What is a token in AI?
In models like GPT, a token is a substring of text—often part of a word. English text averages about 4 characters per token. This AI text splitter uses that approximation so you can stay under context limits without calling an API. Actual tokenization varies by model and language, so treat the estimate as a guide and leave headroom when needed.
Character vs token splitting
Character-based splitting cuts text at a fixed character count. It is predictable but can split mid-word or mid-sentence. Token-based (approximate) splitting uses a character-per-token ratio so chunks align better with how models count context. For split long text for AI workflows, token-aware chunking is usually preferred. This tool offers both: approximate mode for GPT token splitter style limits and character mode when you need strict size control.
Why GPT has context limits
Every model has a maximum context window (e.g. 8k, 32k, or 128k tokens). Sending text longer than that limit fails or gets truncated. A GPT text splitter or token splitter online like this one lets you split long documents into chunks that fit. You can then send one chunk at a time, or build embeddings per chunk for retrieval-augmented generation (RAG).
Best chunk size for embeddings
For embedding chunk tool and RAG pipelines, 256–512 tokens per chunk is common; some use up to 1024. Smaller chunks give finer retrieval but more chunks to index. Larger chunks preserve more context but can dilute relevance. Use the strategy selector: paragraph-first or sentence-first keeps semantic boundaries; AI embedding optimized applies sentence-boundary overlap for smoother continuity at chunk edges.
GPT context window examples
GPT-3.5 often uses 16k context; GPT-4 and GPT-4o support 128k. Claude and Gemini offer 200k or 1M tokens. Use the model preset dropdown to get suggested chunk sizes. Set max tokens per chunk below the model limit so each chunk fits with system and user message overhead—a context window AI tool like this keeps everything in the browser with no API calls.
When to use overlap vs no overlap
Use overlap (e.g. 50–200 tokens) when building embeddings or when context at chunk boundaries matters—overlap reduces cut-off sentences and improves retrieval. Use no overlap when you need strict, non-overlapping segments (e.g. for exact character budgets or duplicate-free processing). This tool uses sentence or word boundaries for overlap when possible so you do not get mid-sentence cuts.
How to prepare text for embeddings
Keep chunks within your model's limit (e.g. 512 or 8192 tokens). Prefer splitting on sentence or paragraph boundaries so chunks stay readable. Use overlap if your embedding model benefits from context continuity. This context window limit tool supports both approximate token limits and strict character limits, plus overlap—all in the browser with no data sent to a server.
How to prepare text for embeddings
Keep chunks within your model’s limit (e.g. 512 or 8192 tokens). Prefer splitting on sentence or paragraph boundaries so chunks stay readable. Use overlap if your embedding model benefits from context continuity. This context window limit tool supports both approximate token limits and strict character limits, plus overlap—all in the browser with no data sent to a server.
GPT-4 vs GPT-3 context windows (reference)
Newer models typically support larger context windows (e.g. 128k tokens). Older or smaller models may support 4k–8k. Check your model’s docs for the exact limit. When using this split text for ChatGPT or other APIs, set “max tokens per chunk” below the model’s limit so each chunk fits comfortably after system and user message overhead.