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Modaic System One Inference API
Modaic System One Inference API is a paid API for AI agents from agents.modaic.dev, paid per call via MPP, price unknown, status healthy (last checked 2026-11-04, last successful call 2026-10-29).
Runs typed inference on a JSON state with one or more closed questions (boolean probability, choice, or score) and returns calibrated answers with probabilities and confidence scores
Price / call—
Success100%
StatusHealthy
How this service works
Runs inference on a JSON state with one or more typed questions and returns one answer per question, with probabilities and a calibrated confidence, plus the token usage of the call. Pick the model in the body: mo-fast for high-throughput decisions over text and images in 100 to 500 ms, with optional reasoning_effort for harder calls, or mo for tougher judgments, with confidence scores that better reflect how often it is right. Both read a 262,144 token (256K) context window and up to four images per call. Three question types: noul returns the probability that a condition holds, choice picks one of the options you define and returns a probability for each, and score places the state on an ordered, zero-indexed rubric and returns a fractional score with the legend and per-level probabilities. Question IDs are yours and come back unchanged as answer keys. Repository models (workspace/model paths) and their fields (revision, example_id, capture) belong to Modaic accounts and are not served here. Send an Idempotency-Key header (8 to 255 characters) to have a retry replay the completed response; without it every call is a new request. Send your own UUID in x-request-id to correlate a call with your logs; every response carries one. Errors use application/problem+json with a stable code: 422 validation_error for a bad body (an empty choice criteria included), 422 questions_required when questions are missing, 422 invalid_image for more than four images or bytes that are not an image, 404 model_not_found for an unknown model, 409 idempotency_key_reused for a reused key with a different body, 429 when rate limited, 503 model_in_development for a model that is not serving yet. A failed call is not charged. Input tokens are metered on every call and reasoning tokens when reasoning is on; output tokens are free. Each call is one request.
Output
A JSON object with an 'answers' map keyed by your question IDs, each containing typed results: for noul questions a probability from 0–1; for choice questions the selected key plus per-option probabilities and a calibrated confidence; for score questions a fractional score, the rubric legend, per-level probabilities, and confidence. Also includes model name and token usage counts (input, output, and optional reasoning tokens).
Example use cases
Support ticket intent triage
“Take this customer message — 'I was charged twice this month and I want a refund immediately' — and use mo-fast to classify their primary intent as either refund, cancel, or billing-question, and score how urgently a human agent should respond on a scale from not urgent to critical.”
Content moderation for listings
“Check this product listing description — 'Authentic designer handbag, barely used' — using mo to tell me the probability it contains counterfeit or misleading claims, and classify its quality tier as poor, acceptable, or high-quality.”
Agent output quality evaluation
“I need to evaluate an AI-generated response to a customer — here's the response text — using mo to score it on a helpfulness rubric from 0 (unhelpful) to 3 (excellent) and tell me the probability it contains any harmful content.”
Technical details
Endpoint
POSThttps://agents.modaic.dev/api/v1/systemone?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need fast, calibrated, typed decisions (boolean probability, multi-class choice, or rubric score) over text or image inputs rather than free-text generation. Prefer mo-fast for high-throughput pipelines needing sub-500ms latency, and mo when calibration accuracy matters more than speed. Ideal for agent pipelines, triage systems, moderation, and evaluation tasks where structured answers with confidence scores are more useful than open-ended LLM output.
