⚖️ Comparison

Standard AI vs Turbo

Side-by-side comparison on real examples. See where Turbo's verification makes the difference between guessing and knowing.

Real Examples

Head-to-Head Comparison

🔢 "What is 847 × 293?"
Standard AI
"847 × 293 = 248,071"
❌ Incorrect (actual: 248,171)
Turbo AI ⚡
"847 × 293 = 248,171"
Verified: 847 * 293 === 248171 → true
✅ Verified (3/3 passed)
🧩 "If all roses are flowers and some flowers fade quickly, do all roses fade quickly?"
Standard AI
"Yes, since roses are flowers and flowers fade quickly, all roses fade quickly."
❌ Logical fallacy (affirming the consequent)
Turbo AI ⚡
"No. The premise states some flowers fade quickly, not all. Therefore we cannot conclude that all roses fade quickly."
✅ Verified (logic check passed)
💻 "Write a function to check if a string is a palindrome"
Standard AI
function isPalindrome(s) {
  return s === s.split('').reverse().join('');
}
⚠️ Untested — fails on "Racecar"
Turbo AI ⚡
function isPalindrome(s) {
  s = s.toLowerCase();
  return s === s.split('').reverse().join('');
}
✅ Tested: "Racecar" → true, "hello" → false
📊 "What percentage of 350 is 73.5?"
Standard AI
"73.5 is 21.5% of 350."
❌ Incorrect (actual: 21%)
Turbo AI ⚡
"73.5 is 21% of 350."
Verified: (73.5/350)*100 === 21 → true
✅ Verified
Accuracy

Accuracy by Category

Based on internal benchmarking by the Mikoshi team across 500+ test queries per category:

Category Standard AI Turbo AI ⚡ Improvement
🔢 Arithmetic
~78%
~99%
+21%
🧩 Logic
~72%
~95%
+23%
💻 Code Generation
~70%
~96%
+26%
📊 Data Analysis
~80%
~97%
+17%
🔤 Factual Recall
~85%
~92%
+7%
📊 About These Numbers

Accuracy is highest for objectively verifiable categories (math, code, logic) where verification code can definitively prove correctness. Factual recall shows smaller improvements because some facts can't be verified with code alone — they rely on the model's training data.

Tradeoffs

Speed vs Accuracy

Turbo trades response time for reliability. Understanding the tradeoff helps you choose when to use each mode:

⚡ Standard Mode
~1-3 seconds

Single generation, no verification. Fast, but accuracy depends on the model and prompt. Best for casual conversation, brainstorming, creative writing.

🔬 Turbo Mode
~10-30 seconds

3 candidates + verification + scoring. Slower, but dramatically more accurate. Best for calculations, code, data analysis, critical decisions.

The 3-10x slower response time is the cost of verification. In practice, this is comparable to a human double-checking their work — the extra time is the price of confidence.

Use Cases

When to Use Turbo

Turbo is most valuable in high-stakes domains where accuracy is non-negotiable:

💰
Finance
Financial calculations, risk analysis, portfolio modelling — where a wrong number means real money lost.
🔬
Research
Data analysis, statistical computations, literature synthesis — where precision affects conclusions.
⚙️
Engineering
Unit conversions, physics calculations, structural analysis — where errors have safety implications.
🏥
Medical
Dosage calculations, statistical analysis of clinical data — where accuracy is literally life-critical.
⚖️
Legal
Contract analysis, regulatory compliance, date calculations — where precision prevents liability.
📊
Data Science
Code generation, data transformations, algorithm correctness — where bugs compound silently.
When to Skip

When Standard AI Is Fine

Not everything needs verification. Standard AI is perfectly adequate for:

🎯 Rule of Thumb

If the answer can be wrong and it doesn't matter much, use standard. If a wrong answer could cost money, cause harm, or undermine trust, use Turbo. When in doubt, Turbo's verification overhead is minimal compared to the cost of acting on wrong information.

🧪 Real Benchmark: ARC-AGI

We tested the Turbo verification pipeline against ARC-AGI — François Chollet's benchmark designed to measure genuine intelligence, not pattern matching. These are real results from code-execution verified AI.

ARC-AGI 1 (Training)
100%
17/17 tasks solved
Baseline without verification: ~82%
+18pp improvement
ARC-AGI 2 (Evaluation)
87.5%
7/8 tasks solved
Baseline without verification: ~50%
+37.5pp improvement

Key Finding: Verification Value Scales with Difficulty

The harder the task, the more verification helps. On easy tasks (ARC-AGI 1), the base model already gets most right — verification adds +18pp. On hard tasks (ARC-AGI 2), the base model drops to 50% — verification adds +37.5pp. This is the architecture working as designed.

Architecture Value Formula
V = E × P × S
E — Error Rate
How often the base model gets it wrong
P — Precision
How reliably verification catches errors (1.0 for code execution)
S — Salvage Rate
How often a caught error gets corrected on retry
ARC-AGI 1 Value
E=0.18 × P=1.0 × S=1.0 = 0.18
ARC-AGI 2 Value
E=0.50 × P=1.0 × S=0.75 = 0.375

What Verification Actually Caught

Water Gravity Fill — 3 attempts to solve
First two attempts got gap-detection wrong. Verification caught mismatches in wall-bounded regions. Third attempt correct.
Shape Borders — 4-connected vs 8-connected
First attempt used 4-connected adjacency for borders. Verification caught it — needed 8-connected. Second attempt correct.
Cell Ejection — 4 attempts to converge
Required 4 iterations to distinguish convex tips from concave pockets — each revision driven by specific training example failures.
Triangular Reflection — Correctly rejected (94-99% accurate, not 100%)
After 5+ attempts, the model couldn't converge. Verification prevented submitting a wrong answer — better to skip than to be wrong. This is the architecture working as designed.
The Bottom Line
On the hardest public AI benchmark, verification nearly doubled accuracy (50% → 87.5%). Every error caught was a real bug in the model's reasoning — wrong adjacency, wrong direction, wrong boundaries. Code doesn't lie.

See the difference for yourself

⚡ Try Turbo in Synapse
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