Mistral vs. Llama

In the rapidly evolving world of large language models (LLMs), choosing the right model is not just about size but efficiency, performance, and practicality.

With models like Mistral and Llama pushing the boundaries of what’s possible, developers face a critical question: Which model delivers the best balance of power and efficiency for real-world applications?

This blog will compare the various models under the Mistral and Llama families, evaluating their capabilities, use cases, and performance.

Llama: Large language model Meta AI#

Llama, first released in February 2023 by Meta, is a family of large language models (LLMs) designed for research, experimentation, and accessibility. Llama models are available in varying sizes, ranging from 1–405 billion parameters. As of January 15, 2025, there are three main versions of Llama, Llama 1, Llama 2, and Llama 3, with sub-versions tailored for specific use cases. All Llama models are based on transformer architecture. The table below outlines a series of Llama models, including their release dates, available parameter sizes, and key capabilities.

Mistral models: Efficient and powerful#

For the first time, Mistral, released in October 2023, achieved a remarkable balance between performance and efficiency. With only 7 billion parameters, Mistral outperformed Llama 2 (13B) on various benchmarks and surpassed Llama 1 (33B) in reasoning, mathematics, and code generation tasks. Mistral achieved this efficiency through careful design choices and focusing on reducing computational overhead using different techniques. The table below highlights notable Mistral models, including their release dates, available parameter sizes, underlying architectures, and capabilities.

Architectural differences#

Mistral models follow two main transformer-based architectural styles: dense transformer and Mixture of Experts (MoE) transformer, each designed for different efficiency and performance trade-offs.

As seen in Mistral 7B, the dense transformer architecture ensures that all model parameters are utilized for every input. This design benefits from optimizations like grouped query attention (GQA) for faster inference and sliding window attention (SWA) to handle longer contexts better. The result is a compact yet highly efficient model, well-suited for various applications where a balance of performance and cost matters.

On the other hand, the Mixture of Experts (MoE) transformer architecture, exemplified by Mixtral 8x7B, introduces a sparse computation approach. It consists of eight expert blocks but activates only two per token, significantly reducing computational overhead while maintaining high model capacity. This structure allows for better scaling, offering improved efficiency without the full cost of a densely activated model. By leveraging MoE, Mixtral achieves a strong balance between model size, inference speed, and cost-effectiveness.

Both architectures are optimized for modern AI applications, with the dense model offering streamlined, consistent performance and the MoE model maximizing efficiency by selectively using computational resources. This flexibility makes Mistral models well-suited for various tasks, from real-time inference to large-scale deployments requiring optimal trade-offs between cost and accuracy.

Beyond transformers, Mistral has also explored state-space models (SSMs) in its Codestral Mamba 7B, leveraging the Mamba 2 architecture. Unlike traditional transformer-based models, Mamba 2 is not a further optimization of transformers but rather an alternative approach that eliminates self-attention in favor of selective memory updates and state-space representations. The Mamba 2 architecture is designed to handle long-context processing more efficiently by replacing self-attention mechanisms with a continuous-time state-space formulation. This allows the model to maintain memory over long sequences without the quadratic complexity associated with transformers.

While Mistral models introduce efficiency-focused innovations like the sparse Mixture of Experts (MoE), the Llama family has made significant strides in architectural enhancements that improve training, fine-tuning, and scalability. Llama 2 introduced refinements in training stability, allowing better optimization and gradient updates, which resulted in models that are easier to fine-tune. Llama 3 further improved efficiency in adapting to domain-specific applications. Llama models benefit from Meta’s large-scale distributed training, which enables efficient scaling across billions of parameters. This helps in deploying models effectively across different hardware configurations. Llama 3 models, particularly Llama 3.1 and 3.3, significantly improve multilingual understanding and generation, making them strong candidates for cross-language tasks. These advancements make Llama models highly versatile, particularly for enterprise applications where multilingual support and stable training are crucial.

