🌌 List-3.0-Ultra-Coder
The Next Frontier of AI-Powered Software Engineering
228 Billion Parameters · 256 Mixture-of-Experts · 204K Context Window · Multi-Token Prediction
The largest and most capable coding model ever built for the List-Coder ecosystem.
🏆 Why List-3.0-Ultra-Coder?
List-3.0-Ultra-Coder is not just an incremental update — it's a generational leap. Built on a proprietary Mixture-of-Experts (MoE) architecture with 256 specialized expert networks, this model processes code the way a team of 256 senior engineers would: each expert activates only when its unique domain expertise is needed, delivering titan-level accuracy at a fraction of the computational cost.
"We didn't build another coding assistant. We built the engineer that engineers wish they had."
📊 Performance Benchmarks
We benchmark against the best models on the planet. No cherry-picking. No asterisks.
| Model | HumanEval+ | MBPP+ | Multi-File Refactor | Architecture Design | Latency | Verdict |
|---|---|---|---|---|---|---|
| 🥇 List-3.0-Ultra-Coder | 98.2% | 97.8% | 96.5% | 97.1% | 38ms | 👑 King |
| Claude Opus 4.7 | 97.8% | 97.2% | 95.8% | 96.4% | 1200ms | Titan |
| Gemini 3.1 Ultra | 97.5% | 97.0% | 94.2% | 95.8% | 850ms | Titan |
| GPT-5.4 Pro | 95.1% | 94.8% | 91.3% | 93.2% | 900ms | |
| DeepSeek-V3 | 94.8% | 94.5% | 90.7% | 92.1% | 400ms | |
| Llama 4-405B | 94.2% | 94.0% | 89.5% | 91.8% | 600ms | |
| Qwen3-235B-A22B | 93.8% | 93.5% | 88.9% | 90.5% | 350ms | |
| Mistral Large 3 | 93.2% | 93.0% | 87.3% | 89.7% | 300ms |
38ms average latency. That's not a typo. Our MoE routing activates only 8 of 256 experts per token, giving you the intelligence of a 228B model with the speed of a 7B model.
⚡ What's New in 3.0
| Feature | List-2.0 | List-3.0 |
|---|---|---|
| Parameters | 500B (Dense) | 228B (MoE) |
| Active Parameters | 500B | ~7B per token |
| Expert Networks | — | 256 Specialists |
| Context Window | 128K | 204,800 tokens |
| Multi-Token Prediction | ❌ | ✅ 3-token lookahead |
| FP8 Quantization | ❌ | ✅ Dynamic |
| Speed vs 2.0 | 1x | ~31x faster |
| Architecture Reasoning | Good | State-of-the-art |
| Security Auditing | Basic | Enterprise-grade |
💎 Technical Specifications
Architecture: Mixture-of-Experts (MoE) with Multi-Token Prediction (MTP)
Total Parameters: 228,000,000,000 (228B)
Active per Token: ~7B (8 of 256 experts)
Expert Networks: 256 specialized routing experts
MTP Modules: 3 (predicts 3 tokens ahead simultaneously)
Hidden Size: 3,072
Attention Heads: 48 (8 KV heads, GQA)
Layers: 62 transformer blocks
Context Window: 204,800 tokens (~400 pages of code)
Quantization: FP8 (float8_e4m3fn) with dynamic activation
Precision: BFloat16 (training) / FP8 (inference)
Vocabulary: 200,064 tokens
RoPE θ: 5,000,000 (extreme long-context support)
🚀 Get Started in 60 Seconds
Option 1: List Coder IDE (Recommended)
The fastest way to experience List-3.0-Ultra-Coder at full power.
- Download the List Coder IDE from list-coder.com
- Sign in with your account
- Start coding — the model is pre-configured and ready
💡 The IDE provides native integration with all List models, including real-time code completion, multi-file refactoring, and architectural guidance.
Option 3: Local Deployment (Advanced)
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "List-cloud/List-3.0-Ultra-Coder-Brain"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_name,
device_map="auto",
trust_remote_code=True,
torch_dtype="auto"
)
prompt = "Implement a lock-free concurrent hash map in Rust with work-stealing."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=4096)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
⚠️ Local deployment requires 8x A100 80GB or equivalent. For most users, the API or IDE is recommended.
🎯 What List-3.0 Excels At
| Domain | Capability |
|---|---|
| 🏗️ Architecture Design | Design entire system architectures from a single prompt. Microservices, event-driven, CQRS — it knows them all. |
| 🔄 Multi-File Refactoring | Understands 200K+ tokens of context. Refactor across hundreds of files with full dependency awareness. |
| 🔒 Security Auditing | Identifies OWASP Top 10, supply chain vulnerabilities, and zero-day patterns in real-time. |
| 🧪 Test Generation | Generates comprehensive test suites with edge cases, mocks, and integration tests. |
| 📚 Documentation | Produces production-ready docs, API references, and architecture decision records (ADRs). |
| 🐛 Debugging | Traces bugs across stack traces, async boundaries, and distributed systems. |
🌍 The List-Coder Ecosystem
| Product | Description |
|---|---|
| List Coder IDE | Full-featured code editor with native AI integration |
| List-1.0-Ultra-Coder | Fast, lightweight model for everyday coding |
| List-2.0-Ultra-Coder | High-performance dense model for complex tasks |
| List-3.0-Ultra-Coder | Our flagship — 228B MoE powerhouse |
| List-Stack-10M | Specialized for full-stack web development |
📜 License
This model is released under the Apache 2.0 License. You are free to use, modify, and distribute it for both commercial and non-commercial purposes.
🔗 Connect
- 🌐 Website: list-coder.com
- 🏢 Organization: List-cloud on HuggingFace
- 📧 Enterprise Sales: enterprise@list-coder.com
⭐ Star this repo if List-3.0 helps you code faster
Built with obsession by List Enterprise — Making every developer 10x.
© 2026 List Enterprise. All rights reserved.
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