Model Kombat: The LLM Fighting Game!

Playable ML Concepts Explained

This isn't just a basic stick-figure fighting game. Every mechanic-from rendering complexity to the speed at which characters recover-is a direct, playable representation of real-world Large Language Model engineering.

1. 📐 Parameter Scaling vs. Render Tiers

A model's representation capacity (intelligence) scales with its parameter count. In Model Kombat, a fighter's visual complexity, joint detail, and rendering fidelity directly reflect its real-world parameter size:

• Tier 1 (< 5B Parameters - Gemma 2B, Llama 3.2 3B) - Primitive Capsules: Drawn as simple, single-color flat limbs with low joint segmentation. This visualizes the limited representation capacity and coarse output resolution of small edge models.
• Tier 2 (7B - 14B Parameters - Mistral 7B, Claude Haiku) - Simple Vectors: Structured as thin skeletal wireframe vectors.
• Tier 3 (14B - 35B Parameters - Gemini Flash, Mixtral) - Two-Tone Vectors: Rendered as dual-color, layered vector limbs.
• Tier 4 (35B - 100B Parameters - Llama 8B, Claude Sonnet) - Cyborg Shading: Rendered as detailed vector cylinders with dynamic code particle streams flowing along their limbs.
• Tier 5 (> 100B Parameters - o3, GPT-4o, Claude Opus) - Quantum Vectors: Rendered as glowing vector limbs with digital matrix code particles, soft drop-shadow depth buffers, and real-time afterimage motion trails.

2. ⚡ Reasoning Tokens & KV-Cache Overcharging

Instead of arbitrary "mana" or "stamina," fighters charge a Ki bar representing internal processing cycles and Reasoning Tokens (inspired by reasoning chains like OpenAI's o-series):

• Charging Ki: Simulates the time-to-first-token (TTFT) phase, generating reasoning tokens.
• Limit Break: Overcharging past 100% enters a golden-outlined Limit Break state, granting high-speed afterimages and super-armor.
• Context Eviction & Dizzy: If a model holds its overcharged state too long, its context window overflows. This triggers Context Eviction-draining the model's HP and placing it in a Dizzy state. This represents how context window saturation degrades model coherence and leaves it vulnerable to failure.

3. 🌀 Mixture of Experts (MoE) Routing

Sparse Mixture of Experts (MoE) models do not activate all parameters on every token; instead, a gating network routes tokens to specialized experts.

• Active Experts: Native MoE models in the game (like Mixtral and DeepSeek) dynamically spawn floating indicator nodes representing active Text, Math, or Vision experts.
• Routing Buffs: Landing hits routes computation to these experts, granting temporary combat buffs:
  • TEXT FFN: Increases walk speed (low-latency generation).
  • MATH FFN: Increases damage output (logical compute).
  • VISION FFN: Expands attack hitboxes (spatial awareness).

📊 Spotlight: The AI Fighter Registry

Here is a breakdown of 6 key models featured on the ladder, explaining where they excel in real life and how their unique passives translate into combat advantages:

1. 🧠 o3 (OpenAI)

In Real Life: OpenAI's state-of-the-art reasoning model. Rather than generating text instantaneously, o3 employs a reinforcement learning-driven "thinking chain" to plan, verify, and correct its logic before outputting a response. This makes it a titan in mathematics, competitive programming, and complex coding.

In-Game (Tier 5): Deep Thinking Chain passive. o3 charges its Ki at double speed (representing the model's heavy pre-response thinking cycles). This lets you quickly max out your meter, activate MoE, or release powerful special attacks.

2. 📜 Claude Opus (Anthropic)

In Real Life: Anthropic's flagship heavy model. Opus is celebrated for its high-nuance reading comprehension, literary synthesis, and strict compliance with ethical and safety guidelines (governed by Anthropic's "Constitutional AI" framework).

In-Game (Tier 5): Constitutional Blade passive. Opus has an extended melee strike range on all punches and kicks, allowing you to control the neutral game and keep opponents at a distance-visualizing the model's massive context processing reach.

3. 🎤 Gemini Ultra (Google)

In Real Life: Google's largest multimodal model. Unlike models that stitch together separate speech-to-text and vision encoders, Gemini is built natively multi-modal from day one. It processes video, audio, and text simultaneously inside a single model architecture.

In-Game (Tier 5): Ultra Stance Shift passive. Gemini can switch between TEXT, VISION (expanded hitboxes), and AUDIO (increased speed) modality stances instantly without the standard stance-transition delay, adapting to any opponent's position on the fly.

4. ⚡ DeepSeek V3 (DeepSeek)

In Real Life: The groundbreaking open-weights model from DeepSeek. V3 utilizes Multi-Head Latent Attention (MLA) to compress Key-Value caches, dramatically reducing VRAM footprint, alongside a massive Multi-head Latent Attention routing gating network.

In-Game (Tier 4): MLA Attention passive. Compress caching translates to high combat evasion: DeepSeek V3 has a 15% chance to phase-dodge incoming projectiles completely.

5. 🗼 Mistral 7B (Mistral)

In Real Life: The legendary French open-weights model that punches far above its weight class. Mistral 7B introduced Sliding Window Attention (SWA) to the open-source community, allowing the model to handle longer context streams with minimal performance decay.

In-Game (Tier 2): Sliding Window passive. The sliding window translates to faster execution: Mistral has 10% less startup frame lag on punches and kicks, letting you land strikes before your opponent's animations can finish.

6. 📱 Llama 3.2 3B (Meta)

In Real Life: Meta's mobile-first, edge-optimized model. Llama 3.2 3B is trained specifically for local deployment on smartphones and tablets, focusing on high efficiency, quick response times, and localized fine-tuning.

In-Game (Tier 1): Fine-Tuning passive. The model is incredibly resilient to pressure: Llama 3.2 gains +5% defense every time it successfully blocks consecutive hits, adapting to the opponent's combo string.

🏁 Beat the Machine & Share Your Score

Once you defeat all 19 opponents on the ladder, you will face the reigning champion: o3. Can you reach the top of the ladder and claim victory? Click COPY SCORE at the end of your run and paste your stats in the comments below!

💬 Let's Discuss: Which model is your favorite main? What was your longest combo chain on the ladder?

UnitBuilds-CC / Model-Kombat - Arcade fighting game, which LLM will come out on top?

📟 Model Kombat (SYS_08) 🧠🥊 An Interactive, Playable Visualization of Large Language Model Constraints & Architectures

Welcome, AI engineer. Model Kombat is a retro-cyber fighting game designed to teach the mechanical constraints, architectural paradigms, and hardware limits of Large Language Models (LLMs). In this game, 20 real-world AI models face off in a 2D arena. Every visual element, movement mechanic, and combat stance directly translates core machine learning engineering concepts (such as parameter scaling, KV-caching, Mixture of Experts, multi-modality, and alignment safety) into interactive gameplay loops.

🕹️ Play Directly Here - Model Kombat Live Web App

🧠 Educational Core Concepts & Game Translations

Disclaimer: AI was used throughout this project, it is just fitting that it would co-author with me, so special thanks to the Foundry for its tireless hours toiling away and Gemini for producing the cover image.