that invalidates cache every turn
- Workloads already on Batch API with no time pressure
Practical Optimization Tips
- Front-load stable context — put system prompt and rules at the start (cache prefix must be a contiguous prefix)
- Separate stable vs. dynamic — keep codebase context stable, put variable context after the cache boundary
- Monitor cache hit rates — if you're getting <50% hit rates, your prefix is changing too often
- Size your prefix deliberately — the sweet spot is 20K-100K tokens of stable context
For a typical 200-turn autonomous agent session with a 50K prefix, caching saves $45+ at Opus 5 rates. It's the single biggest optimization after choosing the right model tier.
Use our AI Cost Calculator to estimate your project's total token consumption, then apply caching math to see potential savings.
Want to calculate exact costs for your project?
Frequently Asked Questions
What is prompt caching for AI coding?
Prompt caching stores repeated context (system prompts, codebase context) so subsequent turns read from cache at 10% of normal input price instead of reprocessing. Available on Claude and GPT models.
When does prompt caching break even?
With 5-minute TTL (1.25x write cost): after 2 turns. With 1-hour TTL (2x write cost): after 4 turns. Any multi-turn coding session with a stable prefix of 5K+ tokens benefits.
How much can prompt caching save on AI coding?
For a 50K-token system prompt over 10 turns with Opus 5: caching reduces the prefix cost from $2.50 to $0.54 — a 78% savings. Over a full 200-turn session, that's $45+ saved.
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