The battle for open-source AI supremacy has effectively turned into a two-horse race. On one side, we have Meta (Facebook), the corporate giant that changed the world with the release of Llama. On the other side, we have Mistral AI, the scrappy French startup that shocked the industry with its highly efficient models.
For developers, data scientists, and hobbyists running local LLMs, the choice is no longer simple. You aren’t just choosing a brand; you are choosing an ecosystem. To make the right decision, you need a detailed Llama 3 vs Mistral benchmark comparison.
In this comprehensive guide, we move beyond the marketing hype. We will analyze the raw data, looking at coding performance (HumanEval), general reasoning (MMLU), and math skills (GSM8K). We will break down the Llama 3 vs Mistral benchmark comparison across different model sizes—from the lightweight 7B/8B models that run on laptops to the massive 70B+ models that require enterprise GPUs.
Table of Contents
The Contenders: Understanding the Architecture
Before diving into the numbers of the Llama 3 vs Mistral benchmark comparison, it is crucial to understand what we are comparing. These models use different architectures to achieve their results.
Llama 3 (The Powerhouse)
Meta’s Llama 3 focuses on raw power and massive training data.
- Training Data: Trained on over 15 trillion tokens (7x more than Llama 2).
- Vocabulary: Uses a larger tokenizer (128k vocabulary size), making it more efficient at encoding text.
- Philosophy: “Train it longer, make it smarter.”
Mistral / Mixtral (The Efficient Architect)
Mistral AI often uses a “Mixture of Experts” (MoE) architecture (in their Mixtral line).
- MoE Explained: Instead of using all parameters for every prompt, it only uses a fraction. This makes inference faster and cheaper.
- Philosophy: “Compute efficiency above all else.”
When we look at a Llama 3 vs Mistral benchmark comparison, we are often comparing a dense model (Llama) against a sparse model (Mixtral).
Round 1: The “Small” Model Battle (8B vs 7B)
For most users reading this, the “small” models are the most important. These are the models you can run locally on a MacBook M1 or a consumer gaming PC. Here, we look at the Llama 3 vs Mistral benchmark comparison for the 8B and 7B weight classes.
The Specs
- Llama 3 8B: 8 Billion Parameters.
- Mistral 7B (v0.3): 7 Billion Parameters.
Benchmark Results (MMLU & Reasoning)
The Massive Multitask Language Understanding (MMLU) benchmark tests general knowledge and problem-solving.
- Llama 3 8B: Scores approx 68.4%.
- Mistral 7B: Scores approx 62.5%.
In this specific Llama 3 vs Mistral benchmark comparison, Meta has achieved a significant leap. Llama 3 8B punches way above its weight class, often beating older models that were 4x its size (like Llama 2 70B).
Coding and Math (HumanEval & GSM8K)
For developers, coding ability is paramount.
- Llama 3 8B (HumanEval): ~62%
- Mistral 7B (HumanEval): ~40%
The Verdict: In the lightweight category of the Llama 3 vs Mistral benchmark comparison, Llama 3 8B is the clear winner. The massive amount of training data Meta used has resulted in a “smarter” model at the same file size.
Round 2: The Heavyweights (70B vs Mixtral 8x22B)
Now, let’s look at the models used by enterprises and users with dual-GPU setups. This Llama 3 vs Mistral benchmark comparison pits Llama 3 70B against Mixtral 8x22B.
Benchmark Results (MMLU)
- Llama 3 70B: Scores an impressive 82%. This puts it in the same tier as GPT-4 (original version).
- Mixtral 8x22B: Scores approx 77%.
While Mixtral is highly efficient, the Llama 3 vs Mistral benchmark comparison shows that Llama 3 70B holds a distinct advantage in general knowledge and reasoning tasks.
The “Context Window” Factor
However, raw intelligence isn’t everything. We must include context length in our Llama 3 vs Mistral benchmark comparison.
- Llama 3: Standard context is 8k (expandable, but native is low).
- Mistral: Often supports 32k or even 64k natively.
If you need to summarize massive PDF documents, the Llama 3 vs Mistral benchmark comparison tips in favor of Mistral. Llama 3 is smarter sentence-by-sentence, but Mistral can hold more information in its “working memory” at once without complex engineering tricks.
