Llama 3.3 — 70B
All-round flagship; excellent at multilingual conversations and tool use.
via ZelixAI Privacy Cluster →
What is this model?
Llama 3.3 is the third generation of Meta's open-source language-model family, in the 70-billion-parameter variant — the workhorse of the Llama line. Officially multilingual with strong performance in 30+ languages, native tool-use support (function-calling), and a 128K-token context window. As an open-weight model it is fully auditable, and we run it inside our EU cluster without your data ever touching Meta's own infrastructure.
Strengths
Strengths: best multilingual performance in the Privacy Cluster — Dutch, German, French, Spanish, Turkish, Arabic and more are handled fluently without quality drop-off. Native tool-use support makes it ideal for agent workflows where the bot has to call tools (databases, calendars, external APIs). The 128K context window opens the door to long-document RAG and historical conversation context.
Best suited for
- Multilingual conversations (30+ languages)
- Tool-use / function-calling workflows
- Complex reasoning and multi-step tasks
How ZelixAI uses this model
Within ZelixAI, Llama 3.3 70B is our recommendation for customers with multilingual customer service, agent bots that must call tools (such as the Customer Recognition or Order Status tool) and use cases that need both multilingualism and reasoning power. For a customer with Dutch clients plus international branches, this is often the natural starting point — superior to Mistral Small for languages outside the EU core.
Real-world examples within ZelixAI
Concrete praktijkvoorbeelden voor dit model worden binnenkort hier gepubliceerd. Stel intussen vragen via onze contactpagina — we delen graag relevante use-cases uit onze klantbasis.
Limitations and caveats
Limitations: slightly slower than Mistral Small (40–60 tokens/sec vs. 60–100), which is noticeable on longer answers. The larger model size results in slightly higher cost per inference than Mistral Small. We have observed the model occasionally returning type mismatches in tool arguments (string instead of int) — for critical tool calls we therefore always validate via a schema check at the ZelixAI tool layer.
Technical specifications
| Provider | ZelixAI Privacy Cluster |
| Context window | 131K tokens |
| Throughput | 40–100 tokens/s (Fast) |
| Cost tier | Very affordable |
| Tool / function-calling | yes |
| Data residency | EU (Netherlands · Germany · France) |