Source: https://ailoot.ai/models/en/scaledown-summarize/
Canonical: https://ailoot.ai/models/en/scaledown-summarize/
Language: en

# ScaleDown: Summarize

[Markdown](https://ailoot.ai/models/en/scaledown-summarize/index.md) · [JSON](https://ailoot.ai/models/en/scaledown-summarize/facts.json)

## [ScaleDown: Summarize](https://ailoot.ai/models/en/scaledown-summarize/)

Summarize rewrites long text and documents into a fluent, faithful condensation. Set the focus, language, length and format through instructions, then use the result for document review, transcript digests or shorter retrieval context. The published window is 1,000,000 tokens, with a 4,000-token completion limit.

- [**Ailoot Ultra · OpenAI Chat** `POST /v1/chat/completions` `scaledown/summarize`](https://ailoot.ai/models/en/scaledown-summarize/#method-0)
- [**Ailoot Ultra · Anthropic Messages** `POST /v1/messages` `scaledown/summarize`](https://ailoot.ai/models/en/scaledown-summarize/#method-1)

About the model

This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.

Summarize is ScaleDown’s abstractive summarization model. It condenses supplied text and documents into a fluent rewrite rather than selecting a few original sentences verbatim. Its published uses include document review, transcript digests and shortening retrieved material before another model consumes it.

#### Define what the summary must retain

Give the task a focus and an intended reader. A meeting digest can retain decisions, responsibilities and open questions. A document review can retain the main claims and supporting evidence. A retrieval workflow can retain the facts that matter to the question the next model will answer.

Specify the desired language, length and layout in the content instructions. The published model uses system-prompt direction for those choices, while its sampling configuration is fixed for faithful output. A summary for a person and a compact passage for another model can therefore use different layouts without treating sampling variation as the main control.

#### A rewrite with traceable facts

Keep document names and important section boundaries with the source. Ask the summary to preserve names, numbers, stated decisions and any disagreement that affects interpretation. For a transcript, distinguish a proposal from a decision and an unresolved question from an assigned action.

After generation, compare the condensed text with the original material. Check whether a sentence changes the certainty of a claim, drops a condition or combines statements that belong to different speakers or sections. The purpose of a shorter result is to make the evidence easier to consume while keeping its meaning.

#### Documents, images and retrieval context

The published input range includes text, images and files such as PDFs, and the model returns text. Supply readable material and define whether the task concerns the whole document or a selected section. A collection benefits from consistent summary questions and a document identifier retained with each result.

For retrieval pre-processing, shorten content around the specific question that downstream reasoning must address. Retain the passages or facts that support an answer instead of producing a generic overview of the source. Keep the original retrieved material available so the later result can still be checked against it.

#### Plan the output length

The published context window is 1,000,000 tokens and the completion limit is 4,000 tokens. Set a summary length that fits the intended artifact. If a large collection needs substantial detail, organise it into identified documents or sections and retain a separate result for each before composing a collection-level digest.

The result is a text summary. Validate any machine-consumed structure in the application, while keeping the written summary readable for its intended audience. Save the summary with the source identity and the instructions that determined its focus, language and length.

#### Capabilities

- Abstractive summaries that rewrite and condense supplied material.
- Instruction-driven focus, language, length and layout.
- Fixed sampling configuration tuned for faithful output.
- Text, image and file inputs with text output.
- Published context of 1,000,000 tokens and completion limit of 4,000 tokens.

#### Use cases

- **Document review digest** — A document and the findings the reader needs to retain. Result: A concise, identified summary with key facts preserved.
- **Transcript digest** — A transcript with a focus on decisions, responsibilities or open questions. Result: A shorter text separating those categories.
- **Retrieval pre-processing** — Retrieved content and the downstream question. Result: A compact evidence passage for later reasoning.
- **Collection summaries** — Named documents with shared summary instructions. Result: Comparable per-document digests.

#### Limits

- **Context** — Published context window: 1,000,000 tokens.
- **Reply** — Published completion limit: 4,000 tokens.
- **Style controls** — Steer focus, language, length and format through instructions; sampling is fixed.
- **Output contract** — The published output is text; function calling and schema-enforced JSON are not listed.

