Free tool
Context Window Calculator
Will your document fit? Enter how much text you have — words, pages, characters, or tokens — and see which 2026 models can hold it in a single context window, with the cost to process it once.
≈ 66.7K tokens
Fits in 14 of 14 models · cheapest: Llama 4 Maverick at $0.015/run
- Claude Haiku 4.5Anthropic · 200K context$0.067/run
- GLM-5Zhipu / Z.ai · 200K context$0.067/run
- Mistral Large 3Mistral AI · 256K context$0.033/run
- Kimi K2 ThinkingMoonshot AI · 256K context$0.040/run
- GPT-5.4 miniOpenAI · 400K context$0.050/run
- Llama 4 MaverickMeta · 1M context$0.015/run
- DeepSeek V4DeepSeek · 1M context$0.029/run
- Grok 4.3xAI · 1M context$0.083/run
- Gemini 3.5 FlashGoogle · 1M context$0.100/run
- Gemini 3.1 ProGoogle · 1M context$0.133/run
- Qwen 3.7 MaxAlibaba · 1M context$0.167/run
- Claude Sonnet 4.6Anthropic · 1M context$0.200/run
- Claude Opus 4.8Anthropic · 1M context$0.333/run
- GPT-5.5OpenAI · 1.05M context$0.333/run
Token estimate uses standard heuristics (~4 characters or ~0.75 words per token, ~500 words/page) — actual counts vary by tokenizer and language. Cost is the input price to process your text once (output not included); context windows and prices are a June 2026 snapshot. The full context is rarely free — leave headroom for the reply.
Frequently asked
How many tokens is my document?
A good rule of thumb is ~0.75 words per token, or about 4 characters per token, for typical English text. So 50,000 words is roughly 67,000 tokens, and a 500-word page is about 670 tokens. Code, other languages, and unusual formatting tokenize differently, so treat the estimate as a ballpark, not an exact count.
Which AI model has the biggest context window in 2026?
By verified API context windows, GPT-5.5 leads at ~1.05M tokens, with Gemini 3.1 Pro, Claude Opus 4.8 / Claude Sonnet 4.6, Grok 4.3, and the open Llama 4 Maverick all at ~1M. Google has discussed a larger Gemini tier (~2M) and Meta a 10M Llama 4 Scout variant, but those aren't standard API rows. For very long inputs, watch for context-tiered pricing — several models charge more above ~200K tokens.
Does a bigger context window cost more?
Two ways. First, processing more tokens costs more directly (price × token count), which this tool shows as the per-run input cost. Second, several flagships charge a higher per-token rate once a prompt passes ~200K tokens — so a 500K-token prompt can cost more than 2.5× a 200K one. Always leave headroom for the model's reply, too.
Should I use a huge context window or RAG?
If your text fits comfortably and you query it once or twice, a long context is simplest. If you have a large, mostly-static corpus you query repeatedly, retrieval (RAG) is usually cheaper and faster — you only send the relevant chunks each time instead of paying for the whole document on every call.