Open formula. Browser-based calculations. Source-dated rates. No API keys. No provider login. No tracing SDK. No account required. Input payloads are not stored.
Pricing source date: 2026-05-28 · Build verification: 2026-05-28 · Stale threshold: 30 days. This is a context-window planning tool; results depend on the model's current context limit, prompt overhead, retrieved or pasted content, and output reserve, not on provider price rows. Confirm the context limit for the target model version before committing to a prompt, RAG, or summarization design.
Tests whether a planned document, system/prompt overhead, and output reserve fit inside a target context window.
Formula
total_required_tokens = doc_tokens + overhead_tokens + expected_output_tokens; headroom = model_context_window - total_required_tokens
Primary source register
Context windows are model-specific and can differ between API versions. Confirm the current model limit with the provider before production rollout.
- https://openai.com/pricing
- https://www.anthropic.com/pricing
- https://platform.claude.com/docs/en/about-claude/pricing
- https://platform.claude.com/docs/en/about-claude/models/overview
- https://cloud.google.com/vertex-ai/generative-ai/pricing
- https://www.together.ai/pricing
- https://replicate.com/pricing
- https://fireworks.ai/pricing
- https://groq.com/pricing
- https://deepinfra.com/pricing
- https://aws.amazon.com/bedrock/pricing/
- https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/
Included assumptions
- document tokens
- system/history overhead
- reserved output tokens
- target model context size
Excluded assumptions
- provider-specific hidden prompt overhead
- tokenizer drift
- latency impact of long-context requests
Architecture-cost audit
Context growthDocument tokens, system overhead, output reserve, and cumulative context requirements.
Diagnostic output
This tool returns Cost classification, Dominant cost driver, Decision threshold, and Sensitivity. Diagnostic focus: Context-fit threshold and whether RAG/summarization/chunking is required..
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