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Model infoModelsSnapshot: 15 Oct 2026

Maritaca AI / Sabiá

Sabiá 3 (September 2024)

Historical Portuguese model revision explicitly discontinued in Maritaca’s API retirement table.

retiredClosed weightsText generation
Official documentation (opens in a new tab)Reviewed 14 Oct 2026
6 of 17 specification fields collected
Context window32K32,000 tokens
Maximum outputNot collectedAwaiting source review
Input billingNot collectedMaritaca API (retired)
Output billingNot collectedNo inferred price

Specifications

Provider-documented facts. “Not collected” means we have not verified that field yet, not that the feature is unsupported.

Maximum input tokens
Not collected
Maximum output
Not collected
Reasoning
Not collected
Tool calling
SupportedSource for Tool calling (opens in a new tab)

The named September/November revisions were evaluated on BFCL function calling. Current endpoint is retired.

Structured output
Not collected
Streaming
Not collected
Batch processing
Not collected
Fine-tuning
Not collected
Release date
Not collected
Parameters
Not collected
Architecture
Not collected
Languages
PortugueseSource for Languages (opens in a new tab)

Documented specialization, not a claim that other languages are unsupported.

Context caching
Not collected

No prompt-cache contract verified for this exact historical revision.

Access & pricing

Prices belong to an endpoint and billing tier. A provider listing does not guarantee access for every account or region.

API model ID
sabia-3-2024-09-09
Access
No longer available through this API model selector. Current provider recommendation is Sabiazinho 4.
Regions
Not verified for this exact historical deployment.

Endpoint pricing has not been collected. Check the provider for applicable rates.

Data & enterprise terms

Retention, training use, regional processing and enterprise terms have not been summarized for this endpoint.

Caching

Caching can reuse earlier input processing. Availability, lifetime and billing depend on the endpoint. A cache hit still generates a new response.

01 / INITIAL INPUTProcess & retain

The system processes eligible input and retains reusable state.

02 / LATER REQUESTCheck available state

Reuse depends on matching input, session state and the cache policy.

03 / CACHE HITReuse & generate

Reusing state can reduce repeated work. Any charges follow the billing rules below.

Maritaca API (retired)

sabia-3-2024-09-09
Not verified
How it works
Not collected
Minimum reusable input
Not collected
Lifetime & refresh
Not collected
How to enable it
Not collected
Usage fields
Not collected
Billing rules
Not collected
Limits & data boundaries
Not collected

No verified cache rate for this endpoint. An empty rate is not a zero charge.

Caching reviewed 14 Oct 2026

What the measurements say

Brazilian academic/professional exams

Maritaca AI · vendor-reported
80.2% accuracy

Provider technical report revision 2 identifies the September 9 Sabiá 3 and November 13 Sabiazinho 3 checkpoints. Exams average 70 tests after mid-2023. BFCL uses English examples. AgentBench uses inverse-difficulty weights and a 0–10 normalized overall score. These are not results for December 2024 / February 2025 revisions.

Evaluation source (opens in a new tab)

IFEval loose

Maritaca AI · vendor-reported
78score / 100

Provider technical report revision 2 identifies the September 9 Sabiá 3 and November 13 Sabiazinho 3 checkpoints. Exams average 70 tests after mid-2023. BFCL uses English examples. AgentBench uses inverse-difficulty weights and a 0–10 normalized overall score. These are not results for December 2024 / February 2025 revisions.

Evaluation source (opens in a new tab)

IFEval strict

Maritaca AI · vendor-reported
73.8score / 100

Provider technical report revision 2 identifies the September 9 Sabiá 3 and November 13 Sabiazinho 3 checkpoints. Exams average 70 tests after mid-2023. BFCL uses English examples. AgentBench uses inverse-difficulty weights and a 0–10 normalized overall score. These are not results for December 2024 / February 2025 revisions.

Evaluation source (opens in a new tab)

BFCL overall

Maritaca AI · vendor-reported
56.24% accuracy

Provider technical report revision 2 identifies the September 9 Sabiá 3 and November 13 Sabiazinho 3 checkpoints. Exams average 70 tests after mid-2023. BFCL uses English examples. AgentBench uses inverse-difficulty weights and a 0–10 normalized overall score. These are not results for December 2024 / February 2025 revisions.

Evaluation source (opens in a new tab)

AgentBench overall

Maritaca AI · vendor-reported
2.9weighted score / 10

Provider technical report revision 2 identifies the September 9 Sabiá 3 and November 13 Sabiazinho 3 checkpoints. Exams average 70 tests after mid-2023. BFCL uses English examples. AgentBench uses inverse-difficulty weights and a 0–10 normalized overall score. These are not results for December 2024 / February 2025 revisions.

Evaluation source (opens in a new tab)

Latency and throughput depend on the host, request size, reasoning settings and load. There is no measured speed or global usage figure in this record.

The record behind the model

Why it is included

Named provider model without a matching public weight release in the reviewed official inventory. Classification for historical artifacts is inferred; a public tokenizer, public upstream base or licensed executable is not a public release of this derivative.

Classification evidence (opens in a new tab)

Lifecycle dates

API access ended17 Feb 2025Source (opens in a new tab)

Sources reviewed