Maritaca AI Academic Credits
No-cost research and development inference across 10 models; not unrestricted production use.
https://chat.maritaca.ai/apiModels mentioned
sabia-4sabia-4-br-spsabia-4-thinkingsabia-4-thinking-br-spsabiazinho-4sabiazinho-4-br-spsabia-4-2026-01-06sabiazinho-4-2026-01-06sabia-4-smallsabiazim-4Equivalent paid value
How this was valued: Continuous saturation of the documented tier-0 per-model envelope (60 requests, 128k input tokens, and 10k output tokens per minute) for the costliest model that has both a documented tier-0 rate-limit row and a current price, sabia-4, at Maritaca's own BRL rates (R$5.00 input / R$20.00 output per 1M tokens). All figures are BRL; no currency conversion is applied. Partial because the academic credit's face amount and duration are not public and the granted credit balance, not this rate ceiling, ultimately bounds deliverable value, and because approved academic accounts are assumed to sit at the documented tier 0. Subtotal because the tier table lists per-model rows (sabiazinho-4 and sabia-3-family rows are identical) that are not summed since their independence is not explicit, and sabia-4-thinking / BR-SP / small variants are excluded for lacking a documented tier-0 row despite higher list prices (sabia-4-thinking output is R$40/1M). Assumes uninterrupted saturation with no latency or availability loss.
Limits and terms
- Credit Amount
- not public
- Duration
- not public
- Tier 0 Requests Per Minute
- 60
- Tier 0 Input Tokens Per Minute
- 128,000
- Tier 0 Output Tokens Per Minute
- 10,000
What happens to your prompts?
The API DPA says prompts and outputs are immediately discarded and never used for training or improvement; only narrow technical logs and temporary error fragments remain.
- Plan Scope
- Maritaca hosted API, including approved academic-credit use; the public API DPA applies automatically.
- Prompt Retention
- API prompts are processed for inference and immediately discarded after the response, subject only to narrow temporary error fragments.
- Response Retention
- API outputs are immediately discarded after delivery under the same DPA rule.
- Ordinary Logging
- Technical logs without ordinary prompt/output content may be kept for up to 18 months; error fragments may be retained for up to 30 days for diagnosis.
- Model Training
- Maritaca says API prompts and outputs are never used to train models.
- Product Improvement
- API prompts and outputs are excluded from content-based service improvement.
- Human Or Operator Access
- Ordinary content is not retained for review; authorized staff may access narrow error fragments during their 30-day diagnostic window.
- Subprocessors And Routing
- The DPA governs Maritaca and disclosed processors; the qualifying Sabiá API is operated by Maritaca rather than an arbitrary model router.
- Deletion Controls
- Ordinary content is discarded automatically. Technical records and error fragments expire under their stated 18-month and 30-day limits, with data-subject controls supplied by the DPA.
- Caveat
- The narrow error-fragment exception should still be considered before sending regulated or confidential data.
Governing documents
- Data Processing Agreement
- https://www.maritaca.ai/dpa/
Eligibility
- Application Required
- Yes
- Applicants
- studentsfacultyresearchers
- Project Types
- teachingscientific research
Primary sources
Before you build with Maritaca AI Academic Credits
Read the classification narrowly
This record has current first-party evidence for some zero-cost hosted inference. The label describes the bounded offer supported by evidence on 2026-09-19; it does not guarantee permanence, production suitability, uptime, latency, model quality, or access from every region.
Resolve the live model route
This snapshot records 10 model IDs. Match the exact ID against the provider's live catalog before using it in code; zero-price routes and aliases can rotate while an older documentation page remains online. The recorded base endpoint is https://chat.maritaca.ai/api, but the provider's current API reference remains authoritative for paths, authentication, and request shape.
Confirm account and billing boundaries
The public evidence does not settle whether an account is required. Confirm the signed-in flow before treating anonymous or credential-free access as available. The payment-card requirement is conditional or not clearly documented. Treat signup friction and billing exposure as unresolved until the account flow confirms them. The equivalent paid value shown here prices a documented allowance where possible; it is not cash, guaranteed savings, or protection from overage.
Re-read the prompt policy for your plan
This record classifies the reviewed handling as “Private”: The API DPA says prompts and outputs are immediately discarded and never used for training or improvement; only narrow technical logs and temporary error fragments remain. Provider policies can distinguish free, paid, enterprise, opted-in, and feature-specific traffic, so verify the governing terms for the exact account and route that will receive your data.
Follow the evidence, then re-check it
The record links 7 first-party sources covering the offer, catalog, limits, pricing, terms, privacy, or adoption evidence available to the audit. Prefer the newest governing document or live catalog when sources disagree, and submit a correction when a provider changes a material term.
Match the quota to the workload shape
Translate the published allowance into the traffic pattern you actually expect instead of comparing headline totals alone. A daily token pool can look generous while a low requests-per-minute or concurrency ceiling blocks interactive bursts; a high request limit can still fail a long-context job when input, output, or per-request tokens are capped. Separate prompt tokens from generated tokens, include retries and tool calls, and test the largest realistic payload. If the provider documents more than one limit window, the tightest window at your peak load is the practical ceiling. Research, experimental, and community access can also carry eligibility or fair-use constraints that cannot be modeled as a simple number.
Plan fallback without changing the rules
A fallback should preserve more than API syntax. Confirm that the substitute route supports the required modality, context length, streaming behavior, structured output, tools, and safety controls, then compare its prompt-retention and training terms. An OpenAI-compatible request shape does not make providers operationally or contractually equivalent. Decide which errors may trigger a retry, cap retry storms, and prevent an exhausted free route from silently switching to a billable model. If deterministic output matters, record the model revision and sampling settings; rotating aliases and free-model pools can change behavior even when the endpoint remains available.
Monitor the offer as a dependency
Capture the model ID, response model field, rate-limit headers, usage fields, latency, HTTP status, and any provider request identifier for each test. Watch for authorization failures, quota exhaustion, catalog removal, policy revisions, and dashboard balance changes as separate failure modes. Re-check the provider’s live catalog and governing pages on a schedule proportionate to the workload’s importance, and keep the dated sources that supported your decision. Free capacity is especially suitable for prototypes, evaluations, fallbacks, and bounded workloads when the application can tolerate change; a production dependency still needs observability, an exit path, and an owner responsible for re-verification.
Test one complete request before scaling
Start with the smallest permitted request using the exact credential, model ID, endpoint, and account type you intend to deploy. Record the HTTP status, response headers, usage fields, latency, and any dashboard balance change. Then exercise the failure path: an invalid model, an exhausted quota, or a rate-limit response should fail clearly without silently switching to a paid route. If the provider supports streaming or tool calls, validate those features separately because a free model can expose a narrower capability set than its paid counterpart. Keep a budget ceiling outside the application whenever billing is possible, and avoid sending sensitive data until the observed route matches the reviewed data agreement.