Avoid Context Limit Errors — Catch Overflow Before It Fails
"Context length exceeded" is a planning failure, not bad luck. Catch High Risk content before sending: the limit inside the estimate range is the warning.
View Resource →Context Tools
Context Window Estimator
Will this content fit the model's context window? Paste it and get a context budget plan: content-type-aware token estimates (always a range, never false precision), a response budget you choose, a range-aware fit verdict from Safe to Will Not Fit, and the same content compared across models. Runs entirely in your browser — nothing is sent anywhere.
Paste the prompt, document, transcript, or code you plan to send. It never leaves the browser.
Input Analysis (live — informational, not the report)
Context Budget Report
Compare Model Context Windows — Same Content, Every Model
Not "which window is biggest" but "where does MY content fit": the same material and response budget checked across GPT-5, Claude, and Gemini in one report.
View Resource →Context Budget for Long Conversations — the History Eats the Window
Every turn resends the whole history. Budget a growing chat: how much window the conversation already consumes and how many turns of life it has left.
View Resource →Context Window Planning for RAG — Budget the Retrieved Docs
RAG context is a budget with line items: retrieved documents, the question, and the answer all share one window. Plan how many chunks actually fit.
View Resource →Estimate AI Response Budget — Reserve Room for the Answer
Truncated answers are usually a budgeting mistake: nothing was reserved for the response. See how the reserved output changes the whole calculation.
View Resource →Estimate Token Budget — Plan Before You Paste
Token budget planning for real workloads: how much of the window a transcript actually consumes, what is left for the answer, and how much headroom remains.
View Resource →Fit Large Codebase Context — Code Tokenizes Differently
Code is denser in tokens than prose: symbols, indentation, and short identifiers all cost extra. Estimate code files with code ratios before pasting them.
View Resource →Plan Large Document Analysis — When It Will Not Fit
A book-length document against a 200K window: the estimate exceeds the budget at both ends of the range. The plan starts from Will Not Fit, not from hope.
View Resource →Token Estimation Guide — Why Ranges, Why Content Type Matters
How character counts become honest token estimates: content-type ratios, why code and CJK text tokenize denser, and why a range beats a fake-exact number.
View Resource →Will My Prompt Fit? — the Context Budget Check
Stop guessing whether content fits the model. A budget check before sending: estimated token range, reserved response space, and a fit verdict from Safe to Will Not Fit.
View Resource →AI Long Document Analysis Workflow
Get AI to actually read a document that's too big for one prompt — fit it to the model, split it cleanly, package the parts, and analyze them without losing the thread.
View Playbook →AI RAG Context Workflow
Prepare documents for a RAG system so retrieved answers stay accurate — budget the chunk size to the model, ground the sources against drift, and split them on clean boundaries for retrieval.
View Playbook →How it works
Paste the content you plan to send — a document, transcript, code file, or chat history — and pick the target model and a response budget. The live Input Analysis shows characters, words, paragraphs, the detected content type (prose, code, mixed, or CJK-heavy — each tokenizes differently), and the estimated token range. Click Estimate Context Fit for the full context budget report: the model's window minus your reserved response budget gives the available input budget; the estimate range against that budget gives a fit verdict — Safe, Likely Safe, Near Limit, High Risk (the limit falls inside the estimate range), or Will Not Fit — plus a budget breakdown, action guidance, and the same content compared across all supported models. Token figures are always presented as estimates with ranges, never as tokenizer output, and model windows live in one central table verified June 2026. Nothing leaves your browser.
Use cases
- Checking whether a long document fits before pasting it
- Planning the token budget for a recurring AI workflow
- Diagnosing "context length exceeded" errors before retrying
- Choosing the right model for oversized content
Pro tips
- Set the response budget honestly. "Will it fit" questions almost always forget that the answer needs room too — a 200K window with a 16K reserved response is a 184K input budget, not 200K.
- Treat High Risk as a no: it means the context limit falls inside the estimate range, so the same content may fit one day and fail the next depending on tokenization. Don't build workflows on that margin.
- Use the model comparison before cutting content. The same material that's Near Limit on one model can be Safe on a million-token window — switching models is often cheaper than splitting.
- For conversations that will continue, leave more headroom than the verdict requires — every follow-up turn adds the whole history again. Safe today is Near Limit ten turns later.
FAQ
Is this a token counter?
No — it's a context budget planner. A counter answers "how many tokens is this?" and stops. This tool answers the decision question: "will it fit the model I'm about to use, with the response space I need — and what do I do if it won't?" The token estimate is one input into that plan, not the product.
Why does it show a range instead of an exact number?
Because exact would be a lie. Real token counts depend on each model's tokenizer; this tool estimates from characters using content-type-aware ratios (code tokenizes denser than prose; CJK text much denser still). A range is honest about that uncertainty — and the fit verdict uses it: High Risk specifically means the limit falls inside the range.
What does the response budget change?
Everything past trivial sizes. The context window is shared between your input and the model's output, so the room you reserve for the answer comes straight out of the input budget. The same 150K-token document can be Safe with a small reserved response and Will Not Fit when you reserve the model's maximum output.
It says Will Not Fit — now what?
The guidance routes you: split the content into sequenced parts (the Long Prompt Splitter is built for that), switch to a larger-window model (the comparison section shows where it fits), or — if the goal is continuing earlier work rather than re-sending everything — carry a compact state package instead of the full transcript, which is the Context Handoff Builder's job.
How current are the model window sizes?
They live in one central table in the tool, verified June 2026, and the report states that date. Provider limits change — when they do, the table is updated in one place. If you're planning against the edge of a budget, check the provider's current documentation; the tool tells you this too.
Why does the content type matter?
Because tokenizers don't see characters the way you do. Code carries symbols and indentation that tokenize denser than prose; CJK languages can use one token per character or two. The tool detects the content type deterministically (prose, code, mixed, CJK-heavy) and applies the matching estimate ratios — pasting a code file and an essay of the same length gives different token estimates, as it should.
How is this different from the other Context Tools?
Different verbs. The Estimator MEASURES — will it fit, how much budget is there. The Long Prompt Splitter FITS content that's too big by splitting it into sequenced parts. The Context Handoff Builder CARRIES work into a new session. The Long Input Formatter PACKAGES source material with delimiters and grounding. This tool is the category's starting point: it tells you which of those you need.