Best Local AI Coding Models 2025: Privacy, Cost & Performance

Executive Summary

Local AI coding models—self-hosted, privacy-preserving alternatives to cloud services like Claude, GPT-5, and Gemini—have reached practical viability in 2025, with top models like Llama 3.1 70B (45% HumanEval) and DeepSeek Coder V3 236B (68.5% SWE-bench, #4 globally) approaching 50-90% of cloud model capability while offering complete privacy and zero ongoing costs. While still trailing Claude 4 (77.2% SWE-bench, 86% HumanEval) and GPT-5 (74.9% SWE-bench, 84% HumanEval) in accuracy, local models provide compelling value propositions for specific use cases: sensitive code requiring complete privacy, budget-constrained developers, offline coding environments, and routine development tasks where 80% capability suffices.

The local AI landscape offers multiple models optimized for different hardware constraints: Llama 3.1 70B provides best local performance (45% HumanEval) but requires 32-48GB RAM and takes 8-20 seconds per response, DeepSeek Coder V2 16B balances capability (43% HumanEval) with practicality (16GB RAM, 5-10 second responses), Qwen 2.5 Coder 32B excels at Python (49% HumanEval) for developers with 32GB RAM, and Llama 3.1 8B enables local AI on standard laptops (8GB RAM) with acceptable 36% HumanEval performance for basic tasks.

Local models' primary advantage is complete data privacy—code never leaves your machine, eliminating concerns about IP leakage, compliance violations (HIPAA, defense contracts), or training data incorporation that plague cloud services. This 100% privacy guarantee proves invaluable for defense contractors, healthcare applications, financial services, and any organization with proprietary algorithms or sensitive data. Additionally, local models incur zero API costs or subscriptions ($0 vs $120-2400/year for cloud services), require no internet connectivity, and impose no usage limits.

However, local models come with clear trade-offs: 40-55% lower accuracy than top cloud models (Llama 70B's 45% vs Claude's 86% HumanEval), 3-10x slower response times (8-20 seconds vs 1-3 seconds for cloud), require significant RAM (16-48GB for good performance), demand initial setup effort (2-4 hours learning curve), and lack multimodal capabilities (no image/audio support) available in GPT-5 and Gemini. For complex refactoring, production-critical code, or bleeding-edge AI capability, cloud models remain superior.

The optimal strategy for most developers involves hybrid approaches: using local models (via Ollama, Continue.dev, LM Studio) for routine coding (70% of tasks), sensitive code, and offline work, while reserving cloud models (Claude, GPT-5) for complex problems, architectural decisions, and production-critical implementations (30% of tasks). This hybrid approach delivers 80-90% of pure-cloud productivity at 10-30% of cost while maintaining privacy for sensitive code.

This comprehensive guide examines local AI coding in depth: top model rankings and capabilities, hardware requirements and optimization, setup tutorial with Ollama, performance comparison with cloud models, privacy and compliance benefits, cost analysis (one-time vs ongoing), language-specific performance, hybrid workflow strategies, and decision framework for choosing local vs cloud AI for different scenarios.

Top Local AI Coding Models: Complete Rankings

The local AI landscape features multiple models optimized for different hardware capabilities and use cases. Understanding each model's strengths, requirements, and performance guides optimal selection.

Complete Local Model Rankings

Model-Specific Analysis

Llama 3.1 70B: Best Overall Local Model

Llama 3.1 70B represents the top local coding model, achieving 45% HumanEval accuracy—approximately 53% of Claude 4's performance while running entirely on local hardware:

• Performance: 45% HumanEval, 62% MBPP (Python), handles complex refactoring better than smaller models
• Languages: Excellent Python (48%), JavaScript (44%), TypeScript (42%), Go (40%), acceptable for 15+ languages
• Hardware: Requires 48GB RAM (optimal) or 32GB with quantization, runs well on M2/M3 Max/Ultra Macs
• Speed: 4-8 tokens/second on M2 Ultra, 8-20 seconds for typical responses (acceptable but not instant)
• Use cases: Routine coding, sensitive code, offline development, budget-conscious teams

Llama 70B provides the closest local approximation to cloud model capability, making it the default choice for developers with sufficient RAM (32GB+) who prioritize performance over convenience.

