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Mistral 7B vs. Mixtral 8x7B

Mistral 7B vs. Mixtral 8x7B

A French startup, Mistral AI has released two impressive large language models (LLMs) - Mistral 7B and Mixtral 8x7B. These models push the boundaries of performance and introduce a better architectural innovation aimed at optimizing inference speed and computational efficiency.

Mistral 7B: Small yet Mighty

Mistral 7B is a 7.3 billion parameter transformer model that punches above its weight class. Despite its relatively modest size, it outperforms the 13 billion parameters Llama 2 model across all benchmarks. It even surpasses the larger 34 billion parameter Llama 1 model on reasoning, mathematics, and code generation tasks.

Two foundations of Mistral 7B's efficiency:

  1. Grouped Query Attention (GQA)
  2. Sliding Window Attention (SWA)

GQA significantly accelerates inference speed and reduces memory requirements during decoding by sharing keys and values across multiple queries within each transformer layer.

SWA , on the other hand, enables the model to handle longer input sequences at a lower computational cost by introducing a configurable " attention window" that limits the number of tokens the model attends to at any given time.

Name Number of parameters Number of active parameters Min. GPU RAM for inference (GB)
Mistral-7B-v0.2 7.3B 7.3B 16
Mistral-8X7B-v0.1 46.7B 12.9B 100

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Mixtral 8x7B: A Sparse Mixture-of-Experts Marvel

While Mistral 7B impresses with its efficiency and performance, Mistral AI took things to the next level with the release of Mixtral 8x7B, a 46.7 billion parameter sparse mixture-of-experts (MoE) model. Despite its massive size, Mixtral 8x7B leverages sparse activation, resulting in only 12.9 billion active parameters per token during inference.

Mistral 7B vs. Mixtral 8x7B
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The key innovation behind Mixtral 8x7B is its MoE architecture. Within each transformer layer, the model has eight expert feed-forward networks (FFNs). For every token, a router mechanism selectively activates just two of these expert FFNs to process that token. This sparsity technique allows the model to harness a vast parameter count while controlling computational costs and latency.

According to Mistral AI's benchmarks, Mixtral 8x7B outperforms or matches the large language models like Llama 2 70B and GPT-3.5 across most multiple tasks, including reasoning, mathematics, code generation, and multilingual benchmarks. Additionally, it provides 6x faster inference than Llama 2 70B , thanks to its sparse architecture.

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Capabilities and Performance

Mistral 7B vs. Mixtral 8x7B

Both Mistral 7B and Mixtral 8x7B are good at code generation tasks like HumanEval and MBPP, with Mixtral 8x7B having a slight edge and it's better. Mixtral 8x7B also supports multiple languages , including English, French, German, Italian, and Spanish, making them valuable assets for multilingual applications.

On the MMLU benchmark, which evaluates a model's reasoning and comprehension abilities, Mistral 7B performs equivalently to a hypothetical Llama 2 model over three times its size.

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LLMs Benchmark Comparison Table

Model Average MCQs Reasoning Python coding Future Capabilities Grade school math Math Problems
Claude 3 Opus 84.83% 86.80% 95.40% 84.90% 86.80% 95.00%
Gemini 1.5 Pro 80.08% 81.90% 92.50% 71.90% 84% 91.70%
Gemini Ultra 79.52% 83.70% 87.80% 74.40% 83.60% 94.40%
GPT-4 79.45% 86.40% 95.30% 67% 83.10% 92%
Claude 3 Sonnet 76.55% 79.00% 89.00% 73.00% 82.90% 92.30%
Claude 3 Haiku 73.08% 75.20% 85.90% 75.90% 73.70% 88.90%
Gemini Pro 68.28% 71.80% 84.70% 67.70% 75% 77.90%
Palm 2-L 65.82% 78.40% 86.80% 37.60% 77.70% 80%
GPT-3.5 65.46% 70% 85.50% 48.10% 66.60% 57.10%
Mixtral 8x7B 59.79% 70.60% 84.40% 40.20% 60.76% 74.40%
Llama 2 - 70B 51.55% 69.90% 87% 30.50% 51.20% 56.80%
Gemma 7B 50.60% 64.30% 81.2% 32.3% 55.10% 46.40%
Falcon 180B 42.62% 70.60% 87.50% 35.40% 37.10% 19.60%
Llama 13B 37.63% 54.80% 80.7% 18.3% 39.40% 28.70%
Llama 7B 30.84% 45.30% 77.22% 12.8% 32.6% 14.6%
Grok 1 - 73.00% - 63% - 62.90%
Qwen 14B - 66.30% - 32% 53.40% 61.30%
Mistral Large - 81.2% 89.2% 45.1% - 81%

This model comparison table was last updated in March 2024. Source

When it comes to fine-tuning for specific use cases, Mistral AI provides " Instruct" versions of both models, which have been optimized through supervised fine-tuning and direct preference optimization (DPO) for careful instruction following.


The Mixtral 8x7B Instruct model achieves an impressive score of 8.3 on the MT-Bench benchmark, making it one of the best open-source models for instruction.

Deployment and Accessibility

Mistral AI has made both Mistral 7B and Mixtral 8x7B available under the permissive Apache 2.0 license, allowing developers and researchers to use these models without restrictions. The weights for these models can be downloaded from Mistral AI's CDN, and the company provides detailed instructions for running the models locally, on cloud platforms like AWS, GCP, and Azure, or through services like HuggingFace.

LLMs Cost and Context Window Comparison Table

Models Context Window Input Cost / 1M tokens Output Cost / 1M tokens
Gemini 1.5 Pro 128K N/A N/A
Mistral Medium 32K $2.7 $8.1
Claude 3 Opus 200K $15.00 $75.00
GPT-4 8K $30.00 $60.00
Mistral Small 16K $2.00 $6.00
GPT-4 Turbo 128K $10.00 $30.00
Claude 2.1 200K $8.00 $24.00
Claude 2 100K $8.00 $24.00
Mistral Large 32K $8.00 $24.00
Claude Instant 100K $0.80 $2.40
GPT-3.5 Turbo Instruct 4K $1.50 $2.00
Claude 3 Sonnet 200K $3.00 $15.00
GPT-4-32k 32K $60.00 $120.00
GPT-3.5 Turbo 16K $0.50 $1.50
Claude 3 Haiku 200K $0.25 $1.25
Gemini Pro 32K $0.125 $0.375
Grok 1 64K N/A N/A

This cost and context window comparison table was last updated in March 2024. Source


Largest context window: Claude 3 (200K), GPT-4 Turbo (128K), Gemini Pro 1.5 (128K)


Lowest input cost per 1M tokens: Gemini Pro ($0.125), Mistral Tiny ($0.15), GPT 3.5 Turbo ($0.5)

For those looking for a fully managed solution, Mistral AI offers access to these models through their platform, including a beta endpoint powered by Mixtral 8x7B.

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Mistral AI's language models, Mistral 7B and Mixtral 8x7B, are truly innovative in terms of architectures, exceptional performance, and computational efficiency, these models are built to drive a wide range of applications, from code generation and multilingual tasks to reasoning and instruction.

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