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RAG vs Long-Context Calculator

Compare a retrieval-heavy RAG workflow against a long-context alternative before changing architecture, model mix, or rollout assumptions in production. Long-context models make this an architecture decision, not just a prompt-size question.

How this tool works

This calculator runs the deterministic economics model twice: once for a baseline RAG workflow and once for a candidate long-context design that removes retrieval, reranking, vector lookups, and embedding refresh so you can see when larger windows actually beat the retrieval stack.

How It Works

  1. Set the current RAG baseline model and the candidate long-context model.
  2. Keep shared workload assumptions explicit, then set long-context prompt size.
  3. Compare cost, margin, break-even price, and retrieval-stack deltas before changing architecture.

Formula

cost_delta = cost_long_context - cost_rag

request_input_tokens_delta = long_context_input_tokens - (base_prompt_tokens + retrieved_chunks * tokens_per_chunk)

Assumptions and Units

  • Currency: USD
  • Token unit: token
  • Long-context candidate removes retrieval, reranking, vector-query, and embedding-refresh terms
  • Pricing source: daily snapshot in repo, no runtime scraping

Example Scenario

If a deep-research workflow currently uses wide retrieval but a long-context prompt could replace the stack, compare whether the extra prompt tokens are cheaper than retrieval, reranking, and monthly refresh overhead.

Related resources: AI Workflow Cost Calculator, AI Break-even Price Calculator, LLM Model Cost Comparison, RAG Retrieval Cost Calculator, Reranking Cost Calculator, Cache Savings Simulator, Context Window Cost Calculator, Embedding Ingestion Cost Calculator, Deep Research Cost per Seat, RAG vs Long-Context: Cost Tradeoffs, Context Bloat in RAG, How Many Tokens Per Request?, What Is RAG?.

Pricing snapshot: 2026-03-15Baseline: OpenAI / GPT-5 MiniCandidate: OpenAI / GPT-5.2

Step 1 Baseline and Candidate Models

Set the current RAG model as baseline and the long-context alternative as candidate.

Step 2 Quick Mode

Set shared workload assumptions, then size the long-context prompt.

Compare wide docs retrieval against a larger single prompt before changing search architecture.

Step 3 Advanced Assumptions

Tune the baseline retrieval stack and candidate cache/infra only after Quick Mode is close.
Show advanced inputs

Scenario Share URL

Share this link to load these exact assumptions. Pricing uses the published snapshot shown on the page.

Paste into ChatGPT or Claude to discuss this scenario.

Decision Signal

Long-context path lowers modeled cost

Switching to GPT-5.2 and removing the retrieval stack changes cost per user by -$0.2397 and margin by +0.8%.

RAG request input tokens

2,220

Long-context prompt tokens

2,600

Retrieval-stack delta

-$0.9807

Monthly cost impact

-$191.73

Top Cost Drivers

Baseline sensitivity: cost change when one variable is increased by 10%.
Requests Per User Month10.0%
Rerank Docs9.2%
Cache Hit Rate-6.1%

Totals

Baseline vs candidate totals under the same task assumptions.
MetricRAG baselineLong contextDelta
Cost per request$0.0108$0.00681-$0.00399
Cost per user / month$0.648$0.4084-$0.2397
Gross margin %97.8%98.6%+0.8%
Break-even price$0.648$0.4084-$0.2397
Monthly gross profit impactAt 800 active users+$191.73

Component Breakdown

Each component is computed independently, then compared across both architectures.
ComponentRAG baselineLong contextDelta
GenerationModel input/output token spend for requests.$0.0435$0.483+$0.4395
RetrievalExtra model input spend from retrieved context chunks.$0.0198$0-$0.0198
RerankingReranker cost based on docs scored per request.$0.96$0-$0.96
Embeddings IngestionAmortized per-user share of the fixed monthly corpus embedding refresh cost.$0$0-$0
Vector DbVector database query cost across all requests.$0.0009$0-$0.0009
CacheSavings from cache hits. Negative means lower total cost.$-0.3972$-0.0896+$0.3075
InfraNon-model infra overhead per request.$0.021$0.015-$0.006
Sensitivity RankingBaseline sensitivity: cost change when one variable is increased by 10%.
VariableDelta cost %
Requests Per User MonthUser activity level per month.10.0%
Rerank DocsDocs reranked per request.9.2%
Cache Hit RateFraction of requests served by cache.-6.1%
Output TokensGenerated tokens per request.0.3%
Retrieved ChunksRetrieved chunk count per request.0.2%
Tokens Per ChunkAverage chunk size in tokens.0.2%
Input TokensPrompt-side tokens per request.0.1%
Vector Queries Per RequestVector query count per request.0.0%
Monthly Active UsersActive-user estimate used to amortize fixed monthly embedding refresh.-0.0%

Assumptions and Units

Explicit assumptions to keep architecture comparisons reproducible and auditable.
  • CurrencyUSD
  • Token unittoken
  • Pricing snapshot2026-03-15
  • Baseline modelOpenAI / GPT-5 Mini
  • Candidate modelOpenAI / GPT-5.2
  • Monthly users800 active users for fixed-term amortization and business totals
  • Fixed monthly termEmbedding refresh is treated as a fixed monthly cost in business totals

Recommended Next Step

Use this section to turn the architecture comparison into the next implementation checks.

If the long-context path still looks viable after quality and latency checks, review infra constraints next.

Architecture follow-up

RAG vs Long-Context: Cost TradeoffsContext Window Cost Calculator

Compare infra providers

View Infra Recommendations

Sources and Snapshot

Pricing comes from a daily snapshot generated by batch workflows.

Active Pricing Rows

RAG baseline

OpenAI / GPT-5 Mini

  • Input tokens$0.25 / 1M
  • Output tokens$2 / 1M

Long context

OpenAI / GPT-5.2

  • Input tokens$1.75 / 1M
  • Output tokens$14 / 1M

Shared retrieval defaults

  • Embedding input$0.02 / 1M
  • Rerank docs$1 / 1K

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