**Ailoot Ultra · OpenAI Chat** `POST /v1/chat/completions` `scaledown/summarize`

[`scaledown/summarize`](https://ailoot.ai/models/en/#m=scaledown%2Fsummarize&route=Ultra&caller=scaledown%2Fsummarize&fmt=chat)

ScaleDown /`scaledown/summarize`

### Overview

Summarize rewrites long text and documents into a fluent, faithful condensation. Set the focus, language, length and format through instructions, then use the result for document review, transcript digests or shorter retrieval context. The published window is 1,000,000 tokens, with a 4,000-token completion limit.

Accepted inputs

Text

Results

Text

### Available in Ailoot · inputs & methods

**Ailoot Ultra · OpenAI Chat** `POST /v1/chat/completions`

Input types

`text`

Output types

`text`

Output format

json or SSE

| Field | Type | Requirement | Values & limits | Default |
| --- | --- | --- | --- | --- |
| Field`model` | Type`string` | RequirementRequired | Values & limits scaledown/summarize | Default— |
| Field`messages` | Type`message[]` | RequirementRequired | Values & limits Minimum items: 1 | Default— |
| Field`messages[].role` | Type`string` | RequirementRequired | Values & limits Model-specific | Default— |
| Field`messages[].content` | Type`string` | RequirementOptional | Values & limits Applies when: Text conversation | Default— |
| Field`stream` | Type`boolean` | RequirementOptional | Values & limits | Defaultfalse |

Input material combinations

**Text** · UTF8 text

`messages[].content`

Method parameters and lifecycle

| Parameter | Contract |
| --- | --- |
| Parameter`content_type` | Contractapplication/json |
| Parameter`required` | Contractmodel, messages |
| Parameter`material_types` | Contracttext |
| Parameter`messages` | Contractmessage array with role/content shape |
| Parameter`file_material` | Contractactual UTF-8 document inserted as literal text; no file ID inferred |
| Parameter`optional_controls` | Contractstream |
| Parameter`control_scope` | ContractText conversation; stream is a boolean and defaults to false. |
| Parameter`output_modalities` | Contracttext |
| Parameter`hosted_operations` | Contractfalse |
| Parameter`multimodal_input_binding` | Contractnull |

Method aliases: `/chat/completions`

Lifecycle: `synchronous_or_streamed`

Send exact model ID with messages.

Context length is not an output cap.

A text file may supply literal text; image/audio/file-object inputs need exact binding.

### Pricing

Tariff references do not add request options. Supported parameters are listed for the selected method.

Fast mode

Not specified When fast mode is selected

Flex mode

Not specified When flex mode is selected

Maximum reasoning effort

Not specified When maximum reasoning effort uses the default adjustment

#### Ailoot rates

#### Prices by configuration

Match each charge to the conditions shown. Use one rate for each set of alternative configurations.

| Configuration | Rate schedule | Context tokens, more than | Context tokens, up to and including | Charge |
| --- | --- | --- | --- | --- |
| ConfigurationBase token rates | Rate scheduleBase rate | Context tokens, more than— | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens **$0.0001** / 1K tokens Output · Output tokens **$0** / 1K tokens Input · Cache read **—** / 1K tokens Input · Cache write **—** / 1K tokens Input · Cache write, 1 hour **—** / 1K tokens |
| ConfigurationRequest-length tier 1 | Rate schedule— | Context tokens, more than0 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens **$0.0001** / 1K tokens Output · Output tokens **$0** / 1K tokens Input · Cache read **—** / 1K tokens Input · Cache write **—** / 1K tokens Input · Cache write, 1 hour **—** / 1K tokens |

Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. [How reservation and settlement work →](https://ailoot.ai/docs/en/billing/reserve/)

