What are the key differences between GPT-4, Claude, Gemini, and open-source LLMs?

Major LLMs have distinct strengths suited to different applications. GPT-4/GPT-4o (OpenAI): strong general reasoning, excellent instruction following, robust function calling, large context windows (128K), and multimodal capabilities (text, image, audio). Best for: complex reasoning tasks, code generation, and structured output. Claude 3.5 Sonnet/Opus (Anthropic): exceptional at nuanced writing, careful analysis, long context processing (200K tokens), strong at following complex instructions, and designed with safety emphasis through Constitutional AI. Best for: document analysis, writing, careful reasoning, and tasks requiring thoughtful responses. Gemini 1.5 Pro (Google): massive context window (up to 2M tokens), strong multimodal capabilities including video understanding, good integration with Google ecosystem, and competitive performance on benchmarks. Best for: very long document processing, video analysis, and Google Workspace integration. Open-source LLMs: Llama 3.1 (Meta, 8B/70B/405B, excellent quality-to-size ratio, permissive license), Mistral/Mixtral (efficient architecture, strong European language support), Qwen 2.5 (Alibaba, strong multilingual, competitive quality), and DeepSeek V3 (cost-efficient, strong reasoning). Best for: privacy-sensitive applications, custom fine-tuning, on-premises deployment, and cost control. Selection criteria: evaluate on your specific use case (benchmark on representative tasks), consider latency requirements (smaller models are faster), evaluate cost (open-source eliminates per-token costs), assess privacy requirements (open-source enables local deployment), and consider ecosystem integration needs.