What Are the Context Window Sizes in 2026?
Gemini 2.0: 2M tokens. Claude: 200K tokens. GPT-4o: 128K tokens. Llama 3.1: 128K. Mistral Large: 128K. Context quality degrades beyond ~32K for most models.
Full Comparison Table
| Model | Context Window | ~Pages of Text | Provider |
|---|---|---|---|
| Gemini 2.0 Pro | 2,000,000 | ~6,154 | |
| Gemini 2.0 Flash | 1,000,000 | ~3,077 | |
| Claude Opus/Sonnet | 200,000 | ~615 | Anthropic |
| GPT-4o | 128,000 | ~394 | OpenAI |
| GPT-4o Mini | 128,000 | ~394 | OpenAI |
| Llama 3.1 (all sizes) | 128,000 | ~394 | Meta |
| Mistral Large | 128,000 | ~394 | Mistral |
| Mistral Small 3.1 | 128,000 | ~394 | Mistral |
| DeepSeek V3 | 128,000 | ~394 | DeepSeek |
The "Lost in the Middle" Problem
Research shows that LLMs perform best on information at the beginning and end of the context window, but struggle with information in the middle. This means a 128K context window doesn't give you 128K tokens of equally reliable retrieval. For most models, information recall starts degrading noticeably beyond ~32K tokens.
Practical Implications
- Don't stuff the context — Putting irrelevant information in the context hurts quality and increases cost
- Put important info first and last — Place key instructions at the start and relevant data at the end
- Use RAG for large corpora — For 100K+ token knowledge bases, retrieval-augmented generation beats stuffing the context
- Gemini's 2M window is real but costly — Processing 2M tokens costs significant money and latency even at Flash pricing
Context Window vs. Effective Context
The advertised context window is the maximum. The effective context — where the model reliably retrieves and reasons about information — is typically much smaller. Claude and Gemini have shown the best long-context performance in benchmarks like NIAH (Needle In A Haystack), but all models degrade with length.
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