Model Overview
Top models in each category
Open Source Models
Closed Models
Quick Takeaway
Open source wins on cost: DeepSeek V3 at $0.27/M is 55x cheaper than Claude at $15/M. Closed wins on quality: Claude leads coding at 9.5 vs DeepSeek's 8.9. Open source wins on control: Self-host for data privacy and fine-tuning. Closed wins on convenience: Instant API access with uptime SLAs.
Top Picks by Category
Best models for specific needs
Top Open Source
Top Closed
Factor-by-Factor Comparison
How open source and closed models compare across key dimensions
| Factor | Open Source | Closed | Winner | Notes |
|---|---|---|---|---|
| Coding Performance | 8.3-8.9 | 8.6-9.5 | Closed | Closed models lead by ~0.5 points on average |
| Reasoning Ability | 8.4-8.8 | 8.7-9.4 | Closed | Claude leads significantly at 9.4 |
| Cost Efficiency | $0.27-$2.00/M | $5-$15/M | Open Source | Open source is 5-50x cheaper on average |
| Context Window | 64K-128K | 128K-1M | Closed | Gemini offers 1M context window |
| Self-Hosting | Yes | No | Open Source | Critical for data privacy requirements |
| Fine-tuning | Full access | Limited/API only | Open Source | Open weights allow full customization |
| Enterprise Support | Community/Vendor | Dedicated SLAs | Closed | Closed vendors offer guaranteed support |
| Data Privacy | Full control | Trust vendor | Open Source | Self-hosting ensures data stays local |
| Time to Market | Fast (self-host) | Instant (API) | Tie | Depends on infrastructure readiness |
| Reliability | Self-managed | 99.9% SLA | Closed | Closed APIs have uptime guarantees |
Pricing Comparison
Cost analysis for different scales
| Model | Type | Input ($/M) | Output ($/M) | Coding Score | Value Rating |
|---|---|---|---|---|---|
| DeepSeek V3 | Open | $0.27 | $1.1 | 8.9 | 33.0 |
| Qwen 2.5 Max | Open | $0.35 | $1.4 | 8.6 | 24.6 |
| GLM-5 | Open | $0.5 | $0.5 | 8.3 | 16.6 |
| Llama 4 405B | Open | $0.8 | $2.4 | 8.7 | 10.9 |
| Mistral Large 3 | Open | $2 | $6 | 8.5 | 4.3 |
| Grok 2 | Closed | $5 | $15 | 8.6 | 1.7 |
| Gemini 3 Pro | Closed | $7 | $21 | 8.8 | 1.3 |
| GPT-5.2 | Closed | $10 | $30 | 9.2 | 0.9 |
| Claude Opus 4.6 | Closed | $15 | $75 | 9.5 | 0.6 |
Use Case Recommendations
Which type to choose for specific scenarios
Enterprise with Data Regulations
Self-hosting ensures compliance with GDPR, HIPAA, and data residency requirements
Alternative: Closed with data processing agreements
Startup MVP Development
DeepSeek at $0.27/M tokens vs Claude at $15/M — save $14,700 per million tokens
Alternative: Closed for faster time-to-market
Maximum Code Quality
Claude Opus 4.6 leads coding benchmarks at 9.5/10 with superior refactoring ability
Alternative: Llama 4 405B at 8.7 for cost savings
Long Context Analysis
Gemini 3 Pro offers 1M context window for analyzing entire codebases or documents
Alternative: Qwen 2.5 at 128K for most needs
Custom Fine-tuning
Full model weights allow domain-specific fine-tuning for specialized applications
Alternative: Closed API fine-tuning where available
High-Volume Production
DeepSeek or self-hosted Llama 4 can reduce costs by 90%+ at scale
Alternative: Closed for guaranteed uptime
Research & Experimentation
Access to model weights enables research into model behavior, safety, and improvements
Alternative: Closed API for production experiments
Mission-Critical Systems
99.9% uptime SLAs and dedicated enterprise support reduce operational risk
Alternative: Open source with redundancy
Frequently Asked Questions
Common questions about open source vs closed models
What is the best open source LLM for coding?
DeepSeek V3 currently leads open source models with an 8.9 coding score at just $0.27 per million input tokens. For self-hosting without API costs, Llama 4 405B offers the best combination of performance (8.7 coding) and true open weights with the Llama license.
Are open source models as good as closed models?
The gap has narrowed significantly. Open source models now achieve 8.3-8.9 coding scores vs 8.6-9.5 for closed models. For most applications, open source provides sufficient quality at 5-50x lower cost. Closed models still lead for maximum quality requirements.
What are the advantages of open source AI models?
Open source models offer: (1) 5-50x lower costs, (2) self-hosting for data privacy, (3) full fine-tuning control, (4) no vendor lock-in, (5) transparent model weights for research, and (6) compliance with data regulations like GDPR and HIPAA.
When should I choose closed models over open source?
Choose closed models when you need: (1) maximum coding quality (Claude at 9.5), (2) guaranteed 99.9% uptime SLAs, (3) very long context (Gemini's 1M), (4) instant API access without infrastructure, or (5) enterprise support with dedicated SLAs.
Can I self-host open source LLMs for free?
Yes, models like Llama 4 and Qwen can be self-hosted on your own GPU infrastructure with no per-token cost. However, you pay for compute (GPU rental ~$2-8/hour for inference), electricity, and engineering time. For high volume, self-hosting is usually cheaper than APIs.
What is the cheapest AI model for coding?
DeepSeek V3 at $0.27/$1.10 per million tokens is the cheapest capable coding model. GLM-5 at $0.50/$0.50 is even cheaper for output-heavy workloads. Self-hosted Llama 4 can be cheaper still at very high volumes.
Which open source model has the largest context window?
Llama 4 405B, Qwen 2.5 Max, and Mistral Large 3 all offer 128K token context windows. For longer context, you would need closed models like Gemini 3 Pro (1M) or Claude Opus 4.6 (200K).
Are open source models safe for enterprise use?
Open source models can be safer for enterprises with strict data requirements since you control where data goes. However, closed vendors often provide better security certifications (SOC 2, HIPAA BAA), red-teaming, and safety fine-tuning. Evaluate based on your specific compliance needs.
Related Comparisons
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