AI Model Comparison: End of 2024 Edition

model_landscape_2024:
  anthropic:
    flagship: "Claude 3.5 Sonnet"
    strengths: ["Coding", "Analysis", "Safety", "Long context"]
    context: 200K tokens

  openai:
    flagship: "GPT-4o"
    strengths: ["Multimodal", "Speed", "Ecosystem"]
    context: 128K tokens

  google:
    flagship: "Gemini 1.5 Pro"
    strengths: ["Long context", "Multimodal", "Search integration"]
    context: 1M tokens (preview)

  meta:
    flagship: "Llama 3.1 405B"
    strengths: ["Open weights", "Self-hosting", "Customization"]
    context: 128K tokens

task_performance:
  coding:
    best: "Claude 3.5 Sonnet"
    runner_up: "GPT-4o"
    notes: "Claude excels at complex refactoring and debugging"

  creative_writing:
    best: "GPT-4o / Claude 3.5 (tie)"
    notes: "Style differences—test for your use case"

  analysis_reasoning:
    best: "Claude 3.5 Sonnet"
    runner_up: "GPT-4o"
    notes: "Claude better on nuanced analysis"

  multimodal:
    best: "GPT-4o"
    runner_up: "Gemini 1.5 Pro"
    notes: "GPT-4o has native audio, best image understanding"

  long_context:
    best: "Gemini 1.5 Pro"
    runner_up: "Claude 3.5 Sonnet"
    notes: "Gemini handles 1M tokens, but quality varies"

  speed:
    best: "GPT-4o / Gemini Flash"
    notes: "Both optimized for low latency"

benchmark_comparison:
  note: "Benchmarks don't always reflect production performance"

  humaneval_coding:
    claude_35_sonnet: "92%"
    gpt4o: "91%"
    gemini_15_pro: "84%"

  mmlu_knowledge:
    claude_35_sonnet: "88.7%"
    gpt4o: "88.7%"
    gemini_15_pro: "85.9%"

  graduate_reasoning:
    claude_35_sonnet: "65%"
    gpt4o: "53%"
    gemini_15_pro: "59%"

pricing_comparison:
  per_million_tokens:
    claude_35_sonnet:
      input: "$3"
      output: "$15"

    gpt4o:
      input: "$5"
      output: "$15"

    gemini_15_pro:
      input: "$3.50"
      output: "$10.50"

    gpt4o_mini:
      input: "$0.15"
      output: "$0.60"

    claude_3_haiku:
      input: "$0.25"
      output: "$1.25"

  cost_efficiency:
    best_value_premium: "Claude 3.5 Sonnet"
    best_value_budget: "GPT-4o mini"

model_recommendations:
  chatbot_general:
    primary: "GPT-4o mini"
    fallback: "Claude 3 Haiku"
    reasoning: "Cost-effective, good enough quality"

  coding_assistant:
    primary: "Claude 3.5 Sonnet"
    fallback: "GPT-4o"
    reasoning: "Best code understanding and generation"

  document_analysis:
    primary: "Claude 3.5 Sonnet"
    alternative: "Gemini 1.5 Pro (for very long docs)"
    reasoning: "Strong analysis, good context handling"

  customer_support:
    primary: "GPT-4o mini or Claude Haiku"
    escalation: "Claude 3.5 Sonnet"
    reasoning: "Fast, cheap, route complex to better model"

  content_generation:
    primary: "Claude 3.5 Sonnet or GPT-4o"
    reasoning: "Both excellent, test for your style"

  data_extraction:
    primary: "GPT-4o mini"
    reasoning: "Structured output, good JSON mode"

  real_time_voice:
    primary: "GPT-4o"
    reasoning: "Native audio capabilities"

def select_model(requirements: Requirements) -> str:
    # Budget constrained
    if requirements.cost_sensitive:
        if requirements.quality_threshold == "basic":
            return "gpt-4o-mini"
        elif requirements.quality_threshold == "good":
            return "claude-3-haiku"

    # Coding tasks
    if requirements.task_type == "coding":
        return "claude-3-5-sonnet"

    # Multimodal with audio
    if requirements.needs_audio:
        return "gpt-4o"

    # Very long documents
    if requirements.context_length > 200000:
        return "gemini-1.5-pro"

    # General high quality
    if requirements.quality_threshold == "best":
        return "claude-3-5-sonnet"

    # Default balanced choice
    return "gpt-4o"

open_models:
  llama_3_1:
    sizes: [8B, 70B, 405B]
    use_when:
      - Data privacy requirements
      - High volume cost optimization
      - Need to fine-tune
      - Offline/edge deployment

  mistral:
    sizes: [7B, "8x7B MoE"]
    use_when:
      - European data residency
      - Efficient inference
      - Good quality/size ratio

  considerations:
    pros:
      - No API costs at scale
      - Full control
      - Can fine-tune
      - Data stays local

    cons:
      - Infrastructure overhead
      - Lower quality than top APIs
      - ML ops expertise needed
      - Slower iteration

multi_model_architecture:
  router_pattern:
    classifier: "Small model classifies query"
    simple_queries: "GPT-4o mini / Haiku"
    complex_queries: "Claude 3.5 Sonnet"
    savings: "60-80% vs always using best model"

  fallback_pattern:
    primary: "Claude 3.5 Sonnet"
    fallback_1: "GPT-4o (if Claude unavailable)"
    fallback_2: "Self-hosted Llama (last resort)"
    benefit: "High availability"

  specialist_pattern:
    coding: "Claude 3.5 Sonnet"
    images: "GPT-4o"
    long_docs: "Gemini 1.5 Pro"
    benefit: "Best tool for each job"