AI Context Window Visualizer
About Context Window Visualizer
Visualize how your LLM context window is allocated across system prompts, tools, conversation history, RAG chunks, and response budget. Yellow warning at 80% usage, red critical at 95%.
Cost estimates are based on published API pricing. Token counts from pasted text use a ~4 chars/token approximation. For precise counts, use the Token Counter tool.
What This Tool Does
As of the June 21, 2026 lineup verification, flagship LLM context windows have converged near one million tokens: OpenAI's GPT-5.5 accepts 1,050,000 tokens, Anthropic's Claude Opus 4.8 and Claude Fable 5 accept 1,000,000, and Google's Gemini 3.1 Pro accepts 1,048,576. This visualizer shows how a window is actually allocated across system prompt, tools, conversation history, and retrieved documents.
Use How to Use for execution steps and FAQ for constraints, policies, and edge cases.
Last updated:
This tool is provided as-is for convenience. Output should be verified before use in any production or critical context.
Agent Invocation
Best Path For Builders
Browser workflow
Runs instantly in the browser with private local processing and copy/export-ready output.
Browser Workflow
This tool is optimized for instant in-browser execution with local data handling. Run it here and copy/export the output directly.
/context-window-visualizer/
For automation planning, fetch the canonical contract at /api/tool/context-window-visualizer.json.
How to Use AI Context Window Visualizer
- 1
Enter your model's context window
Input the model name (e.g., 'GPT') and its total context window (e.g., 128,000 tokens). The visualizer shows how the available context is allocated.
- 2
Add system prompt
Paste your system prompt. The visualizer calculates tokens and shows how many tokens it consumes. Account for this when planning conversation length.
- 3
Estimate conversation length
Input expected number of messages in the conversation, average tokens per user message, and average tokens per assistant response. The tool shows total conversation tokens and remaining context.
- 4
Plan for output
Reserve tokens for the expected response (e.g., 1000-2000 for longer outputs). The visualizer shows the remaining context available. If it gets tight, reduce history or truncate earlier messages.
Frequently Asked Questions
What is a context window in AI models?
How is this different from Token Budget Planner?
Why do I run out of context window?
Which AI model has the largest context window?
Does this tool make API calls?
How do LLM context window sizes compare in 2026?
The table lists the context window (maximum input) and maximum output tokens for the 31 current models in this site's vendor-verified dataset. One-million-token windows are now standard at the flagship tier across OpenAI, Anthropic, Google, xAI, and DeepSeek; mid-tier and open-weight models typically sit between 131K and 400K.
| Model | Provider | Context window (tokens) | Max output | Released |
|---|---|---|---|---|
| GPT-5.5 | OpenAI | 1,050,000 | 128K | 2026-04 |
| GPT-5.5 Pro | OpenAI | 1,050,000 | 128K | 2026-04 |
| GPT-5.4 | OpenAI | 1,050,000 | 128K | 2026-03 |
| GPT-5.4 Pro | OpenAI | 1,050,000 | 128K | 2026-03 |
| GPT-5.4 Mini | OpenAI | 400,000 | 128K | 2026-03 |
| GPT-5.4 Nano | OpenAI | 400,000 | 128K | 2026-03 |
| GPT-5.3-Codex | OpenAI | 400,000 | 128K | 2026-02 |
| Claude Fable 5 | Anthropic | 1,000,000 | 128K | 2026-06 |
| Claude Opus 4.8 | Anthropic | 1,000,000 | 128K | 2026-05 |
| Claude Opus 4.7 | Anthropic | 1,000,000 | 128K | 2026-04 |
| Claude Opus 4.6 | Anthropic | 1,000,000 | 128K | 2026-02 |
| Claude Sonnet 4.6 | Anthropic | 1,000,000 | 64K | 2026-02 |
| Claude Sonnet 4.5 | Anthropic | 200,000 | 64K | 2025-09 |
| Claude Haiku 4.5 | Anthropic | 200,000 | 64K | 2025-10 |
| Gemini 3.1 Pro | 1,048,576 | 66K | 2026-02 | |
| Gemini 3.5 Flash | 1,048,576 | 66K | 2026-05 | |
| Gemini 3 Flash | 1,048,576 | 66K | 2025-12 | |
| Gemini 3.1 Flash-Lite | 1,048,576 | 66K | 2026-05 | |
| Gemini 2.5 Pro | 1,048,576 | 66K | 2025-06 | |
| Gemini 2.5 Flash | 1,048,576 | 66K | 2025-06 | |
| Gemini 2.5 Flash-Lite | 1,048,576 | 66K | 2025-07 | |
| Grok 4.3 | xAI | 1,000,000 | 66K | 2026-05 |
| Grok 4.20 | xAI | 1,000,000 | 66K | 2026-03 |
| Grok Build 0.1 | xAI | 256,000 | 66K | 2026-05 |
| DeepSeek V4 Flash | DeepSeek | 1,000,000 | 66K | 2026-04 |
| DeepSeek V4 Pro | DeepSeek | 1,000,000 | 66K | 2026-04 |
| Mistral Medium 3.5 | Mistral | 256,000 | 66K | 2026-04 |
| Mistral Large 3 | Mistral | 256,000 | 66K | 2025-12 |
| Mistral Small 4 | Mistral | 256,000 | 66K | 2026-03 |
| Llama 4 Scout (Groq) | Meta/Groq | 131,072 | 8K | 2025-04 |
| Llama 3.1 8B Instant | Meta/Groq | 131,072 | 131K | 2024-07 |
Source: official model documentation from OpenAI, Anthropic, Google, xAI, DeepSeek, Mistral, and Groq. Lineup verified 2026-06-21; refreshed July 19, 2026.
Does a bigger context window mean better results?
Not by itself. The advertised window is a capacity limit, not a quality guarantee — retrieval quality over very long prompts varies by model and by where information sits in the prompt. Some providers also bill long-context requests at a premium above an input-size threshold, so check the vendor's pricing page before assuming the full window costs the same per token. The practical question is usually allocation, not capacity: how much of the window your system prompt, tool definitions, conversation history, and retrieved documents each consume.
How should I budget a context window?
- Measure the fixed overhead first: system prompt plus tool schemas. This is paid on every single request.
- Reserve output headroom — the model's maximum output tokens come out of the same budget on most APIs.
- Cap conversation history and retrieved-document chunks explicitly rather than letting them grow until requests fail.
- Use the visualizer above to see the allocation as proportions of a real model's window — it runs client-side, so prompt structure and sizes stay on your machine.