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Prompt Optimization Calculator
October 2026 8 min read
Free prompt optimization calculator with 2026 model rates for OpenAI, Anthropic, Google Gemini, DeepSeek, Mistral and xAI. Measure token reduction, dollars saved and an efficiency score that keeps quality honest. No sign-up.
- Reduction
- 40.0%
- 1,200 tok/run saved
- Saved / mo
- $5.76
- 20,000 runs/mo
Optimization at a glance
Saved per run
$0.000288
1,200 tok
Saved per year
$69.12
Efficiency score
58 / 100
C · Needs work
Quality held at
85 / 100
Your prompts — results update live
Original tok / run
Optimized tok / run
Runs / mo
Quality score / 100
Model (dollar savings)
Live result
- Saved / mo
- $5.76
- Reduction
- 40.0%
- Saved / yr
- $69.12
- Score
- 58 / 100 (C · Needs work)
Input share78% in / 22% out
Model & provider comparison for this workload
| Provider · Model | Input $/1M | Output $/1M | Monthly | Note |
|---|---|---|---|---|
| OpenAI · text-embedding-3-smallCHEAPEST | $0.020 | $0.000 | $0.7200 | Embeddings — input only. |
| Google · Gemini 1.5 Flash | $0.075 | $0.300 | $5.70 | Cheapest big-lab model. |
| OpenAI · GPT-4o mini | $0.150 | $0.600 | $11.40 | Budget default for most tasks. |
| Meta (self-host) · Llama 3.1 70B* | $0.350 | $0.400 | $16.60 | *Hosting cost via Together/Fireworks ≈. |
| Anthropic · Claude 3 Haiku | $0.250 | $1.250 | $21.50 | Fast + cheap Anthropic tier. |
| DeepSeek · DeepSeek V3 | $0.500 | $2.000 | $38.00 | Open-weight value leader. |
| DeepSeek · DeepSeek R1 | $0.550 | $2.190 | $41.70 | Reasoning model, MoE efficient. |
| Google · Gemini 1.5 Pro | $1.250 | $5.000 | $95.00 | Huge context, cache discounts. |
| Mistral · Mistral Large | $2.000 | $6.000 | $132.00 | EU-hosted flagship. |
| xAI · Grok 2 | $2.000 | $10.000 | $172.00 | X-integrated flagship. |
| OpenAI · GPT-4o | $2.500 | $10.000 | $190.00 | Flagship multimodal default. |
| Anthropic · Claude 3.5 Sonnet | $3.000 | $15.000 | $258.00 | Best reasoning / coding mix. |
Same workload priced on every model — only per-token rates change. Output billed higher everywhere. Prices: Oct 2026 public API tiers; batch (−50%) and cache (−50–75%) not applied.
Cost-optimization checklist
Tick off what already ships — this page scores 1/6 (17%).
Learn — AI costs, deeply
An AI bill is requests × tokens × rate × rounds. Providers publish per-token prices, but your architecture decides the multipliers: which tier answers, how much context you send, whether prefixes hit cache, whether async work uses batch, and how many tool-call rounds each task needs. Master those five levers and most teams cut 50–90% without changing what users see.
1 · How per-token pricing really works (and the formula)
cost/request = (input tok ÷ 1M × $in) + (output tok ÷ 1M × $out)
monthly = cost/request × requests/day × 30 × rounds-multiplier
blended $/1M = in% × $in + out% × $out · batch ≈ ×0.5 · cached input ≈ ×0.25–0.5
monthly = cost/request × requests/day × 30 × rounds-multiplier
blended $/1M = in% × $in + out% × $out · batch ≈ ×0.5 · cached input ≈ ×0.25–0.5
Tier first
25–100× range
GPT-4o ($10/1M out) vs mini ($0.60/1M out) on identical tokens. Routing beats haggling.
Outputs next
2–5× input
Capped completions, structured outputs, and fewer agent rounds cut the dearest tokens.
