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ManticScore uses a credit system to meter AI-powered operations. Each time you run a research job, start a Forge session, or send a chat message, a small number of credits is deducted from your balance.

Credit costs

These weights are documented here as the canonical source. Clients should treat the table above as static — the previous credits.weights field on GET /v1/usage has been removed in favor of a fixed shape that matches the iOS UsageDTO.

Tier allowances

Each tier sets caps on questions, text-to-speech minutes, and credits per monthly period. Periods roll over at the first of each calendar month UTC. The exact rollover timestamp is returned as period.resets_at on GET /v1/usage.
An explicit non-zero credits_total on the user’s profile (for example, an early-access lifetime grant) overrides the tier’s credits cap. Question and TTS caps always come from the tier table.

Checking your usage

Your current credit usage is included in your profile and bootstrap responses, and is also available as a dedicated GET /v1/usage endpoint that returns the current period, tier with caps, used counters, and remaining allowance in one shot. See Billing and usage for the full response schema.
When used.credits equals tier.caps.credits (or credits_used equals credits_total on the profile), the next AI operation that requires credits returns 429 Too Many Requests. Free-tier accounts recover at the start of the next monthly period.
The questions and tts_seconds counters on GET /v1/usage currently return 0 until the usage aggregator is wired. The caps and remaining math are still authoritative — clients can rely on the shape today and pick up real counts when the aggregator ships.

Rate limits

Credits and rate limits are separate mechanisms. Rate limits cap how many requests you can send per minute, regardless of your credit balance. When a rate limit is hit, the response includes a Retry-After header indicating when you can try again.
Rate-limited research requests with a valid session token may be captured to a retry queue rather than dropped entirely. Each account can have up to 5 pending queued requests.

Research quality stats

You can view aggregate quality scores across all completed research to understand how well the AI is performing on your ideas.
Quality scores range from 0 to 100 and reflect factors like competitor coverage, feature depth, signal quality, and interpretation specificity.