When should you use open source AI models like Llama vs closed models like Claude or GPT? A practical comparison of performance, cost, and use cases.
Open Source vs Closed Source AI: Llama 3 vs Claude vs GPT Performance Breakdown
Meta releases Llama for free. OpenAI charges for GPT. Anthropic charges for Claude.
Why would anyone pay when free exists?
The answer is more nuanced than "free vs paid." Here's the real breakdown.
The Landscape
Closed Source Models
- Claude (Anthropic): 3.5 Sonnet, Opus
- GPT-4 (OpenAI): GPT-4o, GPT-4 Turbo
- Gemini (Google): 2.0, Ultra
You pay per use (API) or subscription. You don't see the weights. Models run on their infrastructure.
Open Source/Open Weights Models
- Llama 3 (Meta): 8B, 70B, 405B parameters
- Mistral (Mistral AI): 7B, Mixtral 8x7B
- Qwen (Alibaba): 2.5 series
- Phi (Microsoft): Phi-3 series
- Gemma (Google): 2, 7B, 27B
Weights are downloadable. You can run them yourself. Varying licenses (some truly open, some restricted).
Performance Comparison
Benchmarks (MMLU)
| Model |
MMLU Score |
Parameters |
| GPT-4o |
88.7% |
Unknown |
| Claude 3.5 Sonnet |
88.7% |
Unknown |
| Llama 3.1 405B |
87.3% |
405B |
| Mistral Large |
84.0% |
Unknown |
| Llama 3.1 70B |
83.6% |
70B |
| Qwen 2.5 72B |
85.3% |
72B |
| Llama 3.1 8B |
68.4% |
8B |
Takeaway: Top open models (405B) approach closed model performance. Smaller open models have meaningful gaps.
Coding (HumanEval)
| Model |
HumanEval |
| Claude 3.5 Sonnet |
92.0% |
| GPT-4o |
90.2% |
| Llama 3.1 405B |
89.0% |
| Llama 3.1 70B |
80.5% |
Takeaway: Closed models still lead in coding, but the gap is shrinking.
Real-World Quality
Benchmarks don't tell the whole story. In practice:
- Instruction following: Closed models are more reliable
- Complex reasoning: Closed models still edge ahead
- Simple tasks: Open models are often sufficient
- Specialized domains: Depends on training data
The Real Decision Factors
1. Privacy and Control
Open source wins when:
- Data must stay on-premise
- Regulatory requirements (HIPAA, GDPR)
- You can't send data to third-party APIs
- You need full control over the model
Closed source is fine when:
- Data isn't sensitive
- Standard enterprise agreements work
- You trust the provider's security
2. Cost at Scale
API pricing (closed models):
- Claude 3.5 Sonnet: $3/$15 per 1M tokens
- GPT-4o: $2.50/$10 per 1M tokens
Self-hosted (open models):
- Llama 70B: ~$1/hour on A100 GPU
- Llama 8B: ~$0.10/hour on consumer GPU
The math:
- Low volume (<1M tokens/day): API is cheaper (no infrastructure)
- High volume (>10M tokens/day): Self-hosted gets attractive
- Very high volume: Self-hosted is dramatically cheaper
For a detailed breakdown of AI pricing and how to optimize costs, check out our real cost of AI guide.
3. Quality Requirements
Use closed models when:
- Quality is paramount
- Complex reasoning required
- You need the latest capabilities
- Edge cases matter
Open models are sufficient when:
- Tasks are well-defined
- Good is good enough
- You're doing high volume/low complexity
- You can fine-tune for your use case
4. Customization
Open source wins:
- Fine-tuning on your data
- Modifying model behavior
- Building specialized variants
- Research and experimentation
Closed source:
- Limited customization
- Fine-tuning options exist but restricted
- What you see is what you get
5. Infrastructure Complexity
Open source requires:
- GPU infrastructure
- Deployment expertise
- Monitoring and scaling
- Updates and maintenance
Closed source:
- Just API calls
- Provider handles everything
- Scales automatically
When to Use What
Use Closed Models (Claude, GPT-4, Gemini)
Best for:
- Product prototyping
- Applications requiring highest quality
- Teams without ML infrastructure
- Variable workloads
- Consumer-facing applications where quality matters
Example: A startup building an AI writing assistant. Quality matters, volume is unpredictable, team is small. Use Claude or GPT-4. For a detailed comparison, see our Claude vs GPT-4 vs Gemini comparison.
Use Open Models (Llama, Mistral)
Best for:
- High-volume, well-defined tasks
- Privacy-sensitive applications
- Cost-sensitive operations at scale
- Customization requirements
- Edge deployment
Example: A company processing millions of support tickets. Task is well-defined, volume is huge, data is sensitive. Self-host Llama 70B.