Volume last
linear × discounts
Cache, batch, and annual commits bend the curve once architecture is lean.
Honest limits: public rates move quarterly and hide add-ons (throughput units, private endpoints, support). Treat calculator outputs as planning estimates — reconcile monthly against provider invoices and own logs.
2 · Token math — count before you pay
| Unit | ≈ Tokens | Rule of thumb |
|---|---|---|
| 1,000 words (English) | ~1,330 | words × 1.33 |
| 4,000 characters | ~1,000 | chars ÷ 4 |
| 1024×1024 image | ~250–1,000 | rises with detail / tiling |
| 1 min audio (STT) | ~3–6K* | *often billed per-minute instead |
| Chat turn (support) | ~1.5–3K | history + system + answer |
Measure your own p50/p95 per endpoint for a week — averages hide the long-tail threads that dominate spend. Code and non-English text tokenize denser; always sample real traffic, not docs examples.
3 · Factor-impact table — what moves the bill most
| Factor | Typical impact | Why |
|---|---|---|
| Model tier choice | Very high | Flagship vs mini/flash is a 25–100× swing on identical tokens. Route by difficulty. |
| Output token volume | Very high | Output costs 2–5× input and compounds with agents (3–5 rounds per task). Cap max_tokens. |
| Prompt bloat | High | Stuffed context is billed every call. Retrieve (RAG) instead of pasting whole docs. |
| Cache hit rate | High | Cached system prompts/docs bill at 25–50%. Stable prefixes = free money. |
| Batch vs real-time | High | Batch APIs cost ~50% for async work (ETL, evals, moderation, backfills). |
| Tool-call loops | Medium–high | Each agent round re-sends history. Compact transcripts + stop conditions. |
| Multimodal inputs | Medium–high | One image ≈ 250–1,000 tokens; a minute of audio ≈ 3–6K tokens. |
| Retries / errors | Medium | Partial generations still bill. Timeouts + idempotency keys stop double-spend. |
| Over-provisioned context | Medium | 1M-context calls tempt 10× prompts. Only send what the task needs. |
| Region / egress | Low | Minor vs token cost, but private endpoints + data-residency add fixed fees. |
| Fine-tune hosting | Low–medium | Inference billed like base model; savings come from shorter prompts, not cheaper tokens. |
| Self-host fixed cost | Lever | GPU $/hr beats API $/token only past break-even volume — model it before migrating. |
Cache + batch playbook (fastest payback)
- • Freeze stable prefixes (system prompt, tools schema, top docs) so cache hits.
- • Route evals, moderation, backfills and nightly ETL to Batch API (−50%).
- • Log cache-hit % weekly; a 60%+ hit rate halves effective input price.
- • Keep temperature/format stable — churned phrasing busts cache silently.
GPU-vs-API break-even playbook
- • GPU monthly ≈ $/hr × 730 × utilization. API monthly ≈ tokens × blended rate.
- • Example: RTX 4090 at $0.49/hr ≈ $358/mo — beats GPT-4o mini only past ~600M tok/mo.
- • Add engineering cost: drivers, scaling, evals, on-call. APIs bundle that.
- • Re-run quarterly — rate cuts keep moving the break-even point upward.
4 · TCO: the bill beyond tokens (4-week measurement protocol)
- Week 1 — instrument: log input/output tokens, model, cache-hit, latency per request.
- Week 2 — change one lever (tier router, cache, batch, or caps) on a canary slice.
- Score = (new − old) ÷ old on cost/request, quality (evals), and p95 latency.
- Add people + infra: TCO = API + GPU/hosting + vector DB + evals + support deflection value.
- Hold a control slice — provider-side changes move everyone at once; diff against it.
5 · RAG vs fine-tuning, honestly priced
- RAG pay-as-you-go — embeddings (~$0.02/1M) + vector DB ($20–200/mo) + retrieved tokens per query. Wins when knowledge changes or needs citations.
