A larger context window is useful only when the model can retrieve and use the relevant information reliably. Compare documented limits, effective retrieval quality, latency, and the cost of sending your real prompt. Maximum tokens are a capacity specification, not a quality benchmark.
A practical evaluation method
- Test several context depths with answer evidence located near and far from the query.
- Measure input cost and time to first token as context grows.
- Compare full-context calls with retrieval, caching, and summarization alternatives.
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.
