Token Budget Calculator
🎯 Token Budget Calculator
Visualize token counts and context window usage across LLM providers
📝 Input Text
📊 Context Window Usage
Note: Token counts are estimates using character-based heuristics. Actual counts vary by ~5-10% depending on content. GPT models use cl100k_base, Claude uses a similar BPE tokenizer, Llama uses SentencePiece.
🗜️ Compaction Simulator
Simulate how summarization or truncation affects your token budget. Useful for planning memory compaction in long-running agents.
💰 Cost Impact
Paste text to see estimated cost savings across providers.
🔄 Fits in Context?
Check if your compacted text fits the target model's context window.
📈 Headroom
See how much space remains for responses after your input.
📚 Token Economy Reference
Rule of Thumb
1 token ≈ 4 characters in English, or ≈ 0.75 words. Code and non-English text typically uses more tokens per character.
Why It Matters
Context window = working memory. Overstuffing dilutes attention. Strategic compaction keeps agents focused.
Compaction Strategies
Summarize old turns, drop failed tool calls, truncate large outputs, dedupe repeated content.