Pricing as of October 8, 2025 — source: OpenAI Pricing.
TL;DR
- GPT-5 delivers higher-capability outputs (best for coding, complex agents) but costs significantly more per output token; GPT-5 mini is 4–5x cheaper and well suited to well-defined, repeatable tasks.
- Monthly cost formula: Monthly Cost ≈ (Monthly_Input_Tokens / 1,000) * Input_Price_$ + (Monthly_Output_Tokens / 1,000) * Output_Price_$. Use this to estimate spend for each workflow.
- When to choose which: use GPT-5 for agentic code/complex reasoning or when output quality materially affects downstream automation; choose GPT-5 mini for bulk summarization, classification, RAG retrieval, and high-volume customer-facing text where latency/cost matters.
- Quick win tactics: route high-volume predictable work to mini, cache inputs, truncate context, use extract-then-infer patterns, and batch requests.
Pricing Snapshot
| Model | Input price ($/1K tokens) | Output price ($/1K tokens) | Context window / max tokens | Link to source |
|---|---|---|---|---|
| GPT-5 | $0.00125 | $0.01000 | not published | OpenAI Pricing |
| GPT-5 mini | $0.00025 | $0.00200 | not published | OpenAI Pricing |
Cached input prices (reduce repeat prompt cost): GPT-5 cached input $0.000125/1K; GPT-5 mini cached input $0.000025/1K (see pricing page).
What this means: output tokens drive the bulk of cost for long generated responses — GPT-5’s output price is 5× GPT-5 mini’s, so workflows that produce large outputs see the largest delta.
What This Means in Practice
Enterprise workloads differ by how much text is read (input) vs. written (output), and by how often prompts repeat. High-volume summarization, classification, and RAG-style Q&A typically have predictable input sizes and benefit most from mini. Agentic tool-use, code generation, and workflows where single-response quality reduces downstream manual checks lean toward full GPT-5.
Examples:
- Summarization/classification: cheap on mini unless the summary requires complex reasoning across many documents.
- RAG Q&A: retrieval contexts increase input tokens — but outputs are usually moderate; mini often wins economically unless the model must synthesize novel logic.
- Agentic/tooling & code: higher failure cost from wrong code — invest in GPT-5 or hybrid routing for these.
3 Realistic Cost Scenarios (Mini vs. Full)
Formula reminder: Monthly Cost ≈ (Monthly_Input_Tokens / 1,000) * Input_Price + (Monthly_Output_Tokens / 1,000) * Output_Price
Scenario 1 — Invoice/PO processing & enrichment (NetSuite-centric)
Assumptions: 2,000 documents/day → 60,000/month; input ≈ 1,000 tokens/doc; output (extracted fields + enrichment) ≈ 200 tokens/doc.
Monthly input tokens = 60,000 × 1,000 = 60,000,000
Monthly output tokens = 60,000 × 200 = 12,000,000
GPT-5 cost: (60,000,000/1,000)*$0.00125 + (12,000,000/1,000)*$0.01000 = 60,000*$0.00125 + 12,000*$0.01 = $75 + $120 = $195/month
GPT-5 mini cost: (60,000,000/1,000)*$0.00025 + (12,000,000/1,000)*$0.00200 = 60,000*$0.00025 + 12,000*$0.002 = $15 + $24 = $39/month
Recommendation: Use GPT-5 mini with validation rules and an exception queue to NetSuite for items that fail heuristics.
Scenario 2 — Support triage & knowledge search (RAG chatbot, 50K queries/mo)
Assumptions: average input tokens per query (user prompt + retrieved context) = 800; output tokens ≈ 250.
Monthly input = 50,000 × 800 = 40,000,000
Monthly output = 50,000 × 250 = 12,500,000
GPT-5 cost: (40,000,000/1,000)*$0.00125 + (12,500,000/1,000)*$0.01000 = 40,000*$0.00125 + 12,500*$0.01 = $50 + $125 = $175/month
GPT-5 mini cost: 40,000*$0.00025 + 12,500*$0.002 = $10 + $25 = $35/month
Recommendation: Route first-pass RAG responses to GPT-5 mini; escalate to GPT-5 for unresolved or high-risk tickets.
Scenario 3 — Sales ops email drafting & QA (agent workflow at scale)
Assumptions: 20,000 emails/month; input (CRM + prompt) = 500 tokens/email; output (draft + variants) = 600 tokens/email.
Monthly input = 20,000 × 500 = 10,000,000
Monthly output = 20,000 × 600 = 12,000,000
GPT-5 cost: 10,000*$0.00125 + 12,000*$0.01 = $12.50 + $120 = $132.50/month
GPT-5 mini cost: 10,000*$0.00025 + 12,000*$0.002 = $2.50 + $24 = $26.50/month
Recommendation: Use GPT-5 mini for draft generation and GPT-5 for spot QA or agentic steps that produce code or complex logic (hybrid routing).
Choice Rubric
- Use GPT-5 when: output quality materially reduces manual review, tasks include code/tooling, or the model must perform multi-step reasoning that materially affects results.
- Use GPT-5 mini when: tasks are well-defined, high-volume, latency-sensitive, or when outputs are short and repetitive (summaries, metadata extraction, RAG answers).
- Hybrid: route bulk work to mini and escalate a percentage (A/B or confidence-threshold) to GPT-5.
8 Proven Ways to Cut API Spend
- Route by intent: cheap intents → mini; risky intents → GPT-5.
- Cache inputs & leverage cached-input pricing for repeat prompts.
- Truncate context and send only necessary fields (extract → infer pattern).
- Batch requests where possible to amortize overhead.
- Limit max output tokens or summarize before full generation.
- Use a confidence model: auto-accept low-risk outputs, escalate low-confidence to GPT-5 or humans.
- Profile token usage per endpoint and set dynamic routing rules.
- Monitor and alert on token spend per workload weekly; run monthly cost retrospectives.
Risks, Assumptions, and Governance
Pricing as of October 8, 2025: OpenAI Pricing. Context window / max tokens: not published. Numbers above use the provided per-1M token prices and convert to $/1K for clarity. Assumptions about tokens per item are estimates — run pilot measurements on real payloads.
Governance notes: run A/B tests, keep PII out of prompts when possible, and include logging & retention policies for prompts/responses aligned to compliance requirements.
CTA
Want a cost model for your stack (NetSuite, RAG, agents)? CFCX Work can benchmark your workloads, run token-profile pilots, and design a mini-first routing strategy. Contact us to get a tailored cost/accuracy plan.
References
- OpenAI API Pricing (pricing as of October 8, 2025)