Side-by-side comparison of top AI models: context window, input/output pricing, strengths, and best use cases. GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro, Llama 3, Mistral, and more. Updated 2025.
Free, no sign-up — one of the SaaSQuickStart AI / LLM tools.
Both are excellent general-purpose models. GPT-4o has a slight edge on multimodal tasks (vision, audio) and is deeply integrated into Microsoft's ecosystem. Claude 3.5 Sonnet consistently scores higher on coding benchmarks (SWE-bench) and tends to follow nuanced instructions more reliably. If you're building a coding assistant, lean toward Claude. For a customer-facing chatbot with image analysis, GPT-4o is a strong default.
The context window is the maximum number of tokens — text chunks roughly equivalent to 3–4 characters — that the model can read and generate in a single API call. A 128K context window can hold roughly 100,000 words (about a 400-page novel), while Gemini 1.5 Pro's 1M context can hold entire codebases or hours of transcripts. Longer context means you can include more background information and conversation history without truncating.
Llama 3.1 is free to download and self-host under Meta's open license. If you run it on your own GPU infrastructure, your only cost is the compute. However, hosted inference through providers like Groq, Together AI, or AWS Bedrock does have a per-token fee (typically $0.50–$0.90/1M tokens for the 70B model). The "Free*" label in this table refers to the open-source download — not hosted inference.
For RAG, context window size matters more than raw quality — you're stuffing retrieved chunks into the prompt, and more room means more retrieved context. Gemini 1.5 Flash is the best budget RAG model with its 1M token window at $0.075/1M input tokens. Claude 3 Haiku is a strong alternative at 200K context for $0.25/1M. If quality matters more than cost, Gemini 1.5 Pro or Claude 3.5 Sonnet handles large-context reasoning better.