Quick start
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Paste text
Long docs and code supported; table updates live.
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Check status
Green fits; red overflow means trim or switch model.
Context Window Checker
Paste prompts or docs to see context limits and estimated token usage per major model. Decide if you need trimming or a larger window.
Privacy: processed locally, never uploaded.
↓ Paste in the input area below to see results instantly
Text to check
Paste prompts, docs, or chat content to see context usage per model.
| Model | Est. tokens | Context limit | Used | Status |
|---|---|---|---|---|
| GPT-4o | 22 | 128,000 | 0.0% | Fits |
| GPT-4o mini | 22 | 128,000 | 0.0% | Fits |
| GPT-4.1 | 22 | 1,047,576 | 0.0% | Fits |
| o3-mini | 22 | 200,000 | 0.0% | Fits |
| Claude Sonnet 4 | 22 | 200,000 | 0.0% | Fits |
| Claude Haiku 3.5 | 22 | 200,000 | 0.0% | Fits |
| Gemini 2.0 Flash | 22 | 1,048,576 | 0.0% | Fits |
Notes
Reading results
Green "Fits" means estimated tokens fit the context window; red "Overflow" means you need trimming or a larger model. Token counts are heuristic and may differ slightly from official counters.
Paste prompts or docs to see context limits and estimated token usage per major model. Decide if you need trimming or a larger window.
vs Token Estimator
This tool compares per-model windows; Token Estimator focuses on char and word stats.
Features and use cases
Estimate token count vs common model context windows and warn about possible truncation.
Use before long-doc Q&A, model selection, and planning RAG context assembly.
Typical Workflow
When preparing to submit long text to an AI model, first paste your content here. The system automatically calculates token count and displays context window limits of major models. Green indicators show compatible models, while red flags suggest trimming or switching to higher-capacity models.
For code files, consider removing comments and blank lines before checking. When nearing limits (e.g., GPT-4 shows 90% usage), click 'Optimization Tips' for targeted advice like splitting paragraphs or switching to API streaming.
Examples
Long prompt
Input
10k+ char document
Output
Per-model fit/overflow
FAQ
Match official counts?
Heuristic only; verify edge cases with each provider tokenizer.
Why do token counts vary across tools?
Variations stem from encoder versions (e.g., old vs new GPT-3.5) or whether special tokens (like system prompts) are counted. Our tool supports encoder switching and clearly marks meta-instruction tokens, aligning with official APIs.