Model Kombat: The LLM Fighting Game!

A developer built Model Kombat, a 2D fighting game that visualizes Large Language Model architectures, parameter scales, and hardware constraints as playable mechanics. The game features fighters representing models like o3, GPT-4o, and Mixtral, with abilities tied to reasoning tokens, KV-cache overcharging, and Mixture of Experts routing.

Ever wondered what would happen if the world's leading Large Language Models settled their benchmark disputes in a 2D cybercity arena? It's easy to look at model performance on standardized benchmarks like MMLU, MATH, or HumanEval . It is much more fun to visualize their underlying architectures, parameter scales, and hardware constraints as a retro-cyber fighting game. So, we built Model Kombat Mixture of Experts Edition 🕹️ Play Directly Here 🎮 Launch Game in Full Screen https://model-kombat-90043718455.us-central1.run.app 🧬 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 instananeously, 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? 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 https://model-kombat-90043718455.us-central1.run.app 🧠 Educational Core Concepts & Game Translations 1. 📐 Parameter Scaling vs. Render Tiers In deep learning, 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 … 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.