Context Window Optimizer

Optimize your LLM context window usage with real-time token counting and cost estimation.

How It Works

  1. Select a model: Choose the target LLM to see its context limit.
  2. Enter your prompts: Paste your system prompt, RAG context and user question.
  3. Visualize usage: Instantly see what percentage of context you're using.

Frequently Asked Questions

How many tokens can I use with GPT-4?
GPT-4 Turbo supports up to 128K tokens. GPT-4o also up to 128K. In practice, stay under 80% of the limit to leave room for the response and avoid errors.
Does long context cost more?
Yes, you pay per token in input AND output. With GPT-4, 100K tokens of context costs ~$1 per request. Optimize your context to reduce costs.
What is Claude's context window?
Claude 3 Opus, Sonnet and Haiku all support 200K tokens of context, the largest on the market. Ideal for long documents or extended conversations.
How do I calculate the number of tokens?
Approximate rule: 1 token ≈ 4 characters in English, ≈ 3 characters in French. This tool uses OpenAI's cl100k_base tokenization for precise counting.
Should I fill all available context?
No. More context = more potential noise. The LLM can get lost in too much information ("lost in the middle" effect). Prioritize targeted, relevant context.
What context/response ratio should I target?
Reserve 20-30% of your token budget for the response. If you use 100K context tokens, expect responses of 20-30K tokens maximum.
Ailog
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Fit it

Visualize your context window usage.

16 tok
55 tok
9 tok
Claude Sonnet 4.51.0M max
System Context Question
0.01% used (log scale)
80 Used · 1000k Available · 0.0% capacity

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Ailog optimizes context automatically.

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How It Works

  1. 1

    Select a model

    Choose the target LLM to see its context limit.

  2. 2

    Enter your prompts

    Paste your system prompt, RAG context and user question.

  3. 3

    Visualize usage

    Instantly see what percentage of context you're using.

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Frequently Asked Questions

GPT-4 Turbo supports up to 128K tokens. GPT-4o also up to 128K. In practice, stay under 80% of the limit to leave room for the response and avoid errors.

Yes, you pay per token in input AND output. With GPT-4, 100K tokens of context costs ~$1 per request. Optimize your context to reduce costs.

Claude 3 Opus, Sonnet and Haiku all support 200K tokens of context, the largest on the market. Ideal for long documents or extended conversations.

Approximate rule: 1 token ≈ 4 characters in English, ≈ 3 characters in French. This tool uses OpenAI's cl100k_base tokenization for precise counting.

No. More context = more potential noise. The LLM can get lost in too much information ("lost in the middle" effect). Prioritize targeted, relevant context.

Reserve 20-30% of your token budget for the response. If you use 100K context tokens, expect responses of 20-30K tokens maximum.

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