Data as of October 13, 2026 · How the score is built
1M tokens per request (provider-listed) contextScaleDown Extract API documentation
ScaleDown Extract
Decision readingScaleDown Extract is tracked, but not publicly ranked yet. The profile exposes 0 sourced benchmark rows and leaves unsupported fields blank until a published record exists.
ScaleDown Extract will be repriced, updated or retired. Get each notice with its source and date. Follow model changes
Lineage
The sequence follows explicit supersedes links. Each score is estimated for that model; a relative can inform a sparse estimate but never sets a floor, so a newer release can score below an earlier one. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.
Base entry
Spec sheet
Each documented value carries its source. Missing fields stay visible as not sourced or not published, rather than disappearing from the page.
- API model ID
- extractScaleDown Extract API documentation
- Context window
- 1M tokens per request (provider-listed)ScaleDown model limits and pricing FAQ
- Maximum output
- Not sourced yet
- Knowledge cutoff
- Not sourced yet
- Input modalities
- textScaleDown Extract API documentation
- Output modalities
- structured JSONScaleDown Extract API documentation
- Parameters
- Not disclosed by the provider
- Availability
- ScaleDown API · Enterprise licensingScaleDown model limits and pricing FAQ
- Cloud regions
- Not tracked yet
- Lifecycle
- Tracked
- API capabilities
- OpenAI-compatible POST /v1/chat/completions with a response_format JSON schema. Extracted entities include confidence, offsets, and surrounding context. Latest and pinned version 1 currently behave identically.ScaleDown Extract API documentation
- Prompt caching
- Not documented in the pricing recordScaleDown API pricing and FAQ
- Self-host
- Weights are not published
- Rate limits
- Not tracked yet
How to read this profile
The visual layer above carries the decisions. These notes preserve the model, ranking, coverage, and family context behind the numbers.
We track ScaleDown Extract, but no weighted text-model benchmark result is published on the site yet. This page shows the metadata and separate protocol evidence we can verify now; a BenchLM score will appear only if compatible public evaluations land.
ScaleDown Extract is a proprietary model with a 1M tokens per request (provider-listed) context window. No explicit reasoning mode is documented in this profile.
Available through the ScaleDown API. Enterprise licensing offers deployment on customer hardware; public downloadable weights are not listed. The provider lists a one-million-token request limit; an architectural configuration is not published.
ScaleDown Extract finds entities and structured fields described by a JSON schema, returning confidence scores and character offsets. The documented model IDs are extract and extract-1, which currently behave identically. Image and PDF inputs pass through AWS Textract OCR before extraction; this is not evidence of native visual reasoning. Parameter count and exact launch date are not published in the reviewed sources. Task-specific provider results are not mapped into general benchmark scores.
The profile has no source-displayable benchmark row yet.
Last updated October 13, 2026. Runtime fields remain blank until a sourced snapshot exists.
Questions
How does ScaleDown Extract perform overall in AI benchmarks?
ScaleDown Extract does not have any source-displayable benchmark rows yet, so this profile does not assign a public score or rank. Documented specifications remain visible, while score-led charts and claims stay unavailable until a published evaluation can be attached to the exact model.
Does ScaleDown Extract have full benchmark coverage on BenchLM?
No. ScaleDown Extract currently has 0 source-displayable rows across 670 tracked benchmark slots. The profile exposes published, non-generated evidence and leaves missing categories blank until an exact evaluation is available. Coverage describes how much was measured; it is not a penalty added to an individual benchmark result.
What is the context window size of ScaleDown Extract?
ScaleDown Extract has a documented context window of 1M tokens per request (provider-listed). That figure is the maximum combined prompt and retained-conversation space reported for this exact model; it is not the maximum output length. The profile keeps output limits separate because providers often publish those limits independently.