- Fine-tuning upfront — training tokens billed once (e.g. mini ≈ $3/1M) + eval + shorter prompts forever. Wins for stable style/format/reasoning.
- Combine them — fine-tune the format, retrieve the facts. Most production systems do both.
- YMYL note — calculators here are planning estimates, not procurement or legal advice; validate against contracts.
6 · Benchmarks & standards we cite
| Workload | Typical $/mo | Tokens |
|---|---|---|
| Side-project chatbotGPT-4o mini / Haiku tier. | $5–$25 | ~5M / mo |
| Growing SaaS copilotMixed mini + flagship routing. | $150–$800 | ~100M / mo |
| Support automation (10K chats)Model choice = 10× swing. | $120–$1,900 | ~250M / mo |
| Doc-processing pipelineBatch API halves this. | $60–$400 | ~200M / mo |
| Enterprise RAG rolloutCache + self-host evaluated. | $2K–$25K | ~2B / mo |
- Provider pricing pages (OpenAI, Anthropic, Google AI, DeepSeek, Mistral, xAI) — per-token source of truth
- NIST AI Risk Management Framework (AI 100-1) — cost-aware deployment & monitoring
- ISO/IEC 42001:2023 — AI management system (budget, roles, measurement)
- Schema.org WebApplication + FAQPage + HowTo — structured data used on these pages
- WCAG 2.2 AA — accessible calculator UX (labels, contrast, keyboard)
Frequently Asked Questions
10 answers covering pricing, tokens, models, caching, batch, RAG, GPUs and budgeting — each targets an exact search phrase.
How is prompt optimization measured?
Enter original and optimized token counts plus monthly runs. Reduction percent equals saved divided by original times 100, and dollars saved multiply tokens saved by your model blended rate. Quality score keeps the optimization honest so shorter never means worse.
What is a good prompt efficiency score?
Above 80 is strong: big reduction with quality held above 85. Between 60 and 80 is workable but shows either weak reduction or quality cost. Below 60 means the rewrite needs another pass before shipping.
How much can prompt optimization save?
Teams commonly cut 30 to 60 percent of prompt tokens via dedup, retrieval instead of pasting, and structured outputs. On 20K runs per month at 3000 tokens each, a 40 percent cut on mini tier saves hundreds of dollars monthly. This page sizes your exact saving.
Does compression hurt answer quality?
It can if pushed blindly, which is why this calculator pairs reduction with your quality score. Compress system instructions and examples first, keep task critical constraints verbatim, and eval before and after on 50 plus real queries.
What is prompt caching versus compression?
Compression shrinks tokens permanently, while caching discounts repeated prefixes without changing them. Use both: compress once for the floor, then cache the stable remainder for 50 to 75 percent off input. The budget pages model each lever.
How do few shot examples affect cost?
Each example adds its tokens to every call, so 5 examples at 150 tokens cost 750 tokens per request before the real prompt. Prune to the fewest examples that hold quality, or retrieve one dynamic example instead of five static ones.
How do I budget prompt experiments?
Multiply runs per variant by tokens per run by model rate, times days of testing. Cap max tokens, test on mini tier first, and batch offline evals at half price. The testing and iteration pages on this site model experiment budgets.
When should I stop optimizing?
Stop when the next hour of prompt work saves less than an hour of API spend at your volume, or when quality dips below your floor. Diminishing returns usually start near 50 percent reduction for chat prompts.
Can I compare models here?
Yes. The model dropdown and comparison table reprice your optimized prompt on all 12 models, so you see whether a cheaper tier beats further compression work. Often switching tiers saves more than another rewrite.
Is this calculator free?
Free forever with no sign up. It runs 100 percent in your browser. Copy results, export CSV or print for review docs.
Long-tail questions this page answers
Each phrase below maps to a section or FAQ further down — written to match the exact phrasing searchers type.
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