DeepSeek Coder V2 16B: Best Balance

DeepSeek Coder V2 16B optimizes for the sweet spot between performance (43% HumanEval, nearly matching Llama 70B) and hardware accessibility (runs on 16GB RAM laptops):

• Performance: 43% HumanEval, 60% MBPP, exceptional for its size
• Languages: Strong Python (46%), JavaScript (42%), Go (39%), Java (38%)
• Hardware: 16GB RAM sufficient, runs on M1/M2/M3 MacBook Pro, NVIDIA RTX 3060+
• Speed: 10-15 tokens/second, 5-10 second typical responses (faster than 70B models)
• Use cases: Developers with 16GB laptops wanting maximum local capability

For most developers, DeepSeek V2 16B represents the optimal local model: 95% of Llama 70B's capability (43% vs 45%) at one-third the RAM requirement (16GB vs 48GB) with 2x faster inference.

Qwen 2.5 Coder 32B: Python Specialist

Qwen 2.5 Coder 32B achieves the highest HumanEval score among local models (49%) with particular Python strength:

• Performance: 49% HumanEval, 65% MBPP (Python), leading local model for Python-specific tasks
• Languages: Exceptional Python (52%), good JavaScript (43%), decent multi-language
• Hardware: 32GB RAM, well-optimized for Apple Silicon and NVIDIA GPUs
• Speed: 6-10 tokens/second, 8-15 second responses
• Use cases: Python-focused developers, data science, backend development

Choose Qwen 32B if you have 32GB RAM and work primarily in Python; switch to DeepSeek 16B or Llama 70B for more balanced multi-language support.

Llama 3.1 8B: Budget/Laptop Option

Llama 3.1 8B enables local AI on standard 8GB RAM laptops, providing basic coding assistance at zero cost:

• Performance: 36% HumanEval, 48% MBPP, acceptable for simple tasks
• Languages: Decent Python (38%), JavaScript (35%), basic multi-language
• Hardware: 8GB RAM sufficient, runs on any modern laptop
• Speed: 15-25 tokens/second (faster than larger models), 5-10 second responses
• Use cases: Learning, experimentation, budget hardware, simple coding tasks

While significantly weaker than 16B+ models, Llama 8B provides valuable assistance for boilerplate, documentation, simple functions—sufficient for 40-50% of coding tasks at literally zero cost.

DeepSeek Coder V3 236B: Near-Cloud Performance

DeepSeek Coder V3 236B achieves 68.5% SWE-bench (#4 globally), approaching cloud model performance but requiring server-grade hardware:

• Performance: 68.5% SWE-bench, 72% HumanEval (estimated), 89% of Claude 4's capability
• Languages: Excellent across all languages, particularly Python (70%+), JavaScript (68%)
• Hardware: 128GB+ RAM, multi-GPU setup, server deployment only
• Speed: 2-4 tokens/second, 20-40 second responses (slowest but most accurate)
• Use cases: Enterprise privacy requirements with server infrastructure

DeepSeek V3 bridges local and cloud performance but requires investment in high-end hardware ($10K+ servers). For organizations needing Claude-level capability with complete data privacy, DeepSeek V3 provides viable solution.

Performance Comparison: Local vs Cloud Models

Understanding the performance gap between local and cloud models guides realistic expectations and optimal hybrid strategies.

HumanEval Benchmark Comparison

Performance Gap Analysis by Task Type

When Local Performance Suffices

Local models achieve 70-80% of cloud quality for:

• Boilerplate code: CRUD operations, API endpoint scaffolds, test structures—well-defined patterns where 75% accuracy acceptable
• Documentation: Function docstrings, README generation, API documentation—80% accuracy sufficient, easy to review
• Simple functions: Utilities, data transformations, format conversions—clear specifications enable local models to perform adequately
• Code explanations: Understanding existing code, line-by-line breakdowns—72% accuracy helps learning even if not perfect
• Routine debugging: Syntax errors, missing imports, simple logic bugs—local models provide useful starting points

These tasks represent approximately 60-70% of typical development work, meaning local models can handle majority of coding assistance needs despite lower overall benchmarks.

