AI Cost Calculators
AI Context Window Cost Calculator
Compare the cost and context-limit risk of sending full conversation history with a managed strategy that retains recent turns and summarizes older messages.
Enter Your Conversation Context Plan
Compare full conversation history with recent-turn retention and summary-based memory.
Monthly Sessions and Tokens
Managed Context Strategy
Static Prefix Caching
Model and Summary Prices
Leave either summary price blank to use the matching main-model input or output price. Leave cached input blank to use the normal input price.
Platform, Setup, and Budget
Estimated monthly context workload
Model requests: 120,000
History growth per completed turn: 500 tokens
Effective cached share of static prefix: 56%
Summary refreshes per session: 2
Monthly summary calls: 20,000
Average summary input per refresh: 2,800 tokens
Context Cost and Limit Comparison
The managed estimate includes summary refresh tokens, cached static prefixes, fixed costs, and amortised implementation.
Managed monthly planning cost
Full-history cost
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Monthly saving
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Managed cost per session
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Full conversation history
Peak context use: 7.42%
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Normal input: 600,000,000
Cached input: 168,000,000
Assistant output: 42,000,000
Summary tokens: 0
Peak total context: 9,500
Overflow turns per session: 0
Cost per session: —
Summary cost: —
Managed recent turns and summary
Peak context use: 5.31%
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Normal input: 516,000,000
Cached input: 168,000,000
Assistant output: 42,000,000
Summary tokens: 72,000,000
Peak total context: 6,800
Overflow turns per session: 0
Cost per session: —
Summary cost: —
Estimated processed-token reduction: 12,000,000 tokens (1.48%)
Managed summary overhead: 72,000,000 tokens
Full-history first overflow turn: No overflow in the planned session
Managed-context first overflow turn: No overflow in the planned session
First-year comparison: Enter current prices
Implementation payback: Enter current prices
Price inputs entered: 0 of 7
Budget status: Add a budget to compare
* Important: This calculator stores no model, cached-input, summary-model, memory-platform, or implementation price. Enter the current rates for the exact provider and model. Blank summary prices fall back to the main-model rates, and a blank cached-input price falls back to normal input. Reasoning tokens, tool calls, images, audio, retrieval, tokenization differences, taxes, and provider-specific long-context rules can change the final cost.
Multi-turn AI cost can rise quickly because earlier messages are often sent again on every request. A context strategy can retain recent turns, summarize older history, and preserve a cacheable prefix. This calculator compares that managed approach with full conversation history.
Measuring Token Growth Across Conversation Turns
Enter monthly sessions, average turns, static instructions, initial context, user tokens, and assistant output tokens. The full-history estimate grows the prompt after every turn by retaining all earlier user and assistant messages.
Results include monthly input and output tokens, peak tokens on the final turn, context-window use, turns above the entered limit, and cost per session.
OpenAI describes the context window as the combined token space used by input, output, and—in some models—reasoning tokens. Very large prompts can cause truncation or incomplete output when the limit is reached.
Comparing Full History With Managed Context
The managed scenario retains the selected number of recent turns. Once older messages exist, it uses the entered summary size instead of sending every old message again.
Summary refresh frequency controls how often the compact memory is regenerated. The calculator includes the input and output tokens consumed by those refresh calls.
The result compares full-history cost with managed-context cost after summary overhead, rather than treating summarization as free.
Including Cached Static Prefixes
Repeated system instructions, tool definitions, schemas, and examples may form a stable prefix. Enter the share of static tokens that can match exactly and the effective cache-hit rate.
Cached-prefix tokens use the entered cached-input price. Static tokens outside that effective hit are charged at the normal input price.
OpenAI currently documents automatic prompt caching for sufficiently long prompts and recommends placing repeated content before changing content. Actual cache behaviour, thresholds, retention, and prices depend on the provider and model.
Using Context Limits for Capacity Planning
Enter the model's total context limit. The calculator checks estimated input plus assistant output for each turn and reports the first overflow turn and the number of planned turns that exceed the limit.
A managed strategy can create more headroom, but summary quality and retrieval quality must still be tested. Removing details solely to reduce cost can damage answer quality.
Practical Decisions This Tool Supports
- Estimate long-running assistant and agent cost.
- Measure how conversation history grows by turn.
- Compare full history with recent-turn retention.
- Include summary refresh and compaction overhead.
- Estimate savings from cached static prefixes.
- Find the first turn likely to exceed a context limit.
- Calculate cost per session and managed-context saving.
- Check the context plan against a monthly budget.
Costs and Risks Outside the Estimate
The result does not automatically include reasoning tokens, tool calls, retrieval, images, audio, storage, vector search, data transfer, taxes, regional premiums, or provider minimum charges unless represented in the entered token averages or fixed monthly cost.
Tokenization varies by model and language. Use provider usage logs or token-counting tools for representative sessions before making a final architecture decision.
Frequently Asked Questions
Why does conversation cost grow over several turns?
When earlier messages are sent again with each new request, the model repeatedly processes an expanding history. Later turns therefore use more input tokens than the first turn.
What is managed context?
Managed context keeps a selected number of recent turns and replaces older history with a shorter summary or compacted representation. This can reduce repeated input tokens while preserving useful state.
How does prompt caching affect the estimate?
The calculator applies the entered cache-hit rate only to the cacheable share of the static prefix. Dynamic conversation history remains normal input unless the provider reports it as cached.
Why include summarization cost?
Creating or refreshing a summary can require additional model input and output tokens. A fair comparison should subtract that overhead from the context savings.
What does an overflow turn mean?
It is a turn where estimated input plus output tokens exceed the entered context-window limit. The application may need truncation, compaction, retrieval, or a model with a larger limit.
Why are all prices blank?
Models have different normal-input, cached-input, output, and long-context prices. Blank fields prevent example rates from appearing as current official prices. Enter the live rates for the exact model being planned.