Performance comparison: Mistral vs. Llama models#

After providing an overview of the architectures and various models within the Mistral and Llama families, evaluating their real-world performance is crucial. Benchmarks serve as standardized tools to assess large language models (LLMs) across diverse tasks, offering insights into their efficiency, accuracy, and scalability.

In the following comparison, we analyze how different Mistral and Llama models perform on key benchmarks, drawing from experiments by Mistral and Meta Llama teams.

Mistral 7B vs. Llama 1 and Llama 2#

Mistral 7B is a 7-billion-parameter dense model designed for high efficiency across a wide range of natural language processing (NLP) tasks. While relatively smaller than many other models, Mistral 7B maintains competitive performance and is optimized for environments with limited computational resources.

Compared to Llama models, Mistral 7B outperforms several benchmarks, offering high-quality results in a smaller, more efficient package. It outperforms Llama 2’s 7B models in all benchmarks and Llama 2’s 13B model in tasks like massive multitask language understanding, reasoning, comprehension, math, and code. Llama 1 33B model is much bigger than Mistral but is more accurate than Mistral in a few benchmarks, including knowledge, reasoning, and the Big-Bench Hard (BBH) benchmark.

• MMLU (Massive Multitask Language Understanding): This benchmark tests general knowledge and understanding across numerous domains, including science, mathematics, humanities, and social sciences. It assesses the model’s ability to retrieve and synthesize knowledge effectively.

• Reasoning: This measures logical thinking and the ability to solve problems that require inference or connecting multiple pieces of information. A model’s performance here reflects its “thinking” ability beyond knowledge retrieval.

• Comprehension: Comprehension evaluates how well a model understands and processes text, often judged by its ability to produce coherent, contextually appropriate responses to given inputs.

• AGI eval: Aimed at testing tasks related to general artificial intelligence, this metric examines the model’s performance on complex, multi-faceted problems beyond domain-specific tasks.

• Math and code: These benchmarks specifically evaluate technical skills. The math benchmark tests arithmetic and problem-solving abilities, while the code benchmark measures the model’s ability to generate or debug programming scripts.

• BBH (Big-Bench Hard): This subset of the benchmark focuses on particularly challenging tasks that test nuanced reasoning, creativity, and problem-solving under uncertainty.

Mixtral 8x7B vs. Llama 2#

Mixtral 8x7B is an MoE model that leverages eight 7-billion-parameter models, with each expert activated based on the task. This architecture enables the model to perform multiple tasks simultaneously, enhancing its flexibility and adaptability.

Mixtral outperforms Llama 2 70B on benchmarks such as MMLU, comprehension, knowledge, math, reasoning, and code while delivering inference speeds up to six times faster. The figure below highlights the trade-off between quality and inference budget.

Mixtral 8x22B vs. Llama 1 and Llama 2#

Mixtral 8x22B extends the MoE approach by combining eight 22-billion-parameter models, offering a much larger scale and enhanced performance for more complex NLP tasks. This model shines in handling large-scale text generation and in-depth contextual analysis. Compared to Llama models, Mixtral 8x22B is designed for high-throughput applications where processing power and scale are essential, making it suitable for demanding tasks such as large-scale content generation and complex reasoning.

The image below shows the performance of Mistral and Llama models on the MMLU (Massive Multitask Language Understanding) benchmark, evaluated relative to the inference budget and measured by the number of active parameters or costs. The shaded orange area identifies models with the best performance-to-cost ratio. The Mixtral 8x7B and 8x22B models are prominently in this area, emphasizing their computational efficiency.

• Mistral 7B performs better (around 60%) than Llama 2 7B, with fewer active parameters per cost.

• Mixtral 8x7B achieves an even better balance of performance (~70%) and cost efficiency.

• Mixtral 8x22B delivers the best performance (~80%) in the highlighted “best performance/cost ratio” zone.

