We Were Paying 5x Too Much for Our AI. Here's How We Fixed It.
When I launched LLMtxtMastery, I made the same mistake every AI founder makes: I picked GPT-4o-mini because it was "good enough" and moved on.
Six months later, I finally looked at the numbers.
We were spending 5x more than we needed to.
Not because GPT-4o-mini is bad — it's excellent. But for our specific task (analyzing webpage content and returning structured JSON), we were paying for capabilities we literally never used:
- Multimodal understanding? Nope.
- Complex reasoning chains? Nope.
- 128K context window? We used 1K.
We were driving a Ferrari to the grocery store.
The Problem Nobody Talks About
Here's the dirty secret of AI product development: most teams never revisit their model choice.
You pick something in week one. It works. You ship. You scale. And that hasty decision compounds into thousands of dollars of unnecessary spend.
I ran the numbers on LLMtxtMastery:
- Current cost: $0.000248 per page analyzed
- Monthly volume: 50,000 pages
- Annual cost: ~$150
Not catastrophic. But here's what stung: I was planning to 10x our volume. That "not catastrophic" number was about to become very real.
The Fix: Right-Size Your Models
I spent a day doing what I should have done months ago — actually analyzing our LLM requirements:
| Capability | Do We Need It? |
|---|---|
| Structured JSON output | ✅ Yes |
| Instruction following | ✅ Yes |
| Content summarization | ✅ Yes |
| Complex reasoning | ❌ No |
| Coding ability | ❌ No |
| Multimodal | ❌ No |
| 128K context | ❌ No (we use 1K) |
We needed maybe 20% of GPT-4o-mini's capabilities.
So I tested alternatives. Three models. One hundred pages each. Scored on quality, speed, and cost.
The Results
Winner: Mistral Small 3.1 24B
| Metric | GPT-4o-mini | Mistral Small 3.1 | Change |
|---|---|---|---|
| Cost per page | $0.000248 | $0.000047 | -81% |
| Annual cost | $148.80 | $28.20 | -$120 |
| Quality score | 8.5/10 | 8.7/10 | +2% |
| Latency | 450ms | 380ms | -16% |
Read that again: 81% cheaper, 2% better quality, 16% faster.
The "safe" choice was the wrong choice.
Why This Matters
AI costs compound. At scale, model selection is the difference between profitable and underwater.
But most teams don't optimize because:
- It feels risky. What if the new model breaks something?
- It takes time. Who has bandwidth for LLM benchmarking?
- They don't know alternatives exist. The model landscape changes weekly.
This is exactly the problem I'm solving with ModelOptix.
Introducing ModelOptix
ModelOptix is "Mint for your LLM stack" — connect your OpenRouter account, and we'll:
- Analyze your actual usage patterns
- Identify where you're overpaying
- Recommend right-sized alternatives with real benchmarks
- Show you the math: what you'll save, what you might trade off
No guessing. No vibes. Just data.
The Bottom Line
That $120/year I saved on LLMtxtMastery? Multiply that across every LLM call in your product. Across every product in your portfolio.
The teams winning in AI aren't the ones with the biggest budgets. They're the ones who ruthlessly optimize their model choices.
Stop paying for capabilities you don't use.
→ Join the ModelOptix waitlist
We're launching soon. Early waitlist members get:
- First access when we launch
- Founding member pricing (locked in forever)
- Direct input on features
This is another post in my journey to build 50 AI-powered micro-businesses by 2030. Follow along on Twitter or LinkedIn.