Round 3: Coding Proficiency (CodeLlama vs Codestral)
Mistral recently released “Codestral,” a model specifically designed for code. How does it fare in a Llama 3 vs Mistral benchmark comparison?
Coding benchmarks are volatile, but general consensus and HumanEval scores suggest a tight race.
- Llama 3 70B: It is an excellent generalist coder. It follows instructions well and explains the code.
- Mistral (Codestral): It is faster and optimized for “Fill-in-the-middle” (FIM) tasks, which is what autocomplete tools (like GitHub Copilot) use.
If you are building an autocomplete plugin, the Llama 3 vs Mistral benchmark comparison favors Mistral/Codestral due to its speed and FIM capabilities. If you are using a chat interface to debug a complex error, Llama 3’s superior reasoning makes it the better choice.
Real-World Usage: The “Vibe” Check
Benchmarks are just numbers. How do these models feel to use? A proper Llama 3 vs Mistral benchmark comparison must include subjective user experience.
Llama 3’s Personality
Llama 3 is known for being slightly more “refused” (safe) out of the box, although the 3.1 updates have relaxed this. It is extremely verbose and helpful.
- Pros: Very articulate, rarely hallucinates on facts.
- Cons: Can be “preachy” regarding safety guidelines unless you use an uncensored fine-tune (like Dolphin-Llama-3).
Mistral’s Personality
Mistral models are famously less filtered. In the community, they are preferred for creative writing and roleplay.
- Pros: Concise, follows instructions without lecturing, feels more “neutral.”
- Cons: Can sometimes be too brief if you don’t prompt it to elaborate.
In a subjective Llama 3 vs Mistral benchmark comparison, creative writers usually prefer Mistral, while business professionals prefer Llama 3.
Hardware Efficiency and Cost
For those running local LLMs, “tokens per second” (TPS) is a critical metric in any Llama 3 vs Mistral benchmark comparison.
Speed (Inference)
Because Mistral often uses Mixture of Experts (MoE), it only activates a few billion parameters per token generation, even if the total model size is huge.
- Result: Mixtral 8x7B runs significantly faster than Llama 3 70B on the same hardware.
VRAM Requirements
- Llama 3 8B: Fits on 6GB VRAM (accessible to almost everyone).
- Mistral 7B: Fits on 6GB VRAM.
- Llama 3 70B: Requires ~40GB VRAM (needs 2x RTX 3090s or 4090s).
- Mixtral 8x7B: Requires ~24GB VRAM (fits on a single RTX 3090/4090).
This is a critical point in the Llama 3 vs Mistral benchmark comparison. If you only have one high-end GPU (24GB VRAM), you cannot run Llama 3 70B at a decent quantization, but you can run Mixtral. Therefore, for single-GPU owners, Mistral wins by default.
The Verdict: Which Model Should You Use?
After analyzing the data in this Llama 3 vs Mistral benchmark comparison, we can draw clear conclusions based on your use case.
Choose Llama 3 If:
- You want the absolute smartest model: Llama 3 70B is currently the king of open weights. It rivals GPT-4 in reasoning.
- You have limited hardware (8GB RAM): The Llama 3 8B is the smartest “tiny” model ever made.
- You need general knowledge: Its 15T token training set covers more world facts.
Choose Mistral If:
- You have a single 24GB GPU: Mixtral 8x7B is the best model that fits on a consumer flagship card.
- You want fewer refusals: Mistral is generally easier to steer for creative or edgy topics.
- You need Long Context: If you are analyzing long documents, Mistral’s native context handling is superior.
The Llama 3 vs Mistral benchmark comparison shows that while Meta has won on raw IQ points, Mistral remains the champion of efficiency and accessibility.
Future Outlook
The AI race moves fast. As we wrap up this Llama 3 vs Mistral benchmark comparison, both companies are already working on their next iterations. Meta is training Llama 4 on even larger clusters, and Mistral is refining its “Large” proprietary models.
However, for 2026, the open-source community is the real winner. Whether you choose Llama or Mistral, you have access to technology that was science fiction just five years ago—running entirely on your own computer, for free.
