LLM Model Comparison 2026

As of early 2026, Gemini 1.5 Pro leads with a 2 million token context window, followed by Gemini 1.5 Flash and Gemini 2.0 Flash at 1 million tokens. Anthropic's Claude models (3.5 Sonnet, 3.7 Sonnet, Opus, Haiku) offer 200K tokens, as do OpenAI's o1 and o3-mini. GPT-4o, GPT-4 Turbo, Llama 3.1, Mistral Large, and DeepSeek V3 top out at 128K tokens. A larger context window lets you process entire codebases, long documents, or entire books in a single prompt, but larger contexts also cost more per request.

LLM APIs charge separately for input tokens (the prompt you send) and output tokens (the text the model generates). Input prices are typically lower because the model only needs to process them; output generation is more compute-intensive. For example, GPT-4o charges $2.50/1M input tokens and $10.00/1M output tokens. If you're building a summarization app where prompts are long and outputs are short, optimize for low input price. For a chatbot generating verbose replies, output price matters more. Prices are usually quoted per million tokens (1M = roughly 750,000 words).

Function calling (also called tool use) lets the LLM output structured JSON that your code can parse and execute as a function call, then feed the result back to the model. Instead of free-form text, the model returns something like {"function": "search_web", "args": {"query": "weather in Tokyo"}}. This enables AI agents that can browse the web, query databases, send emails, or call any API. Most modern models support this: GPT-4o, Claude 3.x, Gemini 1.5+, Llama 3.1, Mistral Large, and DeepSeek V3 all have function calling support. OpenAI's o1 is a notable exception as it's designed for reasoning tasks without tools.

It depends on your use case. GPT-4o is a strong all-rounder with vision, functions, and a well-established ecosystem — good default choice. Claude 3.5/3.7 Sonnet excels at long-document analysis, coding, and following complex instructions with its 200K context. Gemini 1.5 Pro is unmatched for tasks requiring massive context (up to 2M tokens) like processing entire codebases or video transcripts. For cost-sensitive high-volume workloads, GPT-4o mini, Claude 3 Haiku, Gemini 1.5 Flash, or DeepSeek V3 offer dramatically lower prices with solid performance. Always benchmark on your specific task — model rankings shift with each new release.