Model · ScaleDown

ScaleDown Extract

ScaleDown Extract is served by ScaleDown through OpenRouter. It provides 1M tokens of context and supports text, vision, files inputs.

Snapshot 2026-10-16 scaledown/extract

Context window

1M tokens

Input / 1M tokens

$0.040

Output / 1M tokens

Key facts

Provider	ScaleDown
OpenRouter ID	`scaledown/extract`
Modalities	Text, Vision, Files
Best for	Long Context, Budget

Source openrouter.ai/api/v1/models Verified 2026-10-16 Drift-checked daily 06:00 CST

Best-fit workloads

Best for long context

Large context window for documents and transcripts.

Best for budget

Low input/output cost for high-volume workloads.

Cost estimates

These examples use the 2026-10-16 input and output prices. Cache discounts, provider surcharges and tool fees are excluded.

Workload	Input	Output	Estimated cost
Quick test	20,000	5,000	$0.001
Product chat month	500,000	125,000	$0.020
Document batch	5,000,000	1,000,000	$0.200

Strengths

✓ Output is billed at $0: this row charges for input only, at $0.04 per million tokens
✓ A 1,000,000-token window with a 900,000-token output ceiling
✓ Entity extraction driven by a plain-English field list (`provider.options.scaledown.entities`) rather than a fixed schema

Limitations

! A single-purpose endpoint, not a general model: it extracts the fields you describe, and does not do much else
! No published benchmark, and Artificial Analysis does not carry it
! It is the only row here that cannot be compared on output price, because there is not one

OpenRouter API quickstart

Run these examples from your server or edge function. Keep the OpenRouter API key private and do not expose it in browser code.

cURL

curl https://openrouter.ai/api/v1/chat/completions \
  -H "Authorization: Bearer $OPENROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "scaledown/extract",
    "messages": [{ "role": "user", "content": "Summarize this document in five bullet points." }],
    "max_tokens": 500
  }'

JavaScript / TypeScript

const response = await fetch("https://openrouter.ai/api/v1/chat/completions", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${process.env.OPENROUTER_API_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    model: "scaledown/extract",
    messages: [{ role: "user", content: "Summarize this document in five bullet points." }],
    max_tokens: 500,
  }),
});

const data = await response.json();
console.log(data.choices[0].message.content);

How to evaluate ScaleDown Extract

Run five real tasks from your product, not generic demo prompts.
Record token usage, latency and output quality.
Estimate cost using your real input/output mix.
Compare it against one model with a similar price and one stronger model.

Compare models View on OpenRouter ↗