AI Model Comparison Tool

Claude (Anthropic) and GPT-4 (OpenAI) are both frontier LLMs but differ in key areas. Claude has a larger context window (200K vs 128K tokens), tends to be more cautious with harmful content, and excels at nuanced writing and document analysis. GPT-4 has a broader plugin/tool ecosystem via ChatGPT and slightly higher MMLU scores. Claude 3.5 Sonnet is generally preferred for coding agents; GPT-4o for multimodal tasks.

Choose Gemini when you need: (1) Massive context windows — Gemini 1.5 Pro supports 1M tokens (entire codebases, long PDFs). (2) Google Workspace integration — Gemini integrates natively with Docs, Gmail, Drive. (3) Cost-efficiency at scale — Gemini Flash offers very low pricing. (4) Native video/audio support — Gemini handles multimodal inputs including video frames.

Reasoning models (OpenAI o1, o1-mini, o3) perform chain-of-thought reasoning before answering, spending more compute on 'thinking'. Use them for: complex math proofs, multi-step logic puzzles, advanced coding problems, scientific reasoning. They're slower and more expensive than standard models but significantly better on tasks requiring systematic step-by-step thinking.

Open-source LLMs (Llama 3, Mistral, Qwen) are worth considering for: (1) Privacy — data stays on your servers. (2) Cost at scale — run once, pay server costs only. (3) Fine-tuning — full control to train on private data. (4) No vendor lock-in. Downsides: higher engineering overhead, generally lower capability than frontier models, no built-in safety layers. Llama 3.3 70B is competitive with GPT-3.5-level capability.

For budget optimization: (1) Under $1/1M tokens: GPT-4o Mini, Claude Haiku, Gemini Flash, Llama 3.1 8B. (2) $1–10/1M: Llama 3.3 70B (via API), Mistral Large, GPT-4o (input only), Claude Sonnet (input). (3) $10+/1M: GPT-4o (output), Claude Opus, o1 — justify only for complex tasks. Tip: use cheap models for routing/classification and expensive models only for the final generation step.