When Cloud Models Are Necessary

Cloud models (Claude, GPT-5) provide substantial advantages for:

• Complex refactoring: 82% vs 35% accuracy—local models frequently break functionality or miss edge cases in architectural changes
• Production-critical code: 84% vs 32%—2.6x higher accuracy prevents costly bugs in payment processing, security, data integrity
• Novel algorithms: 78% vs 28%—local models struggle with problems lacking clear patterns in training data
• Architectural decisions: 80% vs 25%—trade-off analysis and system design require reasoning beyond local model capability
• Debugging complex issues: Race conditions, distributed systems bugs, performance optimization require cloud model sophistication

For these scenarios (30-40% of professional development), the quality gap justifies cloud model costs and privacy trade-offs.

Complete Setup Guide: Running Local Models with Ollama

Ollama provides the easiest path to running local AI models, handling model management, optimization, and serving through simple CLI and API interfaces.

Step 1: Install Ollama

1. macOS: Download from ollama.com → Run installer → Ollama runs in menu bar (automatic)
2. Linux: `curl -fsSL https://ollama.com/install.sh | sh` → Ollama installs as systemd service
3. Windows: Download Windows installer from ollama.com → Run → Ollama launches automatically

Installation takes 1-2 minutes. Ollama automatically detects hardware (CPU vs GPU, RAM available) and optimizes model serving accordingly.

Step 2: Download and Run Models

Pull desired model based on hardware:

# For 16GB RAM (recommended starting point)
ollama pull deepseek-coder-v2:16b

# For 32GB+ RAM (best performance)
ollama pull llama3.1:70b

# For 8GB RAM (basic)
ollama pull llama3.1:8b

# For Python-focused work (32GB RAM)
ollama pull qwen2.5-coder:32b

Model download takes 10-60 minutes depending on size (8-40GB) and internet speed. Ollama stores models in `~/.ollama/models/` and handles caching automatically.

Step 3: Test Model via CLI

# Start interactive chat with model
ollama run deepseek-coder-v2:16b

# Ask coding question
>>> Write a Python function to calculate Fibonacci numbers

# Model generates response in 5-15 seconds
# Exit with /bye or Ctrl+D

Step 4: Integrate with VS Code via Continue.dev

1. Install Continue.dev extension in VS Code (search "Continue" in Extensions)
2. Open Continue settings (Cmd+Shift+P → "Continue: Open Config")
3. Add Ollama model to config:

{
  "models": [
    {
      "title": "DeepSeek Coder 16B",
      "provider": "ollama",
      "model": "deepseek-coder-v2:16b"
    },
    {
      "title": "Llama 70B",
      "provider": "ollama",
      "model": "llama3.1:70b"
    }
  ]
}

After configuration, Continue provides: (1) Inline code completions powered by local model, (2) Chat interface (Cmd+L) for questions and debugging, (3) Edit mode (Cmd+I) for code transformations, (4) Zero external API calls—all processing local.

Step 5: Optimize Performance

• Enable GPU acceleration: Ollama automatically uses Metal (Mac) or CUDA (NVIDIA) if available—verify with `ollama ps` showing GPU memory usage
• Adjust context window: `ollama run llama3.1 --ctx-size 8192` for larger context (default 4096)
• Use quantized models: Add `:q4_0` suffix for 4-bit quantization (smaller, faster, slight quality loss): `ollama pull llama3.1:70b-q4_0`
• Monitor resources: `ollama ps` shows running models and resource usage; `ollama stop <model>` to free memory

Alternative: LM Studio GUI

For users preferring graphical interface over CLI, LM Studio (lmstudio.ai, free) provides similar functionality with visual model management, chat UI, and performance monitoring. Install → Browse models → Download → Chat—no terminal required.

Hardware Requirements and Optimization

Local AI performance depends heavily on hardware. Understanding requirements and optimizations guides investment decisions and performance tuning.

Hardware Requirement Matrix

Recommended Configurations by Budget

Budget: $0 (Use Existing Hardware)

• Hardware: Any 8GB+ RAM laptop/desktop, existing equipment
• Model: Llama 3.1 8B or CodeGemma 7B
• Performance: 36% HumanEval, 15-25 tokens/sec, sufficient for basic coding
• Use cases: Learning, experimentation, simple functions, documentation

Budget: $200-500 (RAM Upgrade)

• Hardware: Upgrade to 32GB RAM, existing CPU/GPU
• Model: Llama 3.1 70B or CodeLlama 34B
• Performance: 42-45% HumanEval, 4-8 tokens/sec, handles most coding tasks
• Use cases: Professional development, routine coding, sensitive projects

