Context Windows in 2026
A context window is how much text (input + output) a model can consider at once. Windows ballooned in 2026, but bigger isn't automatically better — large context is expensive and, past a point, accuracy drops. Here's the comparison and how to think about it.
The Numbers
| Model | Context | Max output |
|---|---|---|
| Gemini 3.1 Pro | up to 2M | 64K+ |
| Claude Opus 4.8 / Sonnet | 1M | large |
| GPT-5.5 | 1M | large |
| DeepSeek V4 | 1M | 384K |
| Kimi K2.6 | 256K | 256K |
| MiniMax M2.7 | 205K | — |
| GLM-5.1 | ~200K | 128K |
Gemini 3.1 Pro leads at 2M; the Western flagships and DeepSeek sit at 1M; the open-weight Chinese models cluster around 200–256K.
Big Context Isn't Free
Two costs people miss:
- Price. You pay the input rate on every token you send. Stuffing a 500K-token document into each call is expensive, and some models add a long-context surcharge past a threshold — Gemini's input roughly doubles past 200K, for example. RAG pipelines can silently push every request into the higher bracket.
- Accuracy. Nominal window ≠ usable window. Retrieval accuracy often degrades well before the ceiling; many models are noticeably weaker at recalling details past ~128K in practice. Don't over-index on the headline number.
When You Actually Need Large Context
- Whole-repo code analysis / refactors (Gemini 3.1 Pro, DeepSeek).
- Long-document Q&A and contracts — but consider RAG first.
- Long agent runs that accumulate history — though pruning beats resending.
For most tasks, retrieval (RAG) beats brute-force context: fetch the relevant chunks instead of pasting everything. It's cheaper and often more accurate.
Cost-Control Tips
- Prune and summarize conversation history instead of resending it raw.
- Use RAG to send only relevant context.
- Watch the surcharge threshold (e.g. 200K on Gemini/Claude tiers).
- Cache the stable prefix — repeated context can be up to 90–98% cheaper. See prompt caching guide and how to reduce AI API costs.
Bottom Line
Gemini 3.1 Pro has the biggest window (2M); most flagships offer 1M. But large context is costly and hits an accuracy wall — use RAG, prune history, and cache prefixes rather than paying for a giant window you don't fully use. Pick a model on your real task in how to choose an LLM.
Start free with a $1 credit and test long-context performance on your documents.