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🧬 Model Families

MAI models follow a unified naming convention:

MAI M{version} {Specialization} {Variant}
MAI {version} {Variant}
MAI C{version} {Variant}
MAIGEN {version} {Specification}
MAIMIND {version} {Specification}
MAITTS {version} {Specification}
MAIEDITOR {version}.{Date of release} {Update feature name}

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⚡ Variant Breakdown

Rule of thumb: If you need an answer now — use Fast. If you need the right answer to a hard problem — use Thinking.

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📋 Full Model Registry

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📐 The MAI Math — Formulas & Coefficients

1️⃣ Power Multiplier Formula

Every MAI model's effective performance boost is calculated using:

                    MCE² × 8
Power (×)  =  ─────────────
                  9.3 × 2

Or simplified:

Power = (MCE² × 8) / 18.6

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2️⃣ MCE Progression Across Generations

MCE grows with each MSPLIT generation following a square-root scaling law:

MCE(n) = √(2.5 × n)

Where n = MSPLIT generation number.

📈 Insight: Power scales linearly with MSPLIT generation because MCE² = 2.5n, so Power = (2.5n × 8) / 18.6 ≈ 1.075n. Each new generation adds roughly +1.08× to the multiplier.

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3️⃣ Context Window Scaling

Context length doubles with each major version:

Context(v) = 64K × 2^v

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4️⃣ Effective Intelligence Index (EII)

To compare models holistically, we use the EII — a single score combining power and context:

EII = Power(×) × log₂(Context / 1K)

🎯 Notice the pattern? EII ≈ 4.3 × n × (n + 6) / 10 — it grows quadratically, meaning each generation is dramatically more capable than the last. Models like M5 will use: 64 / 9.3, without / 2

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5️⃣ Fast vs Thinking — Speed-Depth Tradeoff

                    Base Latency
Fast Latency   =  ─────────────
                     Power(×)

Thinking Latency = Base Latency × Thinking Depth Factor (TDF)

Where TDF typically ranges from 3× to 8× depending on problem complexity.

💡 When to switch? If Fast gives a confident answer → stay with Fast. If it hedges or the task involves multi-step reasoning → switch to Thinking.

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🔬 MSPLIT Technology — How It Works

┌──────────────┐     ┌──────────────┐     ┌──────────────┐
│  Base Model │     │  Base Model │     │  Base Model │
│      A      │     │      B      │     │      C      │
└──────┬───────┘     └──────┬───────┘     └──────┬───────┘
      │                   │                   │
      └─────────────┬───────┘────────────────────┘
                   │
            ┌───────▼────────┐
            │  PEREX MERGE  │  ← Weighted parameter fusion
            │   Pipeline    │
            └───────┬────────┘
                   │
            ┌───────▼────────┐
            │   MSPLIT nA   │  ← Split-verify-remerge (n passes)
            │  Optimization │
            └───────┬────────┘
                   │
            ┌───────▼─────────┐
            │  Final Merged  │
            │     Model      │  → MCE = √(2.5 × n)
            └─────────────────┘

MSPLIT (Multi-Stage Parameter Splitting) works in three phases:

1. Merge — Multiple base models are fused using the Perex Merge weighted-average pipeline
2. Split — The merged weights are split into parameter subgroups and independently evaluated
3. Re-merge — Only the highest-performing parameter configurations survive and are re-merged

Each MSPLIT generation (3A → 4A) adds an additional split-verify pass, increasing MCE and therefore the power multiplier.

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🛡️ Access & Licensing

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