AI & LLM
Token, cost & context tools for LLMs
LLM Context Window Comparison — GPT vs Claude vs Gemini (2026)
Context windows have grown dramatically in 2025–2026: the three flagship models — GPT-5.5 (1,050,000 tokens), Claude Opus 4.8 (1,000,000 tokens), and Gemini 2.5 Pro (1,048,576 tokens) — all offer roughly 1M-token contexts. For most use cases, context window size is no longer the differentiator; max output per response, pricing, and quality matter more. GPT-4o is the exception with a 128K context window, which is sufficient for the vast majority of document-processing tasks. Paste your prompt into the checker to see how it fits across each model.
GPT-5.5: 1,050,000 ctx | 128,000 max output | usable input 1,018,000
- Claude Opus 4.8: 1,000,000 ctx | 128,000 max output | usable input 968,000
- Gemini 2.5 Pro: 1,048,576 ctx | 65,536 max output | usable input 1,016,576
- GPT-4o: 128,000 ctx | 16,384 max output (sufficient for most tasks)
- Example 800,000-token prompt: fits in all 1M-context models (GPT-5.5 ✓, Claude ✓, Gemini ✓); too large for GPT-4o
- Data as of 2026-07-03
Frequently asked questions
Which LLM has the largest context window in 2026?
As of 2026-07-03, GPT-5.5 (1,050,000 tokens), Claude Opus 4.8 (1,000,000 tokens), and Gemini 2.5 Pro (1,048,576 tokens) are all roughly tied at ~1M tokens. Among these, the max output differs: GPT-5.5 and Claude Opus 4.8 support 128,000 output tokens, while Gemini 2.5 Pro is limited to 65,536 tokens per response.
Does a larger context window always mean better performance?
Not necessarily. Models with very large context windows can sometimes exhibit "lost in the middle" behavior — attention to information in the middle of very long prompts degrades relative to content at the start and end. In practice, context window size matters mainly when processing long documents or large codebases. For typical chat and reasoning tasks, model quality and output length limits (max_tokens) matter more.