• Llama 2 7B, 13B, 33B, and 70B show a trend of increasing performance with more active parameters, but they fall short of the performance/cost efficiency demonstrated by the Mistral and Mixtral models.

While Llama 2 70B achieves high performance (~75%), it requires significantly more active parameters, making it less cost-efficient than the Mistral and Mixtral models.

Codestral vs. CodeLlama and Llama 3 70B#

Codestral 22B is a 22-billion-parameter model designed for code generation and understanding. It is highly optimized for writing code in multiple programming languages, debugging, and solving algorithmic problems. Compared with Llama models, Codestral 22B’s specialization in programming tasks allows it to outperform general-purpose models in technical domains. This makes it an excellent choice for developers needing automated coding, debugging, and software design assistance.

The bar chart below compares the performance of Codestral 22B, CodeLlama 70B, and Llama 3 70B models across Python programming tasks and SQL tasks, using benchmarks like MBPP, CruxEval-O, RepoBench, and Spider.

• MBPP (The Massive Benchmark for Program Synthesis): MBPP is a benchmark used to evaluate models’ ability to generate correct and efficient code snippets for various programming tasks. It consists of problems typically involving algorithmic thinking and logical reasoning. MBPP tests models on their capability to synthesize code that meets specific input-output requirements, making it a useful tool for evaluating a model’s code generation abilities across a broad spectrum of problem types.

• CruxEval-O (Crux Evaluation for Open-Ended Tasks): CruxEval-O is designed to evaluate models on open-ended problem-solving tasks, particularly focusing on tasks requiring more than standard code completion or generation. It assesses a model’s ability to understand, reason, and generate solutions for open-ended coding tasks that might not have a single correct solution. This benchmark emphasizes creativity and problem-solving flexibility.

• RepoBench: RepoBench evaluates models’ performance in generating code by analyzing entire repositories rather than isolated snippets. It focuses on assessing how well models can generate, refactor, or understand code in the context of large codebases. This makes it particularly useful for testing models intended to assist in real-world software development tasks, such as codebase navigation, code understanding, and repository-wide changes.

• Spider: Spider is a benchmark for evaluating models’ ability to translate natural language questions into SQL queries. It contains a variety of SQL-related tasks that require the model to understand the intent behind a natural language question and generate the corresponding SQL code. Spider is widely used to assess a model’s ability to work with structured data and databases, making it an essential benchmark for models focused on SQL generation and query-based tasks.

Codestral 22B excels in Python across benchmarks such as MBPP, CruxEval-O, and RepoBench, demonstrating its strong code generation and problem-solving performance. On the other hand, Llama 3 70B outperforms Codestral 22B and CodeLlama 70B in SQL tasks, particularly on the Spider benchmark. CodeLlama 70B tends to underperform in certain benchmarks compared to Codestral 22B and Llama 3 70B, highlighting its relative limitations in specific coding tasks.

In addition to Python and SQL tasks, Codestral’s performance is evaluated across various programming languages using the HumanEval benchmark, as illustrated in the chart below.

The HumanEval benchmark is a widely used evaluation framework designed to assess the code generation capabilities of language models. It consists of programming tasks, where the model is asked to generate code that solves a specific problem. Each task is a coding challenge, and the model must produce a function that passes a set of test cases.

The benchmark focuses on evaluating how well a model can understand and generate correct code, requiring it to handle various aspects such as:

• Syntax: Correct use of programming language syntax.

• Logic: The ability to write functional code that solves the problem correctly.

• Problem-solving: Ability to reason through programming challenges, ensuring the solution is efficient and accurate.

Codestral 22B outperforms the CodeLlama 70B model across all programming languages, and it surpasses the general-purpose, most efficient Llama model, Llama 3 70B, in languages such as Python, Bash, Java, and PHP. However, in languages like C++, TypeScript, and C#, Llama 3 70B outperforms Codestral 22B. On average, both Codestral 22B and Llama 3 70B models achieve similar performance on the HumanEval benchmark. Despite this, Codestral 22B could be considered more efficient due to its smaller parameter size, delivering comparable performance with fewer resources than Llama 3 70B.

