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Mistral large vs. gpt and claude d

Based on the search results, here's a comprehensive comparison of Mistral Large 2, GPT-4, and Claude 3.5:

Context Window Size

  • Mistral Large 2: 128K tokens[1][6]
  • GPT-4: 8,192 tokens[12]
  • Claude 3.5: 200K tokens[12]

Model Parameters & Architecture

  • Mistral Large 2: 123 billion parameters[1]
  • GPT-4: Approximately 1 trillion parameters[2]
  • Claude 3.5: Over 175 billion parameters[3]

Language Capabilities

  • Mistral Large 2: Native fluency in English, French, Spanish, German, Italian, Portuguese, Arabic, Hindi, Russian, Chinese, Japanese, Korean, and 80+ coding languages[4][6]
  • GPT-4: Strong multilingual capabilities with no specific language count published
  • Claude 3.5: Comprehensive multilingual support with strong performance across major languages[3]

Performance & Speed

  • Mistral Large 2: Designed for single-node inference with long-context applications[6]
  • GPT-4: Industry-leading performance but slower response times[2]
  • Claude 3.5: Operates at twice the speed of its predecessor, Claude 3 Opus[11]

Coding Capabilities

  • Mistral Large 2: Excels in code generation and supports 80+ programming languages[1]
  • GPT-4: Strong coding capabilities with high accuracy
  • Claude 3.5: Solved 64% of coding problems in internal evaluations[11]

Pricing

  • Mistral Large 2: Pricing not specified in search results
  • GPT-4: $30 per million input tokens, $60 per million output tokens[12]
  • Claude 3.5: $3 per million input tokens, $15 per million output tokens[12]

Visual Processing

  • Mistral Large 2: Includes visual processing capabilities[1]
  • GPT-4: Advanced multimodal capabilities through GPT-4V
  • Claude 3.5: Strong visual processing capabilities, particularly for charts, graphs, and text extraction from images[11]

Unique Strengths

  • Mistral Large 2: Excels in function calling and retrieval skills[1]
  • GPT-4: Superior performance in academic tests and professional exams[7]
  • Claude 3.5: Advanced reasoning capabilities and natural, relatable tone in responses[11]

The above text was generated by a large language model (LLM) and its accuracy has not been validated. This page is part of 'LLMs-on-LLMs,' a Github repository by Daniel Rosehill which explores how curious humans can use LLMs to better their understanding of LLMs and AI. However, the information should not be regarded as authoritative and given the fast pace of evolution in LLM technology will eventually become deprecated. This footer was added at 16-Nov-2024.