Open-source vs closed LLMs
The gap between open-weight and closed frontier LLMs is the smallest it's ever been. Here's how they compare on cost, capability, privacy and lock-in in 2026.
| Spec | Open-source | Closed / frontier |
|---|---|---|
| Examples | Llama, Mistral, DeepSeek, Qwen, Gemma | GPT-5, Claude, Gemini, Grok |
| Weights | Public — self-host or hosted API | Private — provider API only |
| Top reasoning | DeepSeek-R1, Llama 4, Qwen 3 | GPT-5, Claude Opus 4 |
| Cost (per Mtok) | $0.10–$3 (hosted), near-zero self-hosted | $3–$75 |
| Data privacy | Full control if self-hosted | Provider sees inputs/outputs |
| Fine-tuning | Full SFT, LoRA, RLHF on your data | Limited hosted fine-tuning |
| Latency floor | Sub-100ms on Groq, Cerebras | 200–800ms typical |
| Lock-in | Low — portable across providers | High — model + tools tied to one API |
When to choose open-source
- You need to keep data on your own infrastructure.
- You're running high-volume inference and want to cut per-token cost by 10–50×.
- You want to fine-tune deeply on private data.
- You want portability across multiple providers (Groq, Together, Fireworks, AWS).
When to choose closed frontier
- You need the absolute strongest reasoning or agentic tool-use today.
- You want first-class multimodality with audio and video.
- You don't want to operate inference yourself.
FAQ
Are open-source LLMs as good as closed ones?
On reasoning and coding, the top open models (DeepSeek-R1, Llama 4, Qwen 3) are within ~5–10% of GPT-5 and Claude Opus 4 on most public benchmarks. On agentic tool use and very long-horizon tasks, closed frontier models still lead.
Is open-source LLM cheaper?
Yes — usually 5–50× cheaper per token on hosted providers like Together, Fireworks and Groq, and effectively free at scale if you self-host on your own GPUs.
Which open-source LLM should I pick?
Llama 4 for general-purpose agents and longest context; Mistral / Codestral for code and EU hosting; DeepSeek-R1 for reasoning at low cost; Qwen 3 for multilingual.
Is 'open-source' actually open?
Llama uses Meta's community license (liberal but restricted above 700M MAU). Mistral, DeepSeek and Qwen ship most weights under Apache 2.0 or similar — closer to true open source.