LLM Context Windows and Capabilities in 2026

Every column above looks like a feature checklist. In practice, only two or three of them will matter for any one product. Here's the short version of which to care about.

• Context window caps how much input plus output can fit in one call. Skip the marketing number and ask if you'll actually pack 200K tokens into one prompt. Most production flows use under 8K.
• Max output is the hard cap on a single response, even when context is 1M. If you're generating long form (reports, code files, transcripts) this is the column that bites.
• Vision and audio are still uneven. Vision is broad now. Audio input is rare outside Gemini and GPT-4o. If you need either, this column does the gating.
• Tools and JSON are table stakes for agent loops and structured extraction. A "no" here means you'll be parsing markdown by hand or wrapping a fragile prompt.
• Cache is the column that quietly saves real money. If your system prompt is stable across calls, prompt caching cuts cost 60% to 80%. When it's worth turning on.
• Batch matters for offline workloads. Half-price async jobs that complete inside 24 hours. If your work isn't user-facing, you should probably be using it.
• Cutoff tells you what the model doesn't know. For anything date-sensitive, pair it with retrieval. RAG basics here.

For a deeper read on picking, see How to choose an LLM in 2026: the definitive guide. Or check the pricing comparison when you're ready to put dollar numbers on each row.