Model Overview
Top models in each category
Open Source Models
Closed Models
Quick Takeaway
Cost depends on the rate and workload: DeepSeek V4 Pro lists $1.32/M input at peak and $0.66/M off-peak, versus $5/M for the Claude listing used here. Quality is editorial: this page's coding scores are comparative guidance, not a vendor benchmark. Control and convenience: evaluate license terms, self-hosting requirements, API reliability, and your own test set before choosing.
Top Picks by Category
Best models for specific needs
Top Open Source
Top Closed
Factor-by-Factor Comparison
How open source and closed models compare across key dimensions
| Factor | Open Source | Closed | Winner | Notes |
|---|---|---|---|---|
| Coding Performance | 8.3-8.9 | 8.6-9.5 | Closed | Closed models lead by ~0.5 points on average |
| Reasoning Ability | 8.4-8.8 | 8.7-9.4 | Closed | Claude leads significantly at 9.4 |
| Cost Efficiency | Varies by hosting, provider, and model | Varies by provider and model | Workload dependent | Compare current rates, cache terms, and infrastructure costs |
| Context Window | 128K-1M+ | 400K-1M | Tie | DeepSeek, Claude, OpenAI, and Gemini now all have 1M-class options |
| Self-Hosting | Yes | No | Open Source | Critical for data privacy requirements |
| Fine-tuning | Full access | Limited/API only | Open Source | Open weights allow full customization |
| Enterprise Support | Community/Vendor | Dedicated SLAs | Closed | Closed vendors offer guaranteed support |
| Data Privacy | Full control | Trust vendor | Open Source | Self-hosting ensures data stays local |
| Time to Market | Fast (self-host) | Instant (API) | Tie | Depends on infrastructure readiness |
| Reliability | Self-managed | 99.9% SLA | Closed | Closed APIs have uptime guarantees |
Pricing Comparison
Cost analysis for different scales
| Model | Type | Input ($/M) | Output ($/M) | Coding Score | Value Rating |
|---|---|---|---|---|---|
Qwen 2.5 Max | Open | $0.35 | $1.4 | 8.6 | 24.6 |
GLM-5 | Open | $0.5 | $0.5 | 8.3 | 16.6 |
DeepSeek V4 Pro | Open | $0.66 | $1.98 | 9.1 | 13.8 |
Llama 4 405B | Open | $0.8 | $2.4 | 8.7 | 10.9 |
Mistral Medium 3.5 | Open | $2 | $6 | 8.5 | 4.3 |
Claude Opus 4.8 | Closed | $5 | $25 | 9.8 | 2.0 |
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 |
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 lists $1.32/M input at peak and $0.66/M off-peak, versus Claude at $5/M; lower input-token costs can matter at high volume
Alternative: Closed for faster time-to-market
Maximum Code Quality
Claude Opus 4.8 leads coding benchmarks at 9.8/10 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?
Use current source-linked model data and licensing terms rather than a single static ranking. DeepSeek V4 Pro has an editorial 9.1 coding score here and lists $1.32 per million peak input tokens ($0.66 off-peak); self-hosting suitability depends on the model license and your infrastructure.
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?
There is no durable single cheapest model: prices, cache discounts, and time-of-day rates change. DeepSeek V4 Pro lists $1.32/$3.96 per million input/output tokens at peak and half those rates off-peak. Check current official price pages for the workloads you plan to run.
Which open source model has the largest context window?
Llama 4 405B, Qwen 2.5 Max, and Mistral Medium 3.5 all offer 128K token context windows. For longer context, you would need closed models like Gemini 3 Pro (1M) or Claude Opus 4.8 (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
Explore specific model comparisons
See Live Benchmark Results
View daily scorecards with task-level breakdowns for all open source and closed models.
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