Which AI model has the longest context window?
The short answer
Gemini 2.5 Pro has the longest production context window at 2,000,000 tokens — by a wide margin. Gemini 2.5 Flash follows at 1,000,000.
Most competitors top out at 128,000 tokens (the GPT-4o family, Llama 3.1, DeepSeek V3, Mistral Large). Claude Opus, Sonnet, and Haiku sit in between at 200,000.
Ranked by context window size
| Model | Context | Practical use case |
|---|---|---|
| Gemini 2.5 Pro | 2,000,000 | Entire codebases, long-doc Q&A without retrieval |
| Gemini 2.5 Flash | 1,000,000 | Same use cases at lower cost, lower quality |
| Claude Opus 4.7 | 200,000 | Long-context reasoning at frontier quality |
| Claude Sonnet 4.6 | 200,000 | Long-context production workloads |
| Claude Haiku 4.5 | 200,000 | Long-context high-volume |
| Qwen 2.5 72B / Coder | 131,072 | Open-weights long-context |
| GPT-4o family | 128,000 | Standard long-context for OpenAI users |
| Llama 3.1 (all sizes) | 128,000 | Open-weights; quality degrades past ~32k |
| DeepSeek V3 | 128,000 | Frontier-class at low price |
| Mistral Large | 128,000 | EU-hosted long-context |
The gap between "context length" and "useful context length"
A 1M-token window doesn't mean the model uses every token equally well. Independent evaluations consistently show:
- Quality degrades on retrieval tasks as context grows past ~32k for most models.
- Gemini 2.5 Pro is currently the best at maintaining recall quality across the full window.
- Claude Sonnet/Opus maintain quality well to ~100k, then drift.
- Llama 3.1 in particular degrades sharply past 32k despite the 128k advertised window.
If your workload depends on the model finding a specific fact buried deep in a long context, test it with your actual prompts before committing.
When you actually need a long context window
- Loading entire codebases for refactoring or audit — Gemini 2.5 Pro is uniquely good here.
- Long-document Q&A without chunking and retrieval.
- Multi-document synthesis where retrieval would lose cross-document relationships.
- Multi-turn conversations with extensive history that you don't want to summarize.
For everything else — most chat, RAG, classification, extraction — 32k-128k is plenty, and shorter is cheaper to run.
Get cost at your context length
Paste your full context into the counter. It will show exact token counts and per-call cost across every model, so you can see which fit your workload and what they'd cost.
Try this on every model
- Claude Opus 4.7 $15.00/$75.00
- Claude Sonnet 4.6 $3.00/$15.00
- Claude Haiku 4.5 $0.80/$4.00
- GPT-4o $2.50/$10.00
- GPT-4o mini $0.15/$0.60
- GPT-4 Turbo $10.00/$30.00
- Gemini 2.5 Pro $1.25/$10.00
- Gemini 2.5 Flash $0.07/$0.30
- Llama 3.1 405B $3.50/$3.50
- Llama 3.1 70B $0.59/$0.79
- Llama 3.1 8B $0.18/$0.18
- Mistral Large $2.00/$6.00
- DeepSeek V3 $0.27/$1.10
- Qwen 2.5 72B $0.90/$0.90
- Qwen 2.5 Coder 32B $0.80/$0.80