### Example requests

#### Ailoot Ultra · OpenAI Chat

curlPython SDK

```bash
set -eu
: "${AILOOT_API_KEY:?Set your Ailoot API key}"
python3 - <<'PY_REQUEST'
import json,os
from pathlib import Path
p=json.loads('{"model":"scaledown/summarize","messages":[{"role":"user","content":"hello"}],"stream":false}')

Path("request.json").write_text(json.dumps(p,ensure_ascii=False))
PY_REQUEST
curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/chat/completions \
  -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \
  --data-binary @request.json -D response-headers.txt -o result.json
python3 - <<'PY_RESULT'
import json
from pathlib import Path
r=json.loads(Path("result.json").read_text())
for x in r.get("choices",[]):
    if x.get("message",{}).get("content"): print(x["message"]["content"])
PY_RESULT
```

```python
from openai import OpenAI
import os

if not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")
client=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)
result=client.chat.completions.create(model="scaledown/summarize", messages=[{"role":"user","content":"hello"}], stream=False)
import base64, json
from pathlib import Path

def save_result(data, stem="result"):
    # Keep every field, including usage, IDs, reasoning and tool requests.
    Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")
    print(json.dumps(data, ensure_ascii=False, indent=2))
    def save_blob(encoded, suffix, label):
        raw = base64.b64decode(encoded, altchars=b"-_", validate=True)
        ext = "png" if raw.startswith(b"\x89PNG\r\n\x1a\n") else "jpg" if raw.startswith(b"\xff\xd8") else "webp" if raw[:4] == b"RIFF" and raw[8:12] == b"WEBP" else suffix
        file = Path(stem + "-" + label + "." + ext)
        file.write_bytes(raw)
        print("Saved", file)
    for index, block in enumerate(data.get("content") or []):
        print("Content", index, json.dumps(block, ensure_ascii=False))
    for choice in data.get("choices") or []:
        print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))
    for index, item in enumerate(data.get("output") or []):
        print("Output", index, json.dumps(item, ensure_ascii=False))
        if item.get("type") == "image_generation_call" and item.get("result"):
            save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))
    for ci, candidate in enumerate(data.get("candidates") or []):
        for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):
            print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))
            blob = part.get("inlineData") or part.get("inline_data")
            if blob and blob.get("data"):
                mime = blob.get("mimeType") or blob.get("mime_type")
                suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")
                save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))

save_result(result.to_dict(mode="json"))
```

### Receive and save results

#### Ailoot Ultra · OpenAI Chat

json or SSE

##### Keep all completed results

Keep every complete returned response, file and content part.

Keep every choice and its content/tool calls/reasoning. In SSE assemble deltas by choice/tool index, retain the final finish reason and usage.

##### If the original response needs checking

Keep request/response IDs and headers. No generic text job retrieval URL is supplied.

Response fields

| Field | Type |
| --- | --- |
| Field`choices[].index` | Typeinteger |
| Field`choices[].message.content` | Typestring\|null |
| Field`choices[].message.tool_calls` | Typetool\_call\[\] |
| Field`choices[].message.reasoning_content` | Typestring |
| Field`choices[].message.reasoning_details` | Typeobject\|array |
| Field`choices[].finish_reason` | Typestring\|null |
| Field`usage` | Typeobject |

Errors and next steps

| Response | Condition | Next step |
| --- | --- | --- |
| Response400 · `invalid_request_error` | ConditionInvalid required fields, material representation or parameter value | Next stepCorrect the indicated fields before a new creation request. |

### Aliases

#### Model aliases

**Ailoot Ultra** [`scaledown/summarize`](https://ailoot.ai/models/en/#m=scaledown%2Fsummarize&route=Ultra&caller=scaledown%2Fsummarize&fmt=chat)

#### Method path aliases

`/chat/completions`

#### Related caller IDs

[`scaledown/summarize`](https://ailoot.ai/models/en/#m=scaledown%2Fsummarize&route=Ultra&caller=scaledown%2Fsummarize&fmt=chat)

**Ailoot Ultra · Anthropic Messages** `POST /v1/messages` `scaledown/summarize`

[`scaledown/summarize`](https://ailoot.ai/models/en/#m=scaledown%2Fsummarize&route=Ultra&caller=scaledown%2Fsummarize&fmt=messages)

ScaleDown /`scaledown/summarize`

### Overview

Summarize rewrites long text and documents into a fluent, faithful condensation. Set the focus, language, length and format through instructions, then use the result for document review, transcript digests or shorter retrieval context. The published window is 1,000,000 tokens, with a 4,000-token completion limit.

