README.md

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title: README
emoji: ⚡
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colorTo: indigo
sdk: static
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🌌 Mythic Artificial Intelligence

by MythicGames

Building the next generation of merged language models

🌐 Visit our platform · 💬 Chat with MAI models · 📂 All Models

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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}

Component	Meaning	Examples
M{version}	Generation / major version	M1, M2, M3, M4
Specialization	Primary task focus	Coder, Chat, Reason, Vision
Variant	Speed / depth profile	Fast, Thinking

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⚡ Variant Breakdown

Variant	Philosophy	Latency	Depth	Best For
🟢 Fast	Speed-first. Minimal chain-of-thought, instant responses	🔽 Low	Standard	Code generation, quick Q&A, real-time chat
🟣 Thinking	Depth-first. Extended internal reasoning before answering	🔼 Higher	Deep CoT	Math, logic, complex analysis, research

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

Model	Specialization	Variant	MSPLIT	MCE	Power (×)	Context	Status
MAI M3 Coder Fast	Reasoning	Fast	3A	2.74	~3.2×	>1M	🟢 Active
MAI M3 Coder Thinking	Reasoning	Thinking	3A	2.74	~3.2×	>1M	🟢 Active
MAI M4 Coder Fast ⭐	Code	Fast	4A	3.16	~4.3×	>1M	🟢 Flagship
MAI M4 Coder Thinking	Code	Thinking	4A	3.16	~4.3×	>1M	🟢 Active
MAI M5 Coder Fast	Multimodal	Fast	4A	3.16	~4.3×	>1M	🔵 Coming Soon

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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

Variable	Full Name	Description
MCE	Merge Coefficient Exponent	Core efficiency metric of the merge. Higher = better synergy between merged weights
8	Base Parameter Scalar	Constant tied to the 8-expert routing in the merge pipeline
9.3	Normalization Factor	Empirical constant derived from benchmark calibration
2	Dual-pass Divisor	Accounts for the two-pass merge verification in MSPLIT

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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.

MSPLIT Gen	n	MCE = √(2.5n)	MCE²	Power (×)
3A	3	√7.5 ≈ 2.74	5	~3.23×
4A	4	√10.0 ≈ 3.16	10.0	~4.30×
5A (projected)	5	√12.5 ≈ 3.54	8	~5.38×
6A (projected)	6	√15.0 ≈ 3.87	16	~6.45×

📈 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

Version (v)	Calculation	Context Window
M3 (v=3)	64K × 2³	1,024K
M4 (v=4)	64K × 2⁴	1,024K (>1M)
M5 (projected)	64K × 2⁵	2,048K (~2M)

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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)

Model	Power (×)	Context	log₂(C/1K)	EII
MAI M3 Reason Fast	3.44	1024K	4	29.07
MAI M4 Coder Fast	4.30	1024K	10	43.00 ⭐
MAI M5 (projected)	6.88	2048K	8	59.18

🎯 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.

Variant	Relative Latency	Relative Accuracy (hard tasks)
Fast	1× (baseline)	~85–92%
Thinking	3–8× slower	~94–99%

💡 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

Access	🔒 Private — all models are served exclusively through our platform
Hosting	Puter.js
Weights	Not publicly distributed
API	Available through the MAI website
Commercial Use	Contact MythicGames for licensing

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🌌 "The future of AI is here"

Mythic Artificial Intelligence · MythicGames · 2026