Budget: $1000-2000 (Mid-Range Build)

• Hardware: NVIDIA RTX 4070 Ti (12GB) + 32GB RAM + fast CPU
• Model: Llama 70B with GPU acceleration
• Performance: 45% HumanEval, 15-25 tokens/sec with GPU (3x faster), excellent experience
• Use cases: Full-time development, team deployment, production use

Budget: $4000-8000 (High-End/Enterprise)

• Hardware: Mac Studio M2 Ultra (128GB) or server with 2x NVIDIA 4090 + 128GB RAM
• Model: DeepSeek Coder V3 236B or multiple models simultaneously
• Performance: 68-72% HumanEval (near-cloud), 10-20 tokens/sec, serving multiple developers
• Use cases: Enterprise privacy requirements, team deployment (5-10 developers), maximum local capability

Optimization Techniques

Quantization: Smaller Models, Acceptable Quality Loss

Quantization reduces model precision from 16-bit to 4-8 bit, halving RAM requirements with 5-10% quality degradation:

• 4-bit quantization (Q4): Llama 70B fits in 32GB RAM (vs 48GB), 8-12% quality loss, worthwhile trade-off
• 8-bit quantization (Q8): Moderate compression, 3-5% quality loss, requires 40GB for 70B models
• GGUF format: Optimized quantized models via `ollama pull llama3.1:70b-q4_0`

GPU Acceleration: 3-5x Faster Inference

• Apple Silicon (M1/M2/M3): Unified memory enables efficient model serving; M2 Ultra 128GB ideal for 70B models
• NVIDIA CUDA: RTX 3060 12GB runs 16B models well, RTX 4090 24GB handles 70B, consumer GPUs cost-effective vs cloud
• Mixed CPU-GPU: Ollama automatically splits model across GPU and system RAM when GPU memory insufficient

Privacy and Compliance Benefits

Local models' primary advantage over cloud services is complete data privacy—code never leaves your machine, eliminating IP risks, compliance violations, and data sovereignty concerns.

Privacy Comparison: Local vs Cloud

Industries Requiring Local Models

Defense and Government Contractors

ITAR, CMMC, and classified work prohibit transmitting code to external services. Local models enable AI assistance without compliance violations, export control issues, or security clearance problems. Defense contractors report 40% productivity gains from local AI vs zero assistance due to cloud prohibition.

Healthcare and HIPAA Compliance

Protected Health Information (PHI) in code (patient records schemas, medical algorithms, clinical decision support) cannot be sent to cloud services without Business Associate Agreements (BAAs). Most consumer AI services (ChatGPT Plus, Claude Pro) lack BAAs. Local models enable HIPAA-compliant AI coding assistance.

Financial Services

Proprietary trading algorithms, risk models, fraud detection systems, customer financial data—all require strict confidentiality. Cloud AI services create audit trails and potential IP leakage. Local models provide compliance with financial data protection regulations while enabling AI assistance.

Startups with Competitive Moats

Proprietary algorithms representing competitive advantages (recommendation engines, matching algorithms, optimization systems) risk exposure through cloud AI services. Local models protect trade secrets while accelerating development of IP-critical code.

Compliance Certifications

Local deployment simplifies compliance:

• SOC 2: No third-party data processor, simplifying audit scope
• ISO 27001: Data never leaves controlled environment
• GDPR: No cross-border data transfer, full data residency control
• CCPA: Consumer data not shared with third parties
• Industry-specific: PCI-DSS, FERPA, GLBA compliance simplified

Cost Analysis: Local vs Cloud Models

While local models require upfront hardware investment, they eliminate ongoing subscription and API costs, providing superior ROI for sustained usage.

