AI Model Comparison Matrix

GPT-4o excels at multimodal tasks (vision, audio) and has a massive ecosystem with plugins. Claude 3.5 Sonnet generally outperforms on coding, long-context tasks (200K context), and instruction following. For pure text generation at lower cost, Claude Haiku or GPT-4o-mini are strong choices.

The context window is how many tokens the model can 'see' at once — input + output combined. A 128K context = roughly 90,000-100,000 words. Larger context lets you process entire codebases, long documents, or extended conversations without truncation. Models with 1M+ contexts (Gemini 1.5 Pro) can process entire books.

Two key metrics: Time To First Token (TTFT) — how long before the response starts streaming; and Tokens Per Second (TPS) — throughput once streaming begins. For real-time chat, low TTFT matters most. For batch processing, TPS is more important. API providers show significant latency variance by region and load.

Proprietary models (GPT-4, Claude, Gemini) are accessed via API, managed by vendors, and typically lead on benchmarks. Open-source models (Llama 3, Mistral, Qwen) can be self-hosted for privacy, customized via fine-tuning, and have no per-token costs beyond compute. Self-hosting Llama 3 70B requires a high-end GPU server.

For coding: Claude 3.5 Sonnet and GPT-4o are generally top-tier. For cost-sensitive coding: GPT-4o-mini or Claude Haiku. For autonomous coding agents: models with large context windows and strong tool-use (GPT-4o, Claude 3.5 Sonnet) are preferred. Codestral from Mistral is specialized for code generation.