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Prompt A/B Testing Calculator
October 2026 8 min read
Free prompt a/b testing calculator with 2026 per-token pricing for OpenAI, Anthropic, Google Gemini, DeepSeek, Mistral and xAI. Estimate cost per request, monthly and annual bills, cache and batch savings and per-user costs. No sign-up.
- Monthly bill
- $69.66
- GPT-4o mini
- Per request
- $0.000464
- 2,500 tok
- Per user / mo
- $0.0348
- Saves
- $20.34
- 23% off raw
This workload at a glance
Daily cost
$2.32
5,000 req/day
Annual projection
$847.53
Per 1K requests
$0.4644
Budget grade
A+ · Comfortable
Well under budget — consider reinvesting headroom in quality (better model, evals).
Usage plans — pick your scale
Plans load realistic request/token presets so costs start near your situation. Fine-tune any input afterwards — results update live.
Your workload — results update live
Model
Requests / day
Tool-call rounds
Input tok / req
Output tok / req
Prompt-cache hit rate: 40%
Batch API share: 20%
Monthly users
Monthly budget ($)
Live estimate
- Monthly
- $69.66
- Annual
- $847.53
- Per request
- $0.000464
- Per user / mo
- $0.0348
- Cache + batch saves
- $20.34 (23%)
Input share80% in / 20% out
Model & provider comparison for this workload
| Provider · Model | Input $/1M | Output $/1M | Monthly | Note |
|---|---|---|---|---|
| OpenAI · text-embedding-3-smallCHEAPEST | $0.020 | $0.000 | $3.89 | Embeddings — input only. |
| Google · Gemini 1.5 Flash | $0.075 | $0.300 | $34.83 | Cheapest big-lab model. |
| OpenAI · GPT-4o mini | $0.150 | $0.600 | $69.66 | Budget default for most tasks. |
| Meta (self-host) · Llama 3.1 70B* | $0.350 | $0.400 | $95.04 | *Hosting cost via Together/Fireworks ≈. |
| Anthropic · Claude 3 Haiku | $0.250 | $1.250 | $132.97 | Fast + cheap Anthropic tier. |
| DeepSeek · DeepSeek V3 | $0.500 | $2.000 | $232.20 | Open-weight value leader. |
| DeepSeek · DeepSeek R1 | $0.550 | $2.190 | $254.75 | Reasoning model, MoE efficient. |
| Google · Gemini 1.5 Pro | $1.250 | $5.000 | $580.50 | Huge context, cache discounts. |
| Mistral · Mistral Large | $2.000 | $6.000 | $793.80 | EU-hosted flagship. |
| xAI · Grok 2 | $2.000 | $10.000 | $1,063.8 | X-integrated flagship. |
| OpenAI · GPT-4o | $2.500 | $10.000 | $1,161 | Flagship multimodal default. |
| Anthropic · Claude 3.5 Sonnet | $3.000 | $15.000 | $1,595.7 | 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 much does prompt a/b testing cost per month?
Most teams land between 5 and 800 dollars per month depending on requests per day, tokens per request and model tier. A prototype on GPT-4o mini or Gemini Flash at 200 requests per day often costs under 25 dollars per month, while the same traffic on a flagship model costs 10 to 20 times more. Enter your own requests per day and tokens above for an exact number.
How is prompt a/b testing calculated?
Cost per request equals input tokens divided by 1M times input price plus output tokens divided by 1M times output price, times tool call rounds. Monthly cost equals cost per request times requests per day times 30, with cache discounts on the cached input share and 50 percent off the batch share. Every figure on this page shows its inputs so finance can audit the math.
Which model is cheapest for this workload in 2026?
Gemini 1.5 Flash is usually cheapest per million tokens, followed by GPT-4o mini and Claude 3 Haiku, with DeepSeek V3 leading open weight value. Flagship tiers cost 25 to 100 times more on identical tokens, so routing simple queries down is the biggest lever. The comparison table on this page reprices your exact workload on all 12 models live.
How do caching and batch change this estimate?
Cached input tokens bill at about 30 percent of standard input price, so a 40 percent cache hit rate cuts roughly 28 percent off the input portion. Batch API halves the cost of the async share for evals, backfills and nightly jobs. Use the sliders above to model your blended bill.
What is a good per user cost benchmark?
Divide monthly cost by monthly active users. Healthy AI SaaS pricing needs price per user at 3 to 4 times cost per user, so 25 cents of cost supports a 1 to 2 dollar add on or must sit inside a 9 dollar seat. The results panel computes per user cost from your monthly users input.
How do I forecast this cost as usage grows 10 times?
Costs scale linearly with users times requests times tokens times rounds, but per user cost should fall via caching, tier routing and batch. Model three scenarios: linear with no optimization, optimized at minus 50 percent via cache plus batch, and tier routed at minus 80 percent with a mini default. Budget the linear case and engineer toward the routed case.
What hidden costs should I add on top?
Add evals and red teaming, retries on partial generations, multimodal token multipliers, vector database and embedding spend, and human review queues. A 20 to 30 percent buffer over raw token math covers most teams. The education section lists the full TCO protocol.
How often should I recheck pricing?
Quarterly. Per token prices fell 10 to 100 times over 2023 to 2025 and keep dropping as smaller models and better hardware ship. Re run the provider table each quarter because last year too expensive flagship may be this year default.
Can I compare providers on this page?
Yes. The model dropdown includes 12 models across OpenAI, Anthropic, Google, DeepSeek, Mistral, xAI and hosted Llama, and the comparison table reprices your exact workload on all of them. For deeper dives see our provider comparison calculators.
Is this calculator free and private?
Free forever with no sign up. It runs 100 percent in your browser so your numbers never leave the page. Copy results, export CSV or print for your budget document.
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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