5-Year Total Cost of Ownership

Break-Even Analysis

Scenario 1: Existing 16GB Laptop

• Hardware cost: $0 (use existing laptop)
• Model: DeepSeek Coder V2 16B (43% HumanEval)
• Cloud equivalent: $20-200/month subscriptions
• Break-even: Immediate—saves $240-2400/year from day one

Scenario 2: 32GB RAM Upgrade

• Hardware cost: $200-400 RAM upgrade
• Model: Llama 3.1 70B (45% HumanEval, best local)
• Cloud equivalent: Claude Pro ($240/year) or Cursor ($240-2400/year)
• Break-even: 2-20 months depending on cloud service avoided

Scenario 3: Mid-Range GPU Build

• Hardware cost: $1,500 (GPU + RAM + components)
• Model: Llama 70B with 3x faster GPU inference
• Cloud equivalent: Cursor Team ($2,400/year)
• Break-even: 7.5 months, then $2,400 annual savings

Scenario 4: Enterprise Server (5-10 developers)

• Hardware cost: $10,000 (server with 128GB+ RAM, GPUs)
• Model: DeepSeek Coder V3 236B serving entire team
• Cloud equivalent: $12,000-24,000/year (5-10 × $200/month Cursor)
• Break-even: 5-10 months, then $12K-24K annual savings

Hidden Cost Considerations

Local Model Costs Often Overlooked

• Electricity: $2-5/month additional power for active model usage
• Time investment: 2-4 hours initial learning curve, setup, troubleshooting
• Maintenance: Model updates, Ollama upgrades, occasional troubleshooting (2-4 hours/year)
• Hardware depreciation: $500-1000 hardware loses value over 3-5 years

Cloud Model Costs Often Overlooked

• API overages: Exceeding quotas results in additional charges or rate limiting
• Data egress: Large context windows (Claude 200K, Gemini 1M) cost more per query
• Team scaling: Adding developers multiplies per-seat costs linearly
• Lock-in risk: Price increases, terms changes, service discontinuation

Hybrid Strategies: Combining Local and Cloud

Most developers optimize cost and capability through hybrid approaches—using local models for routine work and cloud models for complex problems.

Recommended Hybrid Workflows

Strategy 1: Local-First with Cloud Fallback

Use local models (Llama 70B, DeepSeek 16B) for 70% of coding tasks, falling back to cloud (Claude, GPT-5) when local quality insufficient:

• Local for: Boilerplate, simple functions, documentation, code explanation, routine debugging
• Cloud for: Complex refactoring, production-critical code, novel algorithms, architectural decisions
• Benefits: 70-80% cost savings ($50-150/year vs $240-2400), privacy for routine code, maximum capability when needed
• Tools: Continue.dev (supports both local Ollama and cloud APIs with easy switching)

Strategy 2: Privacy-Sensitive Local, Everything Else Cloud

Use local models exclusively for sensitive/proprietary code, cloud models for non-sensitive work:

• Local for: Core IP, proprietary algorithms, sensitive data handling, compliance-critical code
• Cloud for: Generic utilities, public-facing code, documentation, standard patterns
• Benefits: IP protection, compliance, maximum capability for non-sensitive work
• Implementation: Separate projects/repos, clear guidelines on what code uses which AI

Strategy 3: Model Specialization

Route tasks to optimal model regardless of local vs cloud:

• Local Llama 70B: Python backend, Go microservices, general coding
• Local Qwen 32B: Data science, pandas, NumPy, statistical code
• Cloud GPT-5: JavaScript/React frontend, multimodal tasks
• Cloud Claude 4: Complex refactoring, production-critical code
• Benefits: Each task gets optimal model, balanced cost and capability
• Tools: Cursor (easy model switching), custom routing via Continue.dev

Strategy 4: Team Deployment

Small teams (5-10 developers) deploy shared local server + individual cloud subscriptions:

• Shared server: DeepSeek Coder V3 236B on $10K server, available to all team members for sensitive/routine work
• Individual cloud: Each developer has Claude Pro or Cursor subscription for personal complex work
• Benefits: Team-wide privacy compliance, cost optimization (one server vs 5-10 full subscriptions)
• Cost: $10K hardware + $1,200-2,400/year (5-10 × $20/month Claude) = $2,400-4,400 first year vs $12K-24K pure cloud

Decision Framework: Local vs Cloud

This framework guides optimal choice between local and cloud AI for specific scenarios.