Accepted inputs

Text

Results

json · sse

### Available in Ailoot · inputs & methods

**Ailoot Ultra · Anthropic Messages** `POST /v1/messages`

Input types

`text`

Output types

`json`, `sse`

Output format

JSON or SSE

| Field | Type | Requirement | Values & limits | Default |
| --- | --- | --- | --- | --- |
| Field`model` | Type`string` | RequirementRequired | Values & limits scaledown/summarize | Default— |
| Field`messages` | Type`message[]` | RequirementRequired | Values & limits | Default— |
| Field`messages[].role` | Type`string` | RequirementRequired | Values & limits user, assistant | Default— |
| Field`messages[].content` | Type`string` | RequirementRequired | Values & limits | Default— |
| Field`max_tokens` | Type`integer` | RequirementRequired | Values & limits | Default— |
| Field`stream` | Type`boolean` | RequirementOptional | Values & limits | Defaultfalse |

Input material combinations

**Text** · UTF-8 text

`messages[].content string`

Method parameters and lifecycle

| Parameter | Contract |
| --- | --- |
| Parameter`content_type` | Contractapplication/json |
| Parameter`required` | Contractmodel, messages, max\_tokens |
| Parameter`optional_controls` | Contractstream |
| Parameter`material_types` | Contracttext |
| Parameter`text_file_material` | ContractRead the actual UTF-8 document and put its text in the indicated input or message content string. |

Lifecycle: `synchronous_or_streamed`

### Pricing

Tariff references do not add request options. Supported parameters are listed for the selected method.

Fast mode

Not specified When fast mode is selected

Flex mode

Not specified When flex mode is selected

Maximum reasoning effort

Not specified When maximum reasoning effort uses the default adjustment

#### Ailoot rates

#### Prices by configuration

Match each charge to the conditions shown. Use one rate for each set of alternative configurations.

| Configuration | Rate schedule | Context tokens, more than | Context tokens, up to and including | Charge |
| --- | --- | --- | --- | --- |
| ConfigurationBase token rates | Rate scheduleBase rate | Context tokens, more than— | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens **$0.0001** / 1K tokens Output · Output tokens **$0** / 1K tokens Input · Cache read **—** / 1K tokens Input · Cache write **—** / 1K tokens Input · Cache write, 1 hour **—** / 1K tokens |
| ConfigurationRequest-length tier 1 | Rate schedule— | Context tokens, more than0 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens **$0.0001** / 1K tokens Output · Output tokens **$0** / 1K tokens Input · Cache read **—** / 1K tokens Input · Cache write **—** / 1K tokens Input · Cache write, 1 hour **—** / 1K tokens |

Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. [How reservation and settlement work →](https://ailoot.ai/docs/en/billing/reserve/)