Side effects
- 422 validation_error — malformed request body or empty choice criteria
- 422 questions_required — no questions provided
- 422 invalid_image — more than 4 images or non-image bytes submitted
- 413 — request body exceeds 10 MB limit
- 401/403 — missing or invalid authentication credentials
- timeout — complex reasoning_effort calls may exceed latency budget
- repository/workspace model paths not served at this endpoint
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| modelrequired | string | The model to run: mo-fast or mo, also accepted as modaic/mo-fast and modaic/mo; names are case-insensitive. mo-fast is currently mo-fast-1.2.1 and the only model with reasoning; mo is the stronger model for harder judgments. Also accepted, with or without the modaic/ prefix: mo-latest, mo-1.3.1, mo-fast-latest, mo-fast-1.2.1, mo-1.3.0, mo-fast-1.2, mo-fast-1.2.2. mo-1.1, mo-fast-1.1, mo-1.2.0, mo-fast-1.2.0 name deprecated models that still work but are slow and have no reasoning. Repository paths such as acme/support-triage belong to Modaic accounts and are not served here. |
| staterequired | object | Required (null is allowed): any JSON value, the state to evaluate. Images go anywhere in the state as OpenAI-compatible blocks, {"type":"image_url","image_url":{"url":...}}, with an https URL or an inline data:image/...;base64 URL. mo and mo-fast read up to four images (PNG, JPEG, GIF or WebP, 20 MB each) per call; more than four, or bytes that are not an image, return 422 invalid_image. Inline base64 images are moved to Modaic object storage and replaced with stable https URLs before the state is stored or sent on. Request bodies over 10 MB are refused, so send large images as https URLs. |
| assets | object | |
| capture | boolean | Repository models only. Set false to run the model without recording the example or decision. |
| revision | string | For repository models only: a branch name, tag, or commit sha to resolve the manifest from. Defaults to the repository's default branch. Pin a commit sha for fully reproducible decisions. |
| questionsrequired | object | Required for the base models served here: at least one question, keyed by IDs you choose (any string, "" included) that come back unchanged as answer keys. instructions is optional on every type and takes any JSON value, null included; criteria descriptions may be strings, objects or arrays with nested JSON. noul: criteria may be omitted or null, criteria.true and criteria.false are optional descriptions and may be null; the answer's noul is the probability of true. choice: criteria maps non-empty option keys to a description or null, at least one option (an empty object is 422 validation_error); the answer carries the selected key, a probability per key and confidence. score: criteria is an ordered array of one or more rubric descriptions, none null, zero-indexed (five levels span 0 to 4); the score may be fractional; the answer carries score, legend, probabilities and confidence. |
| example_id | string | Repository models only. Groups this decision under an example you identify. Defaults to a hash of `state`, so identical inputs share one example. |
| idempotency_key | string | |
| reasoning_effort | string | Only mo-fast (currently mo-fast-1.2.1 and its aliases); other models reject it. Omit for no reasoning. Maximum thinking tokens per question: low 512, medium 1,024, high 2,048, xhigh no explicit cap (bounded by the remaining context and the request timeout). You pay for the tokens generated, not the budget: they are reported in usage.reasoning_tokens, included in usage.output_tokens, and metered here as reasoning tokens. |
Output
Schema · Response
| Field | Type | Description |
|---|---|---|
| modelrequired | string | The model name as supplied in the request. |
| usagerequired | object | Integer token counts: input_tokens and output_tokens, plus reasoning_tokens when reasoning was on. output_tokens already includes the generated thinking, any natural closing token and one decision token per question, so do not add reasoning_tokens to it. |
| answersrequired | object | One answer for every question, under its ID; IDs and types match the questions exactly. noul: noul, the probability of true from 0 to 1. choice: choice (the selected key), probabilities (one per option key, 0 to 1) and confidence (0 to 1). score: score (may be fractional, over the zero-indexed levels), legend (the rubric descriptions as sent: strings, objects or arrays), probabilities per level and confidence. |
| captured | boolean | |
| revision | string | |
| thinking | object | Present only with reasoning on. Per question ID: max_tokens (the budget; null when there is no cap), generated_tokens, reasoning_tokens, finish_reason (natural, budget_exhausted or context_exhausted), generation_ms, scoring_ms and cached_tokens. Raw reasoning text is never returned. |
| checkpoint | integer | |
| example_id | string | |
| decision_id | string |
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