Pricing and billing

Mouse has one price list for every customer. There are no seat fees, tiers, per-request minimums, or regional surcharges. Zero data retention and POST /v1/calibrations profile application are included at no extra charge.

Rate card

The only decision API with a cache discount — cached tokens at open-weight prices ($0.02 / 1M), with native probabilities, no regional surcharge, and no ZDR gate.

Token category	Price per 1M tokens	Notes
Input tokens (`state`, `instructions`, `criteria`, `framing`)	$0.10	`$0.0000001` per token (`$0.0001` per 1,000 tokens)
Cached input tokens (repeated prefix within ~5 minutes)	$0.02	80% discount (`$0.00000002` per token; no cache-write fee)
Output tokens	$0.00	Free—`mouse-1` scores options in one pass and never generates output tokens

Same price for everyone

Every customer pays the same published rate card:

No enterprise markup or tier gate: Zero Data Retention (Mouse-Data-Retention: none) and POST /v1/calibrations are included at list price (unlike Vercel's $0.10 / 1k ZDR surcharge or enterprise-only ZDR gates).
No regional surcharge: Currently served from US-East (Mouse-Region: us) at $0.10 / $0.02 (unlike OpenAI Decisions' +10% regional processing multiplier).
No cache-write fee: Writing into the prefix cache costs $0.00 extra.
Cost basis: We publish our H100 prefill cost basis once measured on the attached production checkpoint rather than asserting unverified figures.

Cache break-even against other decision APIs

With uncached input at $0.10 / 1M and cached input at $0.02 / 1M, your effective rate per 1M input tokens is:

[ \text{effective_rate}(f) = $0.10 - $0.08 \cdot f ]

where (f \in [0, 1]) is the fraction of billed input tokens served from cache (visible per day and per key in /console/usage).

Comparator	Published input rate	Cache discount	Cache hit fraction (f) needed for `mouse-1` to beat it
Cloudflare Clef (27B)	`$0.24 / 1M`	None	(f \ge 0%) (always cheaper)
OpenAI Decisions (`gpt-6-luna`)	`$0.10 / 1M` (+10% regional)	None	(f > 0%) (any cache hit beats it)
TypeSafe Jev (`jev-1.13.0`)	`$0.042 / 1M`	None	(f > 72.5%)
Microsoft Decision-1 (9B)	`$0.042 / 1M`	None	(f > 72.5%)
Cloudflare Clef-flash (9B)	`$0.038 / 1M`	None	(f > 77.5%)
Perplexity `pplx-decider` / Liquid `d1`	`$0.02 / 1M`	None (`$0.02` read)	Parity at (f = 100%)

Structure your `state` for cache hits

Rubric first, varying item last: Place shared instructions, tool schemas, or policy rubrics at the start of state and the per-request item at the end. JSON object key order is preserved exactly as sent over the wire.
Tenant-isolated, best-effort ~5-minute window: Cache entries live in ephemeral GPU memory, are scoped to your account, and typically remain warm for ~5 minutes under normal load (evicting earlier under memory pressure).
Inspect your own hit rate: The Console Usage page and CSV export break out input_tokens and cached_tokens per key so you can measure (f) directly on your workload.

Exact token accounting

Unlike providers that bundle hidden system prompts into input_tokens, Mouse publishes the exact token accounting formula used by apps/decide/mouse_api/billing.py and itemizes every component in the response's usage object:

[ \text{input_tokens} = \text{state_tokens} + \text{question_tokens} + \text{framing_tokens} + \text{image_tokens} ]

state_tokens: Tokens in your serialized state. Counted and billed once per request, whether you ask 1 question or 128 questions.
question_tokens: Sum of tokens across each question's instructions and rendered option descriptions (criteria).
framing_tokens: Structural delimiters around the request, questions, and options—no hidden system prompt: [ \text{framing_tokens} = 4 + 3 \times Q + 2 \times \sum_{q=1}^{Q} O_q ] where (Q) is the number of questions and (O_q) is the number of options in question (q) (2 for a noul question, len(criteria) for choice and score).
cached_tokens: When an identical state is evaluated again within the 5-minute (300 s) in-memory prefix cache window, cached_tokens equals state_tokens.
cost_usd: Computed to nanodollar precision ($0.000000001) using ROUND_HALF_UP: [ \text{cost_usd} = \frac{(\text{input_tokens} - \text{cached_tokens}) \times $0.10 + \text{cached_tokens} \times $0.02}{1{,}000{,}000} ]

Worked example: 1 question vs 5-question fan-out vs cached repeat

Suppose your agent state is 1,000 tokens, and each question (instructions + criteria + framing) is 40 tokens:

Scenario	`state_tokens`	`question_tokens + framing_tokens`	`cached_tokens`	Cost per call	Cost per 1,000 calls
1 question (cold state)	1,000	40	0	`$0.0001040`	$0.104
5 questions in 1 call (cold state)	1,000	200	0	`$0.0001200`	$0.120 (vs `$0.520` as 5 separate calls)
5 questions in 1 call (cached state within 5m)	1,000	200	1,000	`$0.0000400`	$0.040

Calibration billing

Calling POST /v1/calibrations bills the tokens of the labeled examples at the same $0.10 / 1M input ($0.02 / 1M cached) rate. Fitting 250 typical 100-token examples costs about $0.0025 (one quarter of a cent).
Passing a calibration profile token on POST /v1/systemone is free (0 extra tokens, $0 surcharge).
Refused requests (4xx validation, auth, or rate-limit errors) are never billed.

Prepaid credits and auto-reload

Verified accounts receive $5.00 in free starter credits at signup.
Spend draws from your prepaid Mouse credit balance (/console/billing).
When your balance reaches $0.00, API keys return 402 insufficient_credits within 30 seconds until you top up or auto-reload runs.
Usage settles asynchronously within ~60–120 seconds, so a high-RPS burst can briefly take a balance slightly below zero; the next top-up reconciles it.
Built-in spend cap: Optional auto-reload charges your saved payment method at most once per 24 hours, bounding worst-case spend from a leaked key to your current balance plus one reload amount per day.