The Landscape Has Shifted
Two years ago, the conversation was simple: if you need the best results, use a proprietary model. GPT-4 and Claude sat at the top of every benchmark. Open-source models were for hobbyists and budget use cases.
That is no longer true.
Benchmark Parity
On coding benchmarks like HumanEval and SWE-bench, Llama 3.3 70B now matches GPT-4o on most tasks. DeepSeek R1 outperforms o1 on several math reasoning benchmarks. Qwen 2.5 72B leads all open-source models on multilingual tasks.
The Cost Advantage
Open-source models are served at a fraction of the cost of proprietary alternatives. On Infyrence, Llama 3.3 70B costs $0.59 per million input tokens versus $2.50 for GPT-4o - a 4x difference with comparable quality on many tasks.
When to Use Each
Choose proprietary when:
- You need absolute state-of-the-art reasoning
- Vision or multimodal tasks are critical
- You need guaranteed SLAs
Choose open-source when:
- Cost efficiency is a priority
- You want to fine-tune on proprietary data
- You need on-premise deployment
The right answer is often a mix of both - which is exactly what Infyrence makes easy.