About 16; min
The 70B parameter class is the sweet spot for open-source LLMs — large enough to handle complex reasoning, small enough to run on a single high-end GPU. Mistral Large and Llama 3.1 70B are the two most popular options at this size. Mistral comes from France’s leading AI lab with a focus on European multilingual capabilities. Llama 3.1 70B is Meta’s workhorse model with the largest community support. Here’s how they compare.
Quick Model Overview
| Spec | Mistral Large 2 | Llama 3.1 70B |
|---|---|---|
| Developer | Mistral AI (France) | Meta (USA) |
| Parameters | 123B | 70B |
| Context Window | 128K tokens | 128K tokens |
| License | Mistral Research License | Llama 3.1 Community License |
| Languages | 12 languages (strong European) | 8 languages (English-focused) |
| Function Calling | Native support | Native support |
| VRAM (Q4) | ~72GB | ~48GB |
| API Price (input/M) | $2.00 (Mistral API) | $0.88 (Together AI) |
Mistral Large 2 Overview
Mistral Large 2 is a 123B parameter model that represents Europe’s strongest AI entry. The model excels at multilingual tasks — French, German, Spanish, Italian, Portuguese, Dutch, Russian, Chinese, Japanese, Korean, Arabic, and Hindi all perform at high levels alongside English. The Mistral Research License allows research and testing use; commercial use requires contacting Mistral for licensing terms. Through the Mistral API (La Plateforme), input costs $2.00 per million tokens and output costs $6.00 per million. Mistral Large 2 also runs through AWS Bedrock, Azure AI, and Google Cloud Vertex AI. The model includes native function calling with parallel tool execution — important for building AI agents that interact with external APIs. Code generation quality is competitive with Llama 3.1 70B, and instruction following is precise.
Llama 3.1 70B Overview
Llama 3.1 70B is the most widely deployed open-source model in its size class. At 70 billion parameters, it fits on a single A100 80GB or runs quantized on 2x RTX 4090s (48GB total). The Llama 3.1 Community License allows commercial use for organizations under 700 million MAUs. API access through Together AI costs $0.88 per million tokens, Groq charges $0.59 input / $0.79 output, and AWS Bedrock offers on-demand pricing. The fine-tuning community is the largest of any open model — thousands of specialized variants exist on Hugging Face. Llama 3.1 70B handles English-centric tasks exceptionally well, with solid but not leading multilingual support across 8 languages. The model supports function calling, JSON mode, and 128K context windows across all deployment options.
Benchmark Comparison
| Benchmark | Mistral Large 2 (123B) | Llama 3.1 70B |
|---|---|---|
| MMLU | 84.0 | 82.0 |
| HumanEval (coding) | 84.1 | 80.5 |
| GSM8K (math) | 91.2 | 83.7 |
| MATH | 54.0 | 47.2 |
| MT-Bench | 8.6 | 8.6 |
| ARC Challenge | 88.2 | 87.3 |
| IFEval (instruction) | 84.5 | 80.4 |
| Multilingual (avg) | Excellent (12 languages) | Good (8 languages) |
| French performance | Best-in-class | Moderate |
Key Differences
Size vs Performance
Mistral Large 2 has 123 billion parameters — nearly double Llama’s 70B. This gives Mistral higher benchmark scores on most tasks but requires more VRAM for self-hosting (72GB quantized vs 48GB). For API users, the size difference doesn’t matter — you’re paying per token regardless. For self-hosting, Llama 3.1 70B’s smaller footprint means it runs on more affordable hardware configurations.
Multilingual Capability
Mistral Large 2 significantly outperforms Llama 3.1 70B on non-English tasks. For European languages (French, German, Spanish, Italian), Mistral’s scores are 10-15% higher. For businesses serving European markets, building multilingual chatbots, or processing documents in multiple languages, Mistral provides noticeably better quality. Llama 3.1 70B is primarily English-focused with acceptable but not leading multilingual performance.
Licensing
Llama 3.1’s Community License is more permissive for commercial use — free for any organization under 700M MAUs. Mistral Large 2’s Research License requires commercial licensing arrangements for production use. For companies deploying at scale, Llama’s license is simpler. For research and evaluation, both are freely accessible.
Community and Tooling
Llama 3.1 70B has a dramatically larger community. More fine-tuned variants, more tutorials, more deployment guides, and wider tooling support. Every ML platform, inference framework, and fine-tuning tool supports Llama as a first-class citizen. Mistral is well-supported but the community is smaller — fewer fine-tunes, fewer domain-specific variants, and fewer getting-started resources.
API Cost Comparison
| Provider | Mistral Large 2 | Llama 3.1 70B |
|---|---|---|
| Native API | $2.00 in / $6.00 out | N/A (Meta doesn’t host) |
| Together AI | $1.20 in / $1.20 out | $0.88 in / $0.88 out |
| Groq | N/A | $0.59 in / $0.79 out |
| AWS Bedrock | $2.00 in / $6.00 out | $2.65 in / $3.50 out |
| Self-hosted (VRAM) | ~72GB (Q4) | ~48GB (Q4) |
Who Should Pick What
Pick Mistral Large 2 if:
- Multilingual support (especially European languages) is a core requirement
- Slightly higher benchmark performance justifies the larger model size
- Strong function calling with parallel tool execution powers your AI agent workflow
- You’re building for European markets and value a European AI provider
Pick Llama 3.1 70B if:
- The largest fine-tuning community gives you domain-specific variants
- More permissive licensing simplifies commercial deployment
- Smaller model size (70B vs 123B) fits your hardware budget
- Wider API availability (Groq, Together, Fireworks) gives you pricing options
Llama 3.1 70B wins for most teams because it delivers strong performance at a lower hardware cost with the most permissive licensing and largest community. The 70B size runs on more accessible hardware than Mistral’s 123B, API pricing is lower across all providers, and the fine-tuning community provides ready-made variants for nearly every domain. Mistral Large 2 earns runner-up for superior multilingual performance and slightly higher benchmarks — for European-market products and applications requiring strong non-English capabilities, Mistral is the better technical choice. Both models represent the state of the art for commercially available open-weight LLMs.
FAQ
Can I run Mistral Large 2 locally?
At 123B parameters, quantized (Q4) weights need approximately 72GB VRAM — an A100 80GB, 2x A6000, or 3x RTX 4090 (72GB total). It’s feasible but expensive. Llama 3.1 70B fits on more modest setups (single A100 or 2x RTX 4090).
Is Mistral better than Llama for French?
Yes, significantly. Mistral Large 2 was developed by a French company with explicit focus on French language performance. French benchmark scores are 10-15% higher than Llama 3.1 70B. For French-language applications, Mistral is the clear choice.
Which should I fine-tune?
Llama 3.1 70B. The community has more fine-tuning resources, tutorials, and proven recipes. LoRA and QLoRA fine-tuning on Llama is well-documented across Unsloth, Axolotl, and Hugging Face TRL. Mistral fine-tuning works but has fewer community examples.
Does Mixtral compare to these models?
Mixtral 8x7B (46B total, 13B active via MoE) is smaller and less capable than both Mistral Large 2 and Llama 3.1 70B. It runs faster and cheaper but scores 5-15% lower on benchmarks. Mixtral is better compared to Llama 3.1 8B and Qwen 2.5 7B in its performance tier.
