Open Weights vs Proprietary: What's the Real Difference?
The gap between open and closed models has never been smaller. Here's how to choose the right approach for your needs.
Key Differences at a Glance
| Factor | Open Weights | Proprietary |
|---|---|---|
| Cost | Free (self-hosted) or ~$0.10-1/M tokens via API | $1-25/M tokens (API only) |
| Privacy | Full control — data never leaves your infrastructure | Data sent to provider's servers |
| Customization | Fine-tune, modify, distill freely | Limited to provider's fine-tuning options |
| Performance | 90-97% of frontier on most benchmarks | Best-in-class on most benchmarks |
| Availability | Run offline, no rate limits (self-hosted) | Dependent on provider uptime |
| Setup Complexity | Requires hardware + setup | API key and go |
| Updates | Manual — you control when to upgrade | Automatic — provider updates for you |
Performance: The Gap is Closing
Open-weights models now achieve near-frontier performance on key benchmarks
Coding (LiveCodeBench)
Math (AIME 2025)
Reasoning (MMLU-Pro)
Sources: WhatLLM.org, ArtificialAnalysis.ai, model technical reports (February-March 2026)
When to Use Each
Choose Open Weights When:
- ✓ Privacy is non-negotiable (healthcare, finance, legal)
- ✓ You have predictable, high-volume workloads
- ✓ You need to fine-tune on proprietary data
- ✓ Offline/air-gapped operation is required
- ✓ You want to avoid vendor lock-in
- ✓ You have GPU infrastructure available
- ✓ Long-term cost optimization matters
Choose Proprietary When:
- ✓ You need absolute best performance
- ✓ Getting started fast matters more than cost
- ✓ You don't have GPU infrastructure
- ✓ Workloads are unpredictable/sporadic
- ✓ You need the latest model improvements automatically
- ✓ Complex multimodal tasks (video, advanced vision)
- ✓ You want managed tool integration
💡 The Hybrid Approach
Many teams use both: proprietary APIs for prototyping and complex tasks, self-hosted open weights for production and high-volume workloads. This gives you the best of both worlds — frontier performance when you need it, cost control where it matters.
Cost Analysis: API vs Self-Hosted
Example: Processing 10M tokens/day
Proprietary API
(GPT-5.4 @ $2.50/$15 per M)
Open Weights API
(GLM-5 @ $0.50/$2 per M)
Self-Hosted
(GPT-OSS-120B on H100)
Self-hosting breaks even vs proprietary at ~3M tokens/day. At 50M+/day, savings are massive.
Ready to Go Local?
Check out our guide to running frontier models on your own hardware.
Local Models Guide →