How I Made the Cheapest Model Match the Best — at 1/640th the Cost
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I built a memory system that lets Claude Haiku (the $1/M-token model) answer questions with 100% accuracy — tying Claude Opus (the $5/M-token model) running with the entire knowledge base in context. Haiku + my memory costs $0.10 per thousand questions . Opus + full context costs $64.04 . Same accuracy. 640x cheaper. Both models score 0% without memory. The facts are synthetic — there's nothing in their training data to fall back on. The memory is the entire difference. I'm open-sourcing the…
1Key Takeaways
- I built a memory system that lets Claude Haiku (the $1/M-token model) answer questions with 100% accuracy — tying Claude Opus (the $5/M-token model) running with the entire knowledge base in context.
- Haiku + my memory costs $0.10 per thousand questions .
- Both models score 0% without memory.
- The facts are synthetic — there's nothing in their training data to fall back on.
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that i built a memory system that lets Claude Haiku (the $1/M-token model) answer questions with 100% accuracy — tying Claude Opus (the $5/M-token model) running with the entire knowledge base in context.
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