What is the LLM Context Window Calculator?
The LLM Context Window Calculator is a free, browser-based tool that helps developers and AI engineers plan their token budget before making LLM API calls. Instead of discovering a context overflow at runtime, you can input your system prompt, RAG chunk configuration, conversation history, tool definitions, and expected response size — and see exactly how your tokens are distributed across each component.
All calculations run entirely in your browser. No data is sent to any server.
Who is this for?
- Developers building RAG (Retrieval-Augmented Generation) pipelines who need to tune chunk size and top-K retrieval
- AI engineers designing multi-turn chatbots with large conversation histories
- Anyone who has encountered a "context length exceeded" error and wants to prevent it proactively
- Teams evaluating which LLM to use based on context window size and pricing
How token budgeting works
Every LLM API call has a context window — a maximum number of tokens the model can process in a single request. This window must hold all content: your system prompt, any retrieved documents (RAG), conversation history, tool/function definitions, and room for the model's response. Exceeding this limit results in truncation or an API error.
In a typical RAG pipeline, the token breakdown looks like this: system prompt (500–2,000 tokens), retrieved chunks (chunk_size × top_K tokens), conversation history (turns × avg_tokens_per_turn), tool definitions (150–300 tokens per function), and reserved response space. This calculator makes that breakdown visible and actionable.
FAQ
How many tokens does a RAG pipeline use?
A typical RAG request uses 3,000–10,000 tokens per query. The biggest driver is your retrieval configuration: with 512-token chunks and top-K=5, you're already at 2,560 tokens just for retrieved context. Add a 500-token system prompt, 8 turns of 300-token history, and a 1,000-token response reservation — you're at ~7,000 tokens before the user's question.
What happens when you exceed the context window?
The API returns an error (typically a 400 error with a message like "This model's maximum context length is X tokens"). Some frameworks silently truncate the input instead, which can cause the model to lose important context — leading to incorrect or hallucinated responses. This tool helps you catch that risk before it happens.
How do I reduce token usage in my LLM app?
The most effective optimizations are: (1) Reduce top-K from 10 to 3–5 — this alone often saves 2,000–5,000 tokens. (2) Implement a sliding window for chat history — keep only the last 5–8 turns. (3) Summarize older conversation turns instead of keeping them verbatim. (4) Trim your system prompt — many prompts have 30–40% redundancy. (5) Use smaller chunk sizes (256–512 tokens) with higher overlap rather than large chunks.
What's the context window limit for major models?
As of June 2026: GPT-5.4 and GPT-4.1 (1M), GPT-4o (128K), Claude Opus 4.8 and Sonnet 4.6 (1M), Claude Haiku 4.5 (200K), Gemini 2.5 Pro/Flash (1M), Llama 4 Scout (10M), DeepSeek V4 Flash/Pro (1M). This tool is updated as model limits change — check the model selector for the latest values.
How is this different from a token counter?
A token counter tells you how many tokens a piece of text contains. This tool is a token budget planner — it models your entire LLM application architecture (RAG pipeline, chat history, tool use) and shows you how the total context budget is allocated across each component. It also provides a cost estimate and optimization recommendations.
What is top-K in RAG and how does it affect token usage?
Top-K is the number of document chunks retrieved from your vector database per query. If your chunk size is 512 tokens and top-K is 10, you're injecting 5,120 tokens of retrieved context per request. Reducing top-K from 10 to 5 cuts this in half — often the single highest-impact optimization for token budget management.
How do tool definitions affect my token budget?
Each function/tool definition you pass to the model costs approximately 100–300 tokens depending on how detailed the parameter descriptions are. With 10 tools, you may be spending 1,000–3,000 tokens on tool definitions alone. Consider whether all tools are needed for every request, or if you can conditionally include only relevant tools.
Which LLM has the best cost per token for RAG applications?
For RAG workloads where input tokens dominate, Gemini 2.5 Flash ($0.15/1M input, 1M context) offers the best value among frontier models. DeepSeek V4 Flash ($0.14/1M, 1M context) is slightly cheaper but with lower availability. For high-quality reasoning with long context, Gemini 2.5 Pro ($1.25/1M) gives you 1M context at a lower price than Claude Sonnet 4.6 ($3.00/1M). Use this calculator to compare the actual cost for your specific RAG configuration.
How do I calculate the cost of a RAG API call?
Multiply your total input tokens by the model's input price per million, then add the expected output tokens multiplied by the output price. Example: GPT-5.4 at $2.50/1M input — a 10,000-token request costs $0.025. This tool calculates that automatically based on your configuration. For production planning, multiply by your expected daily request volume to estimate monthly spend.
What is prompt caching and how does it reduce token costs?
Prompt caching lets you reuse previously computed tokens (typically your system prompt and static context) across requests, paying only 10–25% of the normal input price for cached portions. Anthropic, OpenAI, and Google all support some form of caching. For RAG applications with a large fixed system prompt, caching can reduce input costs by 40–60% in production. This tool shows your base cost — actual cached costs will be lower.
GPT-5.4 vs Claude Sonnet 4.6 — which fits more context for the same cost?
GPT-5.4 ($2.50/1M input, 1M context) and Claude Sonnet 4.6 ($3.00/1M input, 1M context) both offer 1M token windows. At equal context usage, GPT-5.4 is 20% cheaper on input. However, Claude Sonnet 4.6 costs more on output ($15 vs $15 — identical). For input-heavy RAG workloads, GPT-5.4 has a cost advantage. Use the model selector to compare exact costs for your specific token breakdown.
Model Context Window & Pricing Comparison
All major LLM API models ranked by context window size. Use the calculator above to estimate exact costs for your RAG configuration.
| Model | Provider | Context Window | Input (per 1M) | Output (per 1M) |
|---|---|---|---|---|
| Llama 4 Scout | Meta | 10,000,000 | $0.08 | $0.30 |
| GPT-5.4 | OpenAI | 1,050,000 | $2.50 | $15.00 |
| GPT-4.1 | OpenAI | 1,050,000 | $3.00 | $12.00 |
| GPT-4.1 mini | OpenAI | 1,050,000 | $0.80 | $3.20 |
| Claude Opus 4.8 | Anthropic | 1,000,000 | $5.00 | $25.00 |
| Claude Sonnet 4.6 | Anthropic | 1,000,000 | $3.00 | $15.00 |
| Gemini 2.5 Pro | 1,000,000 | $1.25 | $10.00 | |
| Gemini 2.5 Flash | 1,000,000 | $0.15 | $0.60 | |
| DeepSeek V4 Flash | DeepSeek | 1,000,000 | $0.14 | $0.28 |
| DeepSeek V4 Pro | DeepSeek | 1,000,000 | $0.44 | $0.87 |
| Claude Haiku 4.5 | Anthropic | 200,000 | $1.00 | $5.00 |
| GPT-4o | OpenAI | 128,000 | $3.75 | $15.00 |
| GPT-4o mini | OpenAI | 128,000 | $0.30 | $1.20 |
Prices in USD per 1M tokens · Updated June 28, 2026 · Sources: OpenAI, Anthropic, Google, DeepSeek, Meta official documentation
About the data
Model context limits and pricing are sourced from official provider documentation and updated regularly. Token estimates use the ~1.3 tokens/word heuristic for browser-side calculation without requiring an API call. Actual token counts from the provider's tokenizer may vary slightly. Prices shown are in USD per 1 million tokens.
Last updated: June 2026