Context Window Calculator — Fit vs Model Limit
See whether your prompt fits each model context window with configurable output reserve. Count tokens first with the LLM token counter, or check word and character stats before you paste.
How fit is calculated
We count tokens locally with the same pipeline as the LLM token counter — js-tiktoken for OpenAI models, heuristics for Claude and Gemini. For each model we subtract your output reserve (default 15 %) from the published context window to get an effective input budget. Your prompt tokens are compared to that budget: • Safe — under 80 % of the effective budget • Near limit — 80 % or more but still fits • Won't fit — prompt tokens exceed the effective budget
How is this different from the token counter context meter?
The token counter answers how many tokens? and shows a quick single-model usage bar. This tool answers will it fit? — it compares all models with fit verdicts and a configurable output reserve. Use the token counter to audit counts and BPE breakdown; use this tool before you pick a model or trim a prompt.
Privacy
All counting runs in your browser. FastMinify never uploads prompts to OpenAI, Anthropic, Google, or any other provider.
Limitations
Estimate-tier counts are directional (±5–15 % typical). Context limits come from a manually maintained table — confirm critical limits on provider docs. For USD cost planning, use the LLM pricing calculator. Image, audio, and tool-call token rules are out of scope for v1.
Options that change fit
FAQ
Context fit, reserve, and accuracy
What does the output reserve mean?
It holds back a percentage of the context window for the model reply. If GPT-4o has 128k tokens and you reserve 15 %, your effective input budget is 108.8k tokens — the rest is headroom for the assistant message.
How is this different from the token counter context meter?
The token counter shows a quick single-model usage bar while counting. This tool compares all registry models with fit verdicts and configurable reserve — use it when the question is will it fit, not how many tokens.
Why are Claude rows labeled Estimate?
Anthropic does not publish a browser-side tokenizer. We use the same characters-per-token heuristic as the token counter and label results Estimate so you are not misled.
Does my prompt leave the browser?
No. All tokenization and fit math run locally. FastMinify does not call provider APIs.
Where do context limits come from?
From the same versioned llm-models.json table as the token counter and pricing tools, reviewed manually.
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Token counting, pricing estimates, and context-window fit — 100% browser-local.
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