In one sentence
Open-source LLMs (Mistral, Llama, DeepSeek) and proprietary LLMs (GPT, Claude, Gemini) each have their strengths. The right choice depends on 3 criteria: control, total cost, and raw performance. We'll help you decide.
🚀 Want to compare side-by-side?
30 open-source AND proprietary models compared: price, performance, language, licences.
Open source: what exactly is it?
An open-source LLM is a model whose weights (the billions of parameters) are publicly downloadable. You can:
- Run it on your server, your computer, or even in the cloud
- Modify it (fine-tuning on your data)
- Redistribute it (according to the licence)
- Inspect it (total transparency)
Proprietary: what exactly is it?
A proprietary LLM is a model accessible only via an API (or chatbot) hosted by the provider. You never have access to the weights. The provider maintains total control over:
- The infrastructure (latency, availability)
- Updates (can change behaviour without warning)
- The price (can increase)
- The data (yours necessarily passes through it)
The big match: Open vs Proprietary
Open source vs Proprietary
| 🔓Open source | 🔒Proprietary | |
|---|---|---|
| Data control | Total (your servers) | Limited (their servers) |
| Raw performance (top level) | Very good (Llama 4, DeepSeek) | State-of-the-art (GPT-5, Opus 4.7) |
| Cost (low volume) | High (infra to pay) | Very low (pay-per-use) |
| Cost (high volume) | Very low (~£0.01/1k tokens) | Expensive (~£0.15/1k tokens top API) |
| Time-to-market | Slow (setup, monitoring) | Immediate (1 line of code) |
| Customisation (fine-tuning) | Total and free | Limited or expensive |
| GDPR compliance | Easy (EU/local) | Complicated (US transfers) |
| Updates | You alone decide | Imposed by provider |
| Support / Documentation | Community (variable) | Professional (SLA, team) |
Cost: the great illusion
Many think open source = free. That's false. Here's the reality:
Monthly cost for 100M tokens/month
Reading the chart:
- At low volume, proprietary APIs are cheaper
- At high volume (100M+ tokens/month), open source becomes competitive
- The break-even is around 50M tokens/month (~5,000-10,000 active users)
Practical cases: what to choose?
📚6 common situations and the right decision
1. "I'm testing a POC in 1 week"
Choice: Proprietary API (Claude, GPT, Mistral) Why: you want immediate results, no infrastructure to set up. The cost for 1 week of tests is negligible.
2. "I have a startup with 500 active users"
Choice: Proprietary API Why: your volume remains low (<10M tokens/month). Open source would cost more in infrastructure than an API.
3. "I have 50,000 active users in production"
Choice: Self-hosted open source (or hybrid) Why: your volume justifies the infrastructure investment. Savings of 50-70% vs proprietary API at this stage.
4. "I process medical / financial data"
Choice: Local open source OR proprietary Enterprise offering with DPA Why: GDPR. Your data must not leave a controlled environment.
5. "I want to customise the model (style, industry vocabulary)"
Choice: Open source (fine-tuning on Mistral or Llama) Why: fine-tuning on proprietary API exists but is limited, expensive, and depends on the provider.
6. "I want the best possible performance"
Choice: Proprietary (Claude Opus 4.7 or GPT-5) Why: proprietary "frontier" models maintain a 6-12 month lead over the best open source.
The best open-source LLMs in 2026
Top open source 2026
| 🏆Model | 🎯For whom? | |
|---|---|---|
| Mistral Large 3 (Mistral AI, FR) | 123B params - excellent FR/EN | GDPR-friendly Europeans |
| Llama 4 (Meta) | 70B / 405B params - generalist | Classic US usage |
| DeepSeek V3 | 671B MoE (37B active) - top performance | Research, reasoning |
| Qwen 3 (Alibaba) | Multilingual, fast | Asia, multilingual |
| Gemma 3 (Google) | Small model (2B-9B) | Edge, mobile, low-resource |
The evolution open vs proprietary
The gap narrows every year
GPT-3 vs GPT-J
Huge gap. GPT-3 (proprietary) crushes GPT-J (open). 18 months behind.
GPT-4 vs Llama 2
Gap reduces to ~12 months. Llama 2 70B is competitive with GPT-3.5.
Claude 3.5 vs Llama 3
Llama 3 70B reaches GPT-4 level. ~6 months gap.
DeepSeek V3 surprises everyone
Chinese open model at 671B MoE, GPT-4o level for ~6% of training cost.
Open source catches up
Llama 4, DeepSeek V3, Mistral Large 3 are at 90-95% of frontier proprietary models. The gap is now 3-6 months.
The hybrid strategy: best of both worlds
Pitfalls to avoid
The metaphor that sums it all up
Key takeaways
- ✅ Open source ≠ free: count ~£3,000/month minimum for a serious deployment
- ✅ Proprietary is the right default choice to start / low volume
- ✅ Open source becomes cost-effective above 50M tokens/month
- ✅ Hybrid strategy = optimum for most cases
- ✅ Always keep an alternative to avoid vendor lock-in
The right choice depends on YOUR context: volume, criticality, GDPR constraints, technical team. No universal answer.
From what monthly volume does open source generally become cost-effective?
Going further
- 💰 Pay or not for AI?, the API vs free decision grid
- 🛠️ 10 essential free AI tools
- 🔒 AI and confidentiality : GDPR implications