Large language models (LLMs) are transforming our interaction with computers, enabling AI-generated text, language translation, creative content, and informative Q&A. However, with a variety of LLMs available, deciding on the best one can be challenging. Today, we’ll compare two top open-source models: Llama 2 vs Mistral.
What is Llama 2?
Llama 2 is a family of large language models (LLMs) developed and released by Meta AI in July 2023. It represents the second generation of the Llama (Large Language Model Meta AI) series and offers significant improvements in performance, capability, and accessibility compared to its predecessor.
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What is Mistral AI?
Mistral AI is a French company focusing on developing and providing cutting-edge, open-source large language models (LLMs) [https://mistral.ai/]. Founded in April 2023 by former employees of Meta and Google DeepMind, Mistral AI has quickly gained recognition for its innovative approach to LLMs.
Llama 2 vs Mistral Performances
Here’s a breakdown of the performance of Llama 2 and Mistral:
Llama 2 Performance
Generally considered a strong all-rounder, performing well across various tasks.
Strengths:
- Good performance in common language tasks like question answering, summarization, and text generation.
- Often praised for its fluency and coherence in generated text.
Weaknesses:
- May not be the absolute best in highly specialized tasks like code generation compared to some focused models.
Mistral AI Performance
Known for its impressive performance on various benchmarks, particularly in reasoning and comprehension tasks.
Strengths:
- Often outperforms Llama 2 on benchmarks for reasoning, world knowledge, reading comprehension, and code (especially for its size).
- Offers efficient memory usage, achieving high performance with a smaller model size compared to Llama 2.
Weaknesses:
- Information about Mistral’s performance in general language tasks like summarization and creative text generation might be limited due to its relative newness.
| Feature | Llama 2 | Mistral |
|---|---|---|
| Overall Performance | Strong All-rounder | Excellent in reasoning and comprehension |
| Strengths | Common language tasks, fluency, coherence | Reasoning, comprehension, code (for size), memory efficiency |
| Weaknesses | Specialized tasks | Limited info on general language tasks |
Additional factors to consider:
- Task-specific needs: If your primary focus is reasoning and comprehension, Mistral might be a strong contender. For general language tasks, Llama 2 might be a good choice.
- Model size and efficiency: Mistral offers good performance with a smaller footprint, potentially reducing computational costs.
Related post-Llama-2 vs GPT-4
Llama 2 vs Mistral Parameters
When comparing large language models like Llama 2 and Mistral, one key difference is the number of parameters each model contains. Parameters are the elements within a model that determine its behavior and output quality. Let’s examine the parameters for Llama 2 and Mistral to see how they differ.
Llama 2 Parameters
Llama 2, developed by Meta AI, comes in several versions with varying parameter sizes. This allows developers to choose the model that best suits their needs, from smaller, lightweight models to large, highly capable ones. The key parameter sizes for Llama 2 are:
- Llama 2-7B: Contains 7 billion parameters.
- Llama 2-13B: Contains 13 billion parameters.
- Llama 2-70B: Contains 70 billion parameters.
These different versions offer varying levels of performance, with the larger models providing more complex capabilities and requiring more computational resources. The smaller models, while less capable in terms of performance, are faster and easier to deploy in resource-constrained environments.
Mistral Parameters
Mistral, developed by Mistral AI, is designed to be a highly efficient large language model. It aims to offer high performance with a more compact and streamlined architecture. Mistral’s key parameter size is:
- Mistral-7B: Contains 7 billion parameters.
Despite having fewer parameters compared to some of Llama 2’s larger versions, Mistral is known for its efficiency and strong performance in various natural language processing (NLP) tasks. Its compact design makes it an attractive option for applications that require efficient models with lower resource demands.
Llama 2 vs Mistral pricing
Considering using cutting-edge language models like Llama 2 and Mistral? Understanding their pricing structures is crucial. Here’s a quick guide:
- Pay-per-Token: Both models typically charge based on the amount of text processed (tokens).
- Variable Costs: Pricing hinges on two factors:
- Model Size: Larger models with more parameters generally cost more per token.
- Provider: Different cloud providers or AI service companies might have varying pricing schemes.
Llama 2 Pricings
- Costs vary depending on the model size and provider.
- Pricing is typically per token, meaning you pay based on the amount of text processed.
- Per 1 million tokens: This ranges from $0.10 to $1.20 per million tokens, depending on the model size. Larger models (above 8.1B parameters) tend to be more expensive.
mistral pricing
Open Models
- open-mistral-7bA: $0.25 per 1 million tokens (inference and embedding)
- open-mixtral-8x7bA: $0.70 per 1 million tokens (inference and embedding)
- Inference: $2.00 – $6.00 per 1 million tokens (depending on usage)
- Embedding: Not specified (likely not available for this model)
Optimized Models
- mistral-small: $2.00 – $6.00 per 1 million tokens (inference only)
- Note: Pricing depends on usage and might be negotiated
- mistral-medium (deprecated): $2.70 per 1 million tokens (inference only)
- mistral-large: $8.00 – $24.00 per 1 million tokens (inference only)
- Note: Pricing depends on usage and might be negotiated
Embeddings
- mistral-embed: $0.10 per 1 million tokens (embedding only)
Proof: – https://mistral.ai/technology/
Conclusion
By understanding the capabilities of these advanced LLMs, you can make informed decisions to leverage their power in your projects.
People Ask Questions
Is Mistral 7B better than GPT 4?
- Logic: Mistral excels (reasoning, Q&A) and might be cheaper.
- Creativity: GPT-4 shines (writing, scripts) and is more accessible.
Choose based on your needs.
Why is Mistral 7B so good?
Mistral 7B excels in reasoning and comprehension tasks thanks to its unique architecture. It packs a punch in a smaller model size compared to competitors, making it potentially more cost-effective.
What is better than Mistral 7B?
Mistral 7B’s best fit depends on your task. For reasoning/comprehension, consider newer large models. For creativity, explore options like Bard. Accessibility might favor public models like GPT-3. Choose based on your needs.
Is Mistral faster than GPT?
Yes, Mistral is generally faster than GPT for similar performance due to its smaller model size and efficient architecture. This translates to quicker response times and potentially lower computational cost.
What is llama 2 used for?
Llama 2 is a versatile large language model used for various tasks like:
- Question answering (finding info from text)
- Summarization (condensing text)
- Text generation (creating different creative text formats)