Open Source vs Proprietary AI Models
Compare open-source and proprietary models across performance, pricing, capabilities, and stability. Tracking 300 models to help you decide which approach best fits your needs.
Quick Comparison
Open Source
Publicly available weightsProprietary
Closed-source / API-onlyOpen Source vs Proprietary
Top Open Source Models by Score
Head-to-Head Metrics
| Metric | Open Source | Proprietary |
|---|---|---|
| Model Count | 104 | 196 |
| Avg Score | 61.7 | 71.7 |
| Median Score | 66.6 | 76.8 |
| Best Score | 83.4DeepSeek V3.2 | 95.9Claude Fable 5.1 |
| Avg Cost ($/1M) | $0.977 | $10.28 |
| Free Models | 10 | 3 |
| Avg Context Window | 436K | 723K |
| Stable Models % | 3.8% | 21.4% |
| Fragile Models % | 92.3% | 71.9% |
Top Open Source Models
Top 20| # | Model | Score |
|---|---|---|
| 1 | DeepSeek V3.2DeepSeek | 83 |
| 2 | Gemma 4 31BGoogle | 81 |
| 3 | Gemma 4 31B (free)Google | 81 |
| 4 | Qwen3.5 397B A17BAlibaba | 79 |
| 5 | R1 0528DeepSeek | 79 |
| 6 | GLM 5.3Zhipu AI | 79 |
| 7 | GLM 5.3 (batch)Zhipu AI | 79 |
| 8 | GLM 5.2Zhipu AI | 79 |
| 9 | GLM 5.3 FlashZhipu AI | 78 |
| 10 | GLM 5.1Zhipu AI | 78 |
| 11 | GLM 5Zhipu AI | 78 |
| 12 | GLM 5.3 Flash (batch)Zhipu AI | 78 |
| 13 | Qwen3.5-122B-A10BAlibaba | 78 |
| 14 | Gemma 2 27BGoogle | 77 |
| 15 | Qwen3.5-27BAlibaba | 77 |
| 16 | MiMo-V2.6-ProXiaomi | 76 |
| 17 | MiMo-V2.5-ProXiaomi | 76 |
| 18 | Qwen3.5-35B-A3BAlibaba | 76 |
| 19 | GLM 5.2 (free)Zhipu AI | 76 |
| 20 | Kimi K2.6Moonshot AI | 76 |
Top Proprietary Models
Top 20| # | Model | Score |
|---|---|---|
| 1 | Claude Fable 5.1Anthropic | 96 |
| 2 | Claude Fable 5.1 (batch)Anthropic | 96 |
| 3 | Claude Fable 5Anthropic | 96 |
| 4 | Claude Fable 5 (batch)Anthropic | 96 |
| 5 | Claude Opus 5.5Anthropic | 95 |
| 6 | Claude Opus 5Anthropic | 95 |
| 7 | Claude Opus 4.8Anthropic | 95 |
| 8 | Claude Opus 4.7Anthropic | 95 |
| 9 | Claude Opus 4.7 (batch)Anthropic | 95 |
| 10 | Claude Opus 4.8 (batch)Anthropic | 95 |
| 11 | GPT-5.5 ProOpenAI | 93 |
| 12 | GPT-5.5 Pro (batch)OpenAI | 93 |
| 13 | GPT-5.5OpenAI | 93 |
| 14 | GPT-5.5 (batch)OpenAI | 93 |
| 15 | Gemini 3.1 Pro Preview Custom ToolsGoogle | 92 |
| 16 | Gemini 3.1 Pro PreviewGoogle | 92 |
| 17 | Gemini 3.1 Pro Preview (batch)Google | 92 |
| 18 | GPT-5.4 ProOpenAI | 92 |
| 19 | GPT-5.4 Pro (batch)OpenAI | 92 |
| 20 | GPT-5.4OpenAI | 92 |
Capability Comparison
Feature adoption by group| Capability | Open Source | Proprietary |
|---|---|---|
| Vision | 39 (37.5%) | 170 (86.7%) |
| Function Calling | 94 (90.4%) | 193 (98.5%) |
| Streaming | 104 (100.0%) | 196 (100.0%) |
| JSON Mode | 88 (84.6%) | 185 (94.4%) |
| Reasoning | 84 (80.8%) | 169 (86.2%) |
| Web Search | 0 (0.0%) | 145 (74.0%) |
| Image Output | 0 (0.0%) | 0 (0.0%) |
Price Comparison
Open Source Pricing
Proprietary Pricing
The Verdict
Open Source leads in free model availability, lower average pricing. With 10 free models, open-source offers the most accessible entry point for experimentation and prototyping.
Proprietary leads in average score, median score, model count, context window size, top model performance, capability coverage. The top proprietary model (Claude Fable 5.1) achieves a score of 96, setting the current performance ceiling.
Across 300 tracked models (104 open-source, 196 proprietary), the landscape continues to evolve rapidly. Open-source models excel for self-hosting, fine-tuning, and cost control, while proprietary models often lead in raw performance and managed API convenience.
Related
The gap is narrowing rapidly. Open-source models like DeepSeek, Qwen, and LLaMA now compete with proprietary models on many benchmarks. However, proprietary models often still lead in raw performance on the most demanding tasks.
Open-source models offer full transparency, self-hosting capability, fine-tuning freedom, no vendor lock-in, and often lower costs. They are ideal for privacy-sensitive applications and organizations that need full control over their AI stack.
The top-scoring open-source model is shown in our leaderboard above. Rankings update hourly based on composite scores that combine benchmarks, pricing, capabilities, and community adoption.
We classify models based on whether their weights are publicly available for download and modification. Models with open weights but restrictive licenses are still counted as open source for this comparison.