import ComparisonTable from ’../../components/ComparisonTable.astro’;
Open-source LLMs are now competitive with frontier proprietary models for many tasks. Mistral and Meta’s Llama 3 are the leading contenders. Here’s how they compare for deployment decisions.
Quick Verdict
Choose Llama 3 if: You need the strongest open-source performance, wide community support, and flexibility for fine-tuning.
Choose Mistral if: You want efficient models with European AI compliance (EU data residency via Mistral’s API) or need specialized models (code, math).
Model Lineup
Mistral
- Mistral 7B: Fast, efficient, 7B parameters
- Mistral Nemo 12B: Strong 12B model
- Mistral Small 3 24B: Best Mistral open model
- Codestral: Code-specialized model
- Mixtral 8x7B: MoE architecture, efficient inference
Llama 3
- Llama 3.2 1B/3B: Edge/mobile deployment
- Llama 3.2 11B/90B: Vision-capable models
- Llama 3.3 70B: Flagship open-source model
- Llama 3.1 405B: Near-frontier capability
Benchmark Comparison
<ComparisonTable headers={[“Benchmark”, “Mistral Small 3 (24B)”, “Llama 3.3 (70B)”]} rows={[ [“MMLU”, “~81%”, “~86%”], [“HumanEval (coding)”, “~78%”, “~82%”], [“MATH”, “~70%”, “~77%”], [“GPQA”, “~52%”, “~58%”], [“Context window”, “128K”, “128K”], [“Inference speed (relative)”, “Faster (smaller)”, “Slower (larger)”], ]} />
Llama 3.3 70B performs better but requires more compute. For equal-size comparisons, Mistral is competitive.
Licensing
Mistral
- Models published under Apache 2.0 license
- Commercial use allowed
- No restrictions on derivatives
Llama 3
- Custom Meta Llama 3 license
- Commercial use allowed (with restrictions over 700M monthly users)
- Attribution required
For most businesses: Both are commercially usable. Meta’s license has edge cases worth reviewing for very large deployments.
Deployment Options
Mistral
- Self-host: Download from Hugging Face
- Mistral API (la Plateforme): EU-based cloud
- Via Ollama, vLLM, llama.cpp
- AWS, Azure, Google Cloud available
Llama 3
- Self-host: Llama.cpp, Ollama, vLLM
- Via every major cloud provider
- Groq (fast inference), Fireworks, Together.ai
- Massive community deployment support
Llama 3 has broader infrastructure support due to Meta’s backing and community investment.
Specialized Use Cases
Mistral’s advantage: Codestral is one of the best open-source code models. For pure coding applications, Codestral often outperforms Llama at similar size.
Llama’s advantage: Llama 3.2 multimodal models handle vision tasks. No Mistral equivalent at the open-source tier.
Fine-Tuning
Both support LoRA/QLoRA fine-tuning. The community ecosystem around Llama 3 fine-tuning is larger — more tutorials, pre-existing fine-tuned variants, and tooling. For organizations planning custom fine-tunes, Llama’s community resources are an advantage.
EU Data Considerations
Mistral is a French company with EU-based infrastructure (la Plateforme). For European businesses with GDPR requirements and EU data residency needs, Mistral’s API is a compliance-friendly option without running your own infrastructure.
Choosing Your Stack
| Requirement | Recommendation |
|---|---|
| Best 70B performance | Llama 3.3 70B |
| Best <15B performance | Mistral Small 3 or Llama 3.1 8B |
| Code generation | Codestral (Mistral) |
| Vision/multimodal | Llama 3.2 11B/90B |
| EU data residency API | Mistral la Plateforme |
| Edge/mobile deployment | Llama 3.2 1B/3B |
| Fine-tuning ecosystem | Llama 3 |
Bottom Line
Llama 3.3 70B leads at the top of the open-source performance stack. Mistral’s family is competitive at smaller sizes and wins on EU compliance and specialized models (code). Most organizations should evaluate both at their target size class — performance differences at the same parameter count are often minimal in practice.