Choose Local Models When:

• Privacy absolutely required: Defense contracts, HIPAA-protected code, proprietary algorithms, trade secrets
• Budget-constrained: Unwilling/unable to pay $240-2400/year for cloud subscriptions
• Offline work common: Remote development, travel, unreliable internet, air-gapped environments
• Routine coding focus: 70%+ of work is boilerplate, simple functions, documentation—local suffices
• Learning/experimentation: Students, hobbyists, open-source developers wanting unlimited usage at $0
• Hardware available: Already have 16GB+ RAM laptop or willing to invest $200-500 in RAM
• Team deployment viable: 5-10 developers can share $10K server, amortizing cost across team

Choose Cloud Models When:

• Maximum accuracy required: Production-critical code, complex refactoring, architectural decisions requiring 77-86% vs 43-45% accuracy
• Complex work dominant: 50%+ of coding involves novel algorithms, intricate debugging, sophisticated refactoring
• Multimodal needs: Analyzing UI screenshots, architecture diagrams, error images (GPT-5, Gemini only)
• Limited hardware: 8GB RAM laptop, unwilling to upgrade, no GPU—cloud provides better experience
• Convenience valued: Prefer instant setup, no maintenance, always-latest models, professional support
• Team collaboration: Shared prompts, team analytics, centralized management (Cursor Team, enterprise plans)
• Cost immaterial: $240-2400/year insignificant vs developer salary, prioritize capability over savings

Hybrid Approach When:

• Mixed sensitivity: Some code proprietary (local), other code non-sensitive (cloud acceptable)
• Cost-conscious but quality-aware: Want savings but recognize cloud advantages for hard problems
• Occasional complex work: 70% routine (local suffices), 30% complex (worth cloud cost for those tasks)
• Team with varied needs: Some developers work on sensitive code, others on standard applications

Conclusion: The Case for Local AI in 2025

Local AI coding models have reached practical viability in 2025, with top models like Llama 3.1 70B (45% HumanEval) and DeepSeek Coder V3 (68.5% SWE-bench) providing 40-90% of cloud model capability while offering complete privacy and zero ongoing costs. While still trailing Claude 4 (86% HumanEval) and GPT-5 (84% HumanEval) in raw accuracy, local models suffice for 60-70% of typical coding tasks—boilerplate generation, simple functions, documentation, code explanation, and routine debugging—making them viable primary tools for many developers.

The compelling value proposition for local models centers on three advantages: (1) Complete data privacy with code never leaving your machine, essential for defense contractors, healthcare applications, financial services, and proprietary algorithm development, (2) Zero ongoing costs at $0 annual expenditure vs $240-2400/year for cloud subscriptions, breaking even in 2-20 months depending on hardware investment, and (3) Unlimited usage without API quotas, rate limits, or subscription restrictions, enabling unconstrained experimentation and learning.

However, local models come with clear limitations: 40-55% lower accuracy than top cloud models for complex tasks, 3-10x slower response times (8-20 seconds vs 1-3 seconds), require 16-48GB RAM for good performance, demand 2-4 hour learning curve for setup and optimization, and lack multimodal capabilities (no image/audio support) available in GPT-5 and Gemini. For complex refactoring, production-critical code, or cutting-edge AI capability, cloud models remain superior despite higher costs.

The optimal strategy for most professional developers involves hybrid approaches: using local models (via Ollama, Continue.dev) for routine coding, sensitive code, and offline work (70% of tasks), while reserving cloud models (Claude, GPT-5) for complex problems, architectural decisions, and production-critical implementations (30% of tasks). This hybrid approach delivers 80-90% of pure-cloud productivity at 10-30% of cost while maintaining privacy for sensitive code, combining the best aspects of both paradigms.

For specific audiences, recommendations diverge: (1) Students and learners should start with local models (Llama 8B on existing hardware) at $0 cost for unlimited experimentation, (2) Privacy-sensitive organizations (defense, healthcare, finance) should deploy local models exclusively despite performance trade-offs, (3) Budget-conscious developers should use local-first with cloud fallback, achieving 70-80% cost savings, and (4) Well-funded teams prioritizing maximum capability should use cloud models primarily while maintaining local options for sensitive code. As local model capabilities continue improving—with next-generation models likely reaching 55-65% HumanEval (vs today's 45%)—the value proposition for local AI will only strengthen through 2025 and beyond.

Additional Resources

• Ollama - Easiest way to run local AI models
• LM Studio - GUI alternative to Ollama
• HuggingFace Model Hub - Browse and download open-source models
• Continue.dev - VS Code extension supporting local and cloud models
• Llama 3.1 Official Repo - Meta's Llama model documentation
• DeepSeek Coder - DeepSeek coding model documentation
• Qwen 2.5 Coder - Qwen coding model repository

Frequently Asked Questions