### Example requests

#### Ailoot Ultra · Anthropic Messages

Complete minimal JSON bodycurl · use your UTF-8 documentPython · save the complete result

```json
{
  "model": "scaledown/summarize",
  "max_tokens": 128,
  "messages": [
    {
      "role": "user",
      "content": "Summarize the incident report."
    }
  ],
  "stream": false
}
```

```bash
set -eu
: "${AILOOT_API_KEY:?Set your Ailoot API key}"
: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}"
export AILOOT_TEXT_FILE
python3 - <<'PY_REQUEST'
import json, os
from pathlib import Path

source_file = Path(os.environ['AILOOT_TEXT_FILE'])
if not source_file.is_file():
    raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')
text = source_file.read_text(encoding='utf-8')
if not text.strip():
    raise SystemExit('Choose a document containing text')
payload = {'model': 'scaledown/summarize', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}
payload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\n\n' + text
Path('request.json').write_text(json.dumps(payload, ensure_ascii=False), encoding='utf-8')
PY_REQUEST
curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/messages \
  -H "x-api-key: $AILOOT_API_KEY" -H 'anthropic-version: 2023-06-01' -H 'Content-Type: application/json' \
  --data-binary @request.json -D response-headers.txt -o result.json
python3 - <<'PY_RESULT'
import json
from pathlib import Path

result = json.loads(Path('result.json').read_text(encoding='utf-8'))
print(json.dumps(result, ensure_ascii=False, indent=2))
PY_RESULT
```

```python
import json, os
from pathlib import Path

source_file = Path(os.environ['AILOOT_TEXT_FILE'])
if not source_file.is_file():
    raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')
text = source_file.read_text(encoding='utf-8')
if not text.strip():
    raise SystemExit('Choose a document containing text')
payload = {'model': 'scaledown/summarize', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}
payload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\n\n' + text
from urllib.request import Request, urlopen
from urllib.error import HTTPError

api_key = os.environ['AILOOT_API_KEY']
if not api_key.strip():
    raise SystemExit('Set AILOOT_API_KEY before making a request')
headers = {'x-api-key': 'PLACEHOLDER_KEY', 'anthropic-version': '2023-06-01', 'Content-Type': 'application/json'}
headers['x-api-key'] = api_key
request = Request('https://api.ailoot.ai/v1/messages', data=json.dumps(payload, ensure_ascii=False).encode('utf-8'), headers=headers, method='POST')
try:
    with urlopen(request, timeout=120) as response:
        raw = response.read()
        Path('response-headers.txt').write_text(str(response.headers), encoding='utf-8')
except HTTPError as error:
    Path('result.json').write_bytes(error.read())
    Path('response-headers.txt').write_text(str(error.headers), encoding='utf-8')
    raise
Path('result.json').write_bytes(raw)
import json
from pathlib import Path

result = json.loads(Path('result.json').read_text(encoding='utf-8'))
print(json.dumps(result, ensure_ascii=False, indent=2))
```

### Receive and save results

#### Ailoot Ultra · Anthropic Messages

JSON or SSE

##### Keep all completed results

Keep every complete returned response, file and content part.

Keep all content blocks, thinking signatures and actual tool-use IDs. Assemble all SSE block deltas; a first text block is not the complete output.

##### If the original response needs checking

Keep request/response IDs and headers. No generic text job retrieval URL is supplied.

Response fields

| Field | Type |
| --- | --- |
| Field`id` | Typestring |
| Field`model` | Typestring |
| Field`content[]` | Typetext\|thinking\|tool\_use\|other content block |
| Field`stop_reason` | Typestring\|null |
| Field`usage` | Typeobject |

Errors and next steps

| Response | Condition | Next step |
| --- | --- | --- |
| Response400 · `invalid_request_error` | ConditionInvalid required fields, material representation or parameter value | Next stepCorrect the indicated fields before a new creation request. |

### Aliases

#### Model aliases

**Ailoot Ultra** [`scaledown/summarize`](https://ailoot.ai/models/en/#m=scaledown%2Fsummarize&route=Ultra&caller=scaledown%2Fsummarize&fmt=messages)

#### Method path aliases

`/chat/completions`

#### Related caller IDs

[`scaledown/summarize`](https://ailoot.ai/models/en/#m=scaledown%2Fsummarize&route=Ultra&caller=scaledown%2Fsummarize&fmt=messages)

FAQ · ScaleDown: Summarize

What inputs and results are available?

`POST /v1/chat/completions` · `scaledown/summarize`: `text` → `text` `POST /v1/messages` · `scaledown/summarize`: `text` → `json`, `sse`

Which fields does the request use?

`POST /v1/chat/completions` · `scaledown/summarize`: `model`, `messages`, `stream` `POST /v1/messages` · `scaledown/summarize`: `model`, `messages`, `max_tokens`, `stream`

What limits apply?

`POST /v1/chat/completions` · `scaledown/summarize`: `messages.min_items`: `1`

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## Ailoot · The company and its project portfolio





Ailoot combines established operating experience, a large client base, stable service and ongoing KPI control.





The company and its project portfolio: millions of customers; monthly sales in the billions.





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