Gemini Code Distillation Data Package
Product Details
Product Overview
This product is a code-focused AI LLM training data package distilled from Google Gemini 3.1 Pro. Released by Google DeepMind in February 2026, Gemini 3.1 Pro is the latest flagship model built on a Sparse Mixture-of-Experts (MoE) architecture, featuring a 1M token context window, 80.6% on SWE-bench Verified, Deep-Think extended reasoning mode, and native multimodal code understanding. Through large-scale instruction distillation, Self-Instruct evolution, execution feedback loops, and Chain-of-Thought extraction, we have refined its full-stack code generation, large codebase comprehension, frontend engineering, terminal automation, and cross-modal code conversion capabilities.
Self-distillation cost: ~¥700,000+. This package saves you 91%+ in distillation costs.
Total Data: ~4.6M+ high-quality code instruction-response pairs
Token Scale: ~12B Tokens (including full reasoning chains, code + execution feedback)
Languages: 20+ programming languages (Python, TypeScript, Java, Go, Rust, C++, Kotlin, Swift, Dart, etc.)
Format: JSONL / Parquet (with instructions, code, test cases, execution results, quality scores)
Source Model: Gemini 3.1 Pro (SWE-bench Verified 80.6%, Terminal-Bench 68.5%, ARC-AGI-2 77.1%, 1M token context)
Distillation Methods: Self-Instruct + Evol-Instruct + Deep-Think CoT extraction + execution feedback loops + multimodal code distillation
Gemini 3.1 Pro Unique Advantages
Feature | Details | Distillation Value |
|---|---|---|
1M Token Context | Process ~50,000 lines of code in a single pass, understanding entire project architectures | ~850K long-context code instructions |
Deep-Think Mode | Optional deep reasoning mode that allocates additional compute for complex code problems | ~600K deep reasoning code chains |
Sparse MoE Architecture | Sparse Mixture-of-Experts with highly efficient parameter activation | ~400K efficient reasoning code samples |
Native Multimodal Code | Generate code directly from UI screenshots, design mockups, and videos (WebDev Arena #1) | ~350K vision-to-code instructions |
Agentic Coding | Multi-step tool use, bash command execution, autonomous programming workflows | ~450K agentic task chains |
10 Code Task Categories
# | Task Type | Volume | Core Content |
|---|---|---|---|
1 | Full-Stack Code Gen | ~1.1M | Frontend (React/Vue/Svelte), Backend (Node/Python/Go), Database (SQL/NoSQL), API design |
2 | Frontend & Web Apps | ~900K | Responsive UI, CSS animations, WebGL/Three.js, PWA, performance optimization (WebDev Arena #1 quality) |
3 | Large Codebase Understanding | ~650K | Cross-file dependency analysis, architecture comprehension, code navigation, 1M-token whole-repo analysis |
4 | Debugging & Bug Fixing | ~550K | Runtime error localization, logic bug fixes, performance bottleneck analysis, memory leak detection |
5 | Algorithms & Data Structures | ~500K | Competitive algorithms, dynamic programming, graph theory, string processing, complexity optimization |
6 | Multimodal Code Conversion | ~350K | UI screenshot→code, design mockup→component, video→functional code, sketch→prototype (Gemini exclusive) |
7 | Agentic Coding | ~300K | Multi-step programming task chains, tool orchestration, bash generation & execution |
8 | Code Refactoring | ~300K | Code smell elimination, design patterns, performance optimization, code simplification |
9 | Testing & QA | ~250K | Unit tests, integration tests, E2E tests, property testing, fuzzing test case generation |
10 | Documentation & DevOps | ~150K | API docs, README writing, CI/CD configs, Docker/K8s deployment scripts, architecture docs |
Comparison with Claude / GPT Codex Packages
Dimension | Gemini 3.1 Pro (This) | Claude Package | GPT-5.4 Codex Package |
|---|---|---|---|
SWE-bench | 80.6% | 80.8% | 84% |
Context Window | ★★★★★ (1M tokens) | ★★★★ (200K tokens) | ★★★★★ (1M tokens) |
Frontend/Web Dev | ★★★★★ (WebDev Arena #1) | ★★★★ | ★★★★ |
Multimodal Code | ★★★★★ (Native vision→code) | ★★★ | ★★★ |
Large Codebase | ★★★★★ (50K lines single input) | ★★★★ | ★★★★★ |
Agentic Coding | ★★★★ | ★★★★★ | ★★★★ |
Terminal/DevOps | ★★★★ (Terminal-Bench 68.5%) | ★★★ | ★★★★★ |
Code Precision | ★★★★ | ★★★★★ | ★★★★★ |
Competitive Algo | ★★★★ | ★★★ | ★★★★★ |
Overall Code Ability | ★★★★ | ★★★★★ | ★★★★★ |
Value for Money | ★★★★★ (Lowest price) | ★★★ (Highest price) | ★★★★ |
Key Differentiator: Gemini 3.1 Pro is the undisputed leader in frontend engineering and multimodal code (ranked #1 on WebDev Arena for human preference), with the longest 1M token context window, making it irreplaceable for large codebase comprehension and vision-to-code conversion. Combined with Deep-Think extended reasoning, it excels in complex architecture design. At HK$59,000, it offers the best value for money.
Cost Comparison
Item | Self-Distillation | This Package |
|---|---|---|
API Costs | ¥350,000+ (Gemini 3.1 Pro API $2/$12 per 1M tokens × 12B tokens) | HK$59,000 |
Compute | ¥120,000+ (execution validation, test runs, multimodal processing) | |
Data Engineering | ¥150,000+ (instruction design, quality filtering, dedup, benchmark decontamination) | |
Human Time | 2-4 months (data engineers + AI researchers) | |
Total | ¥700,000+ / 2-4 months |
Quality Assurance
Three-layer deduplication: Exact match → MinHash approximate → AST-level semantic dedup
Benchmark decontamination: SWE-bench / HumanEval / MBPP / LiveCodeBench problems and variants fully filtered
Execution validation: ~78% of code data passes automated compilation and test verification
Multi-dimensional scoring: Each sample scored on correctness / code quality / readability / efficiency
Cross-language consistency: Same problems maintain solution equivalence across languages
Use Cases
Train frontend/full-stack code-specialized models (e.g., web app development AI assistants)
Inject multimodal code capabilities into existing models (design mockup→code, screenshot→component)
Enhance model large codebase comprehension and navigation ability
Build training data foundation for Agentic Coding systems
High-quality knowledge base for code RAG retrieval augmentation
Delivery
Download link sent within 24 hours of purchase
Supports Google Drive / OneDrive / custom object storage downloads
Includes data dictionary, field documentation, and usage guide
7-day after-sales technical support
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