While CodeLlama 70B may underperform in certain benchmarks, it offers notable strengths that make it a competitive choice for developers. It supports multilingual code across languages like Python, JavaScript, TypeScript, and Rust, providing flexibility for diverse programming needs. Its scalability—with model sizes of 7B, 13B, and 70B—allows users to choose the best fit for their computational resources. Its extended context length further enhances its ability to work with large codebases, making it particularly valuable for enterprise applications and long-range code dependencies.

Mistral Large 2 vs. Llama 3.1 405B#

Mistral Large 2 is an enhanced version of the original Mistral model, designed to handle more complex NLP tasks with higher accuracy and improved efficiency. It is significantly more capable in code generation, mathematics, and reasoning, providing advanced support. It also offers much stronger multilingual support, enabling more accurate performance across multiple languages. The model also integrates advanced function calling capabilities, allowing it to manage more sophisticated tasks.

When compared to Llama models, Mistral Large 2 matches the performance of the top Llama model, Llama 3 405B, in code generation and math.

Llama 3.1 405B is slightly better than Mistral Large 2 regarding language diversity. However, considering the substantial difference in parameter count—Mistral Large 2 has 123 billion parameters, nearly one-third of the Llama 3.1 405B model—Mistral Large 2 remains highly efficient, delivering impressive performance, as shown in the chart below.

Mathstral 7B vs. Llama 3 8B#

Mathstral 7B is a specialized model for mathematical reasoning, solving equations, and handling formula parsing. This model is adept at tasks requiring logical inference and mathematical expression understanding. Unlike Llama models, which are focused on general-purpose NLP tasks, Mathstral 7B is fine-tuned for academic, scientific, and engineering applications that demand precise mathematical problem-solving capabilities. Its performance in mathematical contexts sets it apart from models optimized for broader language tasks.

The chart below compares the performance of Mathstral 7B and Llama 3’s 8B model on mathematical reasoning tasks across various benchmarks. Mathstral outperforms Llama 3’s 8B model on all benchmarks.

• Math: This is a general category that likely refers to a collection of standard mathematical tasks that test the model’s ability to solve problems involving arithmetic, algebra, geometry, and other mathematical concepts. It serves as a basic evaluation of a model’s mathematical reasoning skills.

• GSM8K (8-shot): GSM8K (Grade School Math 8K) is a dataset containing math word problems. The “8-shot” aspect refers to few-shot learning, where the model is given 8 examples of how to solve problems before being asked to solve new problems. This benchmark tests a model’s ability to learn from a few examples and generalize to similar math word problems.

• MathOdyssey (maj@16): MathOdyssey is a benchmark focused on solving complex mathematical reasoning problems. The “maj@16” notation suggests the metric used is based on majority voting over 16 problems, where the model’s ability to provide correct solutions is evaluated. This helps assess the model’s accuracy and robustness in handling difficult math tasks.

• GRE Math (maj@16): The GRE (Graduate Record Examinations) Math benchmark assesses the model’s performance on mathematics questions similar to those in the GRE, typically involving algebra, geometry, and data interpretation. The “maj@16” again refers to majority voting over 16 problems, ensuring the model’s accuracy is evaluated across a set of problems.

• AMC 2023 (maj@16): The AMC (American Mathematics Competitions) 2023 benchmark involves math problems similar to those in the AMC contests. These problems typically test high school-level mathematics, including problem-solving and reasoning skills. The “maj@16” indicates that the evaluation is based on the model’s performance across 16 problems, emphasizing its accuracy and consistency.

• AIME 2024 (maj@16): The AIME (American Invitational Mathematics Examination) 2024 benchmark is a more advanced level of mathematics competition compared to the AMC. AIME problems involve deep problem-solving and mathematical reasoning. Like other benchmarks, “maj@16” means the model’s performance is evaluated based on a majority vote across 16 problems, highlighting its ability to solve complex, competition-level math problems.