Hybrid Approach
Many organizations use both:
- Claude/GPT-4 for complex, low-volume tasks
- Llama/Mistral for simple, high-volume tasks
Route based on task complexity and privacy requirements.
Self-Hosting Reality Check
Running open models yourself sounds appealing. Reality:
For a complete guide to running models locally, see our local AI guide with step-by-step setup instructions.
Hardware Requirements
Llama 3.1 8B:
- Runs on: Gaming GPU (RTX 4090), M3 Max Mac
- Memory: 16GB+ VRAM
- Speed: Acceptable for development
Llama 3.1 70B:
- Runs on: 2x A100 80GB or equivalent
- Memory: 140GB+ VRAM
- Cost: ~$2-4/hour cloud, $30K+ to own
Llama 3.1 405B:
- Runs on: 8x A100 80GB or H100 cluster
- Memory: 800GB+ VRAM
- Cost: ~$20+/hour cloud, $200K+ to own
Operational Complexity
You'll need:
- Deployment framework (vLLM, TGI, Ollama)
- Load balancing for production
- Monitoring and logging
- Model updates process
- Fallback handling
Total Cost of Ownership
Self-hosting is cheaper at scale but has hidden costs:
- Engineering time
- Infrastructure management
- Downtime risk
- Update overhead
Calculate all-in cost, not just GPU hours.
The Quality Gap: Is It Closing?
Where Open Models Caught Up
- General knowledge
- Simple summarization
- Basic coding
- Translation
Where Closed Models Still Lead
- Complex reasoning chains
- Nuanced instruction following
- Edge case handling
- Newest capabilities
The Trend
Each Llama release closes the gap. What required GPT-4 last year works with Llama 70B today.
But closed model companies keep improving too. The gap shrinks but doesn't disappear.
Practical Recommendations
For Startups
Start with closed models (Claude or GPT-4). Speed matters more than cost at your scale. Consider open models when you hit volume that makes self-hosting economical.
For Enterprises
Evaluate both. Likely outcome: closed models for complex tasks, open models for high-volume operations. Build infrastructure for open models if data privacy is a concern.
For Developers
Use closed models for prototyping. Know open model options for when clients have requirements that rule out APIs.
For Researchers
Open models are essential. You need to see weights, modify architectures, run experiments.
Frequently Asked Questions
What's the real difference between open source and closed source AI models?
Open source models like Llama have downloadable weights that you can run yourself on your infrastructure, while closed source models like Claude and GPT-4 run on the provider's servers and you pay per use. The choice involves trade-offs between quality versus cost at scale, control versus convenience, and customization versus latest features.
Are open source AI models like Llama as good as GPT-4 or Claude?
Top open models like Llama 3.1 405B approach closed model performance on benchmarks, but closed models still lead in complex reasoning and edge case handling. For simple, well-defined tasks, open models are often sufficient. The quality gap is shrinking with each release but hasn't disappeared.
When should I use open source models instead of ChatGPT or Claude?
Use open source models when you have high-volume well-defined tasks, data that must stay on-premise due to privacy or regulations, cost-sensitive operations at scale, need for customization through fine-tuning, or edge deployment requirements. Open models become economically attractive above 10 million tokens per day.
How much does it cost to self-host open source AI models?
Llama 70B costs approximately one dollar per hour on an A100 GPU, while smaller Llama 8B models cost around 10 cents per hour on consumer GPUs. Factor in engineering time, infrastructure management, and operational overhead when calculating total cost of ownership—self-hosting is cheaper at high volume but has hidden costs.
Can I run open source AI models on my own computer?
Smaller models like Llama 3.1 8B can run on gaming GPUs or M3 Max MacBooks with 16GB+ VRAM. Larger models like Llama 70B require data center GPUs with 140GB+ VRAM. Very large 405B models need massive GPU clusters. Tools like Ollama make local running simple for development. For more details, see our guide to running LLMs locally.
What's the best strategy for choosing between open and closed AI models?
Start with closed models (Claude or GPT-4) for speed and quality, especially at low volumes. Consider open models when you hit volume that makes self-hosting economical or have privacy/customization requirements. Many organizations use both: closed models for complex low-volume tasks, open models for simple high-volume operations.
The Bottom Line
Open vs closed isn't about free vs paid. It's about:
- Quality vs cost at scale
- Control vs convenience
- Customization vs latest features
For most use cases today: Closed models offer better quality with less hassle.
For specific needs (privacy, scale, customization): Open models are increasingly viable.
The winning strategy for most organizations: Start closed, add open as scale and requirements demand.
The gap is closing. A year from now, this calculus might shift. But today, closed models remain the default choice for quality-sensitive applications, while open models carve out space for high-volume and privacy-critical use cases.
Choose based on your actual constraints, not ideology about "open" or "closed."
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