Llama 3 vs Mistral: Open Source Showdown
Llama 3 vs Mistral: Open Source Showdown
If you want to run AI models on your own hardware, Llama (Meta) and Mistral are the two leading options. Both are open-weight models that you can download, deploy, and customize freely. But they differ in architecture, efficiency, licensing, and ideal use cases. This guide compares them head-to-head.
AI model comparisons are based on publicly available benchmarks and editorial testing. Results may vary by use case.
Quick Summary
| Feature | Llama 3 | Mistral |
|---|---|---|
| Provider | Meta | Mistral AI |
| Sizes | 8B, 70B, 405B | 7B, 8x7B (Mixtral), 8x22B, Large |
| Architecture | Dense transformer | Dense + MoE (Mixtral) |
| License | Llama Community License | Apache 2.0 (smaller) / Commercial (larger) |
| Context Window | 128K | 32K-128K (varies by model) |
| Best For | Best overall open model | Efficiency, multilingual, European compliance |
Benchmark Comparison
| Benchmark | Llama 3 405B | Llama 3 70B | Llama 3 8B | Mistral Large | Mixtral 8x22B | Mistral 7B |
|---|---|---|---|---|---|---|
| MMLU | 86.1% | 82.0% | 68.4% | 84.0% | 77.8% | 64.2% |
| HumanEval | 81.2% | 72.3% | 55.1% | 75.6% | 65.4% | 48.9% |
| MATH | 68.4% | 55.2% | 35.7% | 58.1% | 48.3% | 30.5% |
| Multilingual | 80.1% | 75.3% | 62.8% | 83.5% | 74.6% | 61.2% |
| MT-Bench | 8.9 | 8.4 | 7.6 | 8.5 | 8.1 | 7.4 |
Benchmark scores are approximate and based on publicly reported results.
Detailed Comparison
Raw Performance
Llama 3 405B is the most capable open model available. It approaches frontier closed-source model performance on many benchmarks. At the 70B tier, Llama 3 70B outperforms Mistral’s comparable offerings. At the smallest tier (7-8B), the models are close, with slight advantages depending on the specific benchmark.
Efficiency and Architecture
Mistral’s Mixtral models use a Mixture of Experts (MoE) architecture, which activates only a subset of parameters for each token. This means Mixtral 8x22B has the capacity of a very large model but the inference speed of a smaller one. For organizations where inference cost and speed matter, MoE architecture is a significant advantage.
Llama 3 uses a dense transformer architecture, meaning all parameters are active for every token. This gives it better per-parameter performance but higher compute requirements.
Multilingual Support
Mistral has a notable edge in multilingual performance, particularly for European languages. This reflects Mistral’s European origins and training data emphasis. If your application needs strong French, German, Spanish, Italian, or other European language support, Mistral is often the better choice.
Licensing
Llama 3 uses Meta’s custom community license, which is permissive for most uses but includes some restrictions (e.g., companies with over 700 million monthly active users need a separate license). Mistral’s smaller models use the Apache 2.0 license, which is more permissive. Larger Mistral models have commercial licenses.
For most businesses, both licenses are fine. Check the specifics if you are a very large organization.
Fine-Tuning Ecosystem
Both models have robust fine-tuning ecosystems with support from tools like Hugging Face, Axolotl, and various cloud providers. Llama has a slightly larger community and more publicly available fine-tuned variants due to its earlier release and Meta’s active community engagement.
Hardware Requirements
| Model | Minimum VRAM (FP16) | Minimum VRAM (Quantized) | Practical Hardware |
|---|---|---|---|
| Llama 3 8B | 16 GB | 6 GB (4-bit) | Single consumer GPU |
| Mistral 7B | 14 GB | 5 GB (4-bit) | Single consumer GPU |
| Llama 3 70B | 140 GB | 40 GB (4-bit) | Multi-GPU or cloud |
| Mixtral 8x22B | ~90 GB | ~45 GB (4-bit) | Multi-GPU or cloud |
| Llama 3 405B | 810 GB | ~200 GB (4-bit) | Multi-node cluster |
How to Run Llama Locally: Setup Guide
Pros and Cons
Llama 3
Pros:
- Highest performance among open models (405B)
- Large and active community
- Strong 128K context window
- Extensive fine-tuned variant ecosystem
- Well-documented, backed by Meta
Cons:
- Dense architecture means higher compute costs
- Custom license (not pure open source)
- 405B requires significant infrastructure
- Weaker multilingual support than Mistral
Mistral
Pros:
- MoE architecture offers better efficiency
- Superior multilingual performance (especially European languages)
- Apache 2.0 licensing for smaller models
- European provider (data sovereignty benefits)
- Good performance relative to compute requirements
Cons:
- Lower peak performance than Llama 3 405B
- Smaller community and fewer fine-tuned variants
- Larger models have commercial licenses
- Smaller context windows on some models
Best Use Cases
Choose Llama 3 when:
- You need the highest performance from an open model
- English-language tasks are primary
- You have adequate GPU infrastructure
- You want the largest fine-tuning community
- Large context window (128K) is important
Choose Mistral when:
- Inference efficiency and speed are priorities
- Multilingual support (especially European languages) is important
- European data sovereignty matters for your organization
- You want Apache 2.0 licensing
- You need strong performance with limited GPU resources (Mixtral MoE)
Our Recommendation
For most users who want the best open model regardless of efficiency, Llama 3 (at the 70B or 405B tier) is the top choice. For organizations that prioritize efficiency, multilingual support, or European compliance, Mistral is the better fit. At the smallest tier (7-8B), both are excellent choices for running on consumer hardware.
Many organizations use both: Llama for English-centric tasks and Mistral for multilingual or efficiency-sensitive workloads.
Key Takeaways
- Llama 3 405B is the most capable open model available, approaching closed-source performance on many benchmarks.
- Mistral’s MoE architecture offers better efficiency (more capability per compute dollar).
- Mistral leads on multilingual support; Llama leads on raw English-language performance.
- Both have strong fine-tuning ecosystems and are suitable for production deployment.
- At the smallest tier (7-8B), both run on consumer hardware and are close in capability.
Next Steps
- Learn to run models locally with our setup guide: How to Run Llama Locally: Setup Guide.
- Explore the best local AI models for privacy: Best Local/On-Device AI Models for Privacy.
- Compare open vs. closed source approaches: Open Source vs Closed Source AI: Pros, Cons, and When Each Wins.
- See all model benchmarks on our leaderboard: AI Benchmark Leaderboard: MMLU, HumanEval, MATH.
This content is for informational purposes only and reflects independently researched comparisons. AI model capabilities change frequently — verify current specs with providers. Not professional advice.