Mistral, LLaMA, Mixtral, and How They Compare

OpenAI and Anthropic APIs offer speed and convenience, but they also come with costs, rate limits, and limited control. As teams look to lower costs and gain more flexibility, self-hosting open-source large language models (LLMs) has become a serious alternative.

So what models are worth considering? And how does their performance compare to closed commercial options? This article gives a practical overview of top open-source models that can be deployed in production – and what trade-offs come with them.

*If you’re evaluating how to transition from API calls to a self-hosted or hybrid setup, teams like S-PRO help navigate model selection, architecture, and deployment without burning months on trial and error.

1. Mistral 7B

What it is: A dense transformer model with 7.3B parameters, released by Mistral AI. Trained for general-purpose language tasks.

Why it’s notable: It’s extremely fast and lightweight. On modern GPUs (A100 or even consumer-grade RTX 3090), Mistral 7B delivers solid performance with low latency.

Use cases: Chat, summarization, code assistance, classification

Performance: Comparable to GPT-3.5 in many benchmarks. Works well for real-time applications. Optimized versions (e.g., via vLLM or GGUF quantization) make it very cost-effective to run.

2. Mixtral 8x7B (Mixture of Experts)

What it is: A “mixture of experts” model using 8 Mistral-like 7B models, but only two are active per inference.

Why it’s notable: Much stronger than a single 7B model while keeping runtime and memory use efficient. You get bigger-model accuracy without running the full compute load.

Use cases: Long-form generation, chatbots, advanced summarization

Performance: Beats GPT-3.5 and gets close to GPT-4 in some tasks. Slightly higher infra requirements but still manageable for teams with access to mid-size GPU clusters.

Deployment tip: Mixture of experts routing requires optimized inference frameworks like vLLM or DeepSpeed-MoE.

3. LLaMA 2 (Meta)

What it is: Meta’s family of models, including LLaMA 2 7B, 13B, and 70B.

Why it’s notable: LLaMA 2 has strong community support, clean licensing for commercial use, and reliable performance across general NLP tasks.

Use cases: Chat interfaces, instruction-following bots, translation, QA

Performance: LLaMA 2 13B is a popular midpoint – better performance than Mistral 7B, with reasonable hardware requirements. LLaMA 2 70B rivals GPT-3.5 but needs serious GPU resources (multi-A100 setup).

Deployment note: Quantized versions using GGUF format can reduce memory footprint dramatically.

4. Other Notables

• Nous Hermes 2 / OpenHermes: Fine-tuned LLaMA models optimized for instruction-following.
• Phi-2 (Microsoft): A small 2.7B model focused on low-latency inference with strong code and reasoning ability.
• Command R+ (Reka): Open-weight models for multi-language and reasoning-heavy tasks.

These smaller models are excellent for embedding AI in edge apps, real-time analytics, or situations where budget or latency is a constraint.

5. Comparing Cost and Latency

Here’s a rough comparison (assuming use of A100 GPU, batch inference):

Note: Performance depends heavily on quantization, prompt size, and infra setup.

6. When Self-Hosting Makes Sense

• Your product depends on low-latency inference
• You want to reduce API costs at scale
• You need full control over the model behavior
• You’re operating in regulated environments where data privacy is critical

Hiring experienced AI developers is key. Model deployment, optimization, and observability require more than downloading weights. 

Open-source models have come a long way – and they’re no longer just research toys. With the right stack, Mistral and Mixtral can power real products with competitive speed and accuracy. If you are not sure where to start, start with a chat with an IT consulting company in the US to set things up properly.