10 Ways to Cut Your GPT-6 API Bill

GPT-6's top tier costs $50 per million output tokens — 100× as much as the budget tier. Many teams overspend simply because they never optimized how they call the API. Here are ten concrete techniques, with real numbers — model each one in the calculator to see your own saving.

1. Pick the cheapest tier that does the job

This is the single biggest lever. GPT-6 ships in three tiers:

Many teams benchmark classification, extraction, and summarization on Luna first — at 95% lower per-token cost than Sol, it's the cheapest tier to test. Moving a workload from Sol to Luna cuts the bill by 95%. Always benchmark the cheap tier first — you can read our full Astra vs Sol vs Luna guide for a decision framework.

2. Use prompt caching aggressively

Cached input tokens are billed at 0.1× the standard input rate (e.g. $0.01 vs $0.10 per 1M on Luna). If your app reuses the same system prompt or document context across requests, caching bills the cached share at 0.1× the standard input rate ($0.01 vs $0.10 per 1M on Luna). Structure prompts so the stable prefix comes first — that's the part that gets cached.

3. Trim output tokens

Output tokens cost 5× as much as input tokens on every tier. Set tight max_tokens limits, ask for concise formats ("reply in bullet points, max 50 words"), and prefer structured outputs over prose. A support bot that answers in 80 tokens instead of 300 saves ~73% on the output side.

4. Use the Batch API for non-urgent work

OpenAI's Batch API is officially documented at 0.5× (50% off) for workloads that can tolerate up to 24-hour turnaround. Backfills, evaluations, dataset labeling, and nightly reports are good candidates. Check the official pricing page for current batch discounts.

5. Shorten your system prompts

That 2,000-token system prompt gets billed on every single request. Audit it quarterly: remove redundant instructions, replace few-shot examples with one crisp example, and move static reference material into cached context or retrieval instead of the prompt.

6. Don't send the whole conversation history

Multi-turn apps often resend the entire history each turn, so token usage grows quadratically. Summarize older turns, use a sliding window of the last N messages, or store long-term memory in a vector DB and retrieve only what's relevant.

7. Route simple requests to Luna, hard ones to Sol

You don't need one tier for everything. Use a cheap classifier (even a regex or a Luna call) to route: simple lookups → Luna, complex reasoning → Sol. This hybrid pattern can land well below an all-Sol bill — plug your own routing split into the calculator.

8. Deduplicate and cache responses

If users ask the same questions repeatedly, cache the answers. Even a simple exact-match cache on your most common 100 queries can eliminate 10–30% of API calls for FAQ-style workloads.

9. Set usage limits and alerts

Not a per-token saving, but the cheapest incident is the one that never happens. Set per-key monthly budgets and alert thresholds in the OpenAI dashboard. One runaway loop without a max_tokens cap can burn more than a month of normal usage.

10. Measure before and after

You can't optimize what you don't measure. Plug your per-request token counts into our free GPT-6 cost calculator to model each technique above — tweak the tier, toggle caching, adjust output length, and see the monthly number update as you change inputs.

The bottom line

Combining tier right-sizing, caching, and output trimming moves the bill substantially — the calculator shows the exact figure for your workload with just three moves: right-size the tier, enable caching, and trim outputs. Start with the calculator, model your workload, and work down the list.