Codestral Mamba 7B vs. CodeLlama 7B and 34B#

Codestral Mamba 7B is focused on advanced code generation and algorithmic problem-solving. This model can generate code, debug, and solve complex programming challenges, making it a valuable tool for software development. Compared with Llama models, Codestral Mamba 7B excels in technical accuracy and problem-solving capabilities, providing developers with an efficient model for automating coding tasks and improving productivity in programming environments.

On average, Codestral Mamba 7B outperforms the CodeLlama 7B and CodeLlama 34B models on the HumanEval benchmark. However, on the MBPP benchmark, the larger CodeLlama 34B model, with five times the number of parameters, outperforms Codestral Mamba 7B.

Pixtral Large vs. Llama 3.2 90B#

Pixtral Large 124B and Llama 3.2 90B are advanced AI models for multimodal tasks, including text reasoning, document understanding, and visual question answering. Pixtral Large 124B is a larger model designed for superior reasoning, document processing, and structured problem-solving, with strong performance in math and text-based tasks. Llama 3.2 90B is a powerful but slightly smaller model focused on general-purpose multimodal understanding, performing well in vision tasks, and maintaining competitive accuracy in reasoning tasks.

These models are evaluated across multiple benchmarks to measure their effectiveness in handling complex reasoning and vision-based tasks, as shown in the chart below.

• Mathvista (CoT) (mathematical reasoning): This benchmark assesses a model’s ability to solve mathematical problems using Chain of Thought (CoT) reasoning, a step-by-step logical approach to problem-solving.

• MMMU (CoT) (multimodal math understanding): It evaluates multimodal math understanding, testing how well the model can interpret and solve math problems involving text and images. It is particularly relevant for real-world scenarios where mathematical problems are presented alongside visual representations, such as diagrams or graphs.

• ChartQA (CoT) (chart-based question answering): It focuses on a model’s ability to answer questions based on chart data, including bar charts, pie charts, and line graphs. This benchmark is important for data analysis and interpretation, particularly in business and scientific applications.

• DocVQA (ANLS) (document visual question answering): This benchmark evaluates how well the models can extract and interpret textual information from documents. The metric used is ANLS (Average Normalized Levenshtein Similarity), which measures how closely a model’s generated responses match the correct answers.

• VQAv2 (VQA Match) (visual question answering): This benchmark tests models’ ability to answer questions based on images. It includes object recognition, scene understanding, and reasoning over visual content.

• AI2D (BBox) (bounding box detection in diagrams): This benchmark evaluates a model’s ability to understand and interact with diagrams using bounding boxes (BBox). This is particularly relevant for spatial reasoning and object localization in structured diagrams.

Pixtral Large 124B outperforms Llama 3.2 90B in structured reasoning, document intelligence, and math-heavy tasks. Both models perform equally well in vision-based tasks like image question answering and bounding box detection. If the use case involves structured data, math, or document processing, Pixtral is a better choice. If the focus is general vision tasks, both models are comparable.

Conclusion#

When choosing between Mistral and LLaMA, align your selection with your project’s key requirements:

• Choose Mistral if:

  • You need high efficiency and performance per dollar.

  • You operate with limited resources (e.g., edge devices, startups).

  • Your use case involves fast inference, code generation, or mathematical reasoning.

• Choose Llama if:

  • You need strong multilingual and general-purpose NLP capabilities.

  • You’re developing enterprise-scale applications with custom fine-tuning.

  • You have the compute resources to leverage large, dense models effectively.

Mistral’s optimized architecture makes it ideal for cost-sensitive, task-specific deployments, while LLaMA excels in large-scale, multilingual, and fine-tuned applications. Understanding these strengths helps you maximize performance, scalability, and efficiency for your needs.