Source: https://ailoot.ai/models/en/deepseek-ai-deepseek-r1-distill-qwen-14b/
Canonical: https://ailoot.ai/models/en/deepseek-ai-deepseek-r1-distill-qwen-14b/
Language: en

# DeepSeek R1 Distill Qwen 14B

[Markdown](https://ailoot.ai/models/en/deepseek-ai-deepseek-r1-distill-qwen-14b/index.md) · [JSON](https://ailoot.ai/models/en/deepseek-ai-deepseek-r1-distill-qwen-14b/facts.json)

## [DeepSeek R1 Distill Qwen 14B](https://ailoot.ai/models/en/deepseek-ai-deepseek-r1-distill-qwen-14b/)

DeepSeek-R1-Distill-Qwen-14B is a text reasoning checkpoint based on Qwen2.5-14B and examples generated by DeepSeek-R1. It supports worked mathematical, programming and logical answers, with DeepSeek's own tokenizer, prompting recommendations and release configuration.

- [**Ailoot Ultra · OpenAI Chat** `POST /v1/chat/completions` `deepseek-ai/DeepSeek-R1-Distill-Qwen-14B`](https://ailoot.ai/models/en/deepseek-ai-deepseek-r1-distill-qwen-14b/#method-0)
- [**Ailoot Ultra · Anthropic Messages** `POST /v1/messages` `deepseek-ai/DeepSeek-R1-Distill-Qwen-14B`](https://ailoot.ai/models/en/deepseek-ai-deepseek-r1-distill-qwen-14b/#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.

DeepSeek-R1-Distill-Qwen-14B is the 14B-size Qwen-based reasoning checkpoint released by DeepSeek. Its base is Qwen2.5-14B, fine-tuned with examples prepared by DeepSeek-R1. It accepts a written problem or conversation and returns text that can include a mathematical solution, an explanation of program behavior or a conclusion reached from several supplied conditions.

Reasoning transfer and the named edition

DeepSeek distinguishes the full R1 and R1-Zero models from the dense models distilled onto Qwen and Llama bases. For the distilled releases, the training data carries reasoning patterns from the larger R1 model into a different checkpoint. The 14B name identifies this particular Qwen base; it does not identify the full R1 architecture, the 32B distill checkpoint or the ordinary Qwen2.5-14B-Instruct release.

The maker describes 800,000 curated examples for the Qwen distill releases and notes changes to their configurations and tokenizers. Keep the DeepSeek release settings when using this model. Those changes matter even though the model's origin is Qwen2.5. The 14B checkpoint's own configuration specifies 48 layers, 40 query attention heads and 8 key/value heads, with maximum positions set to 131,072.

What to supply for a useful worked answer

For a tutoring task, include the problem and the learner's attempted solution, then mark the transition that needs explanation. Ask the model to identify the relevant condition, explain the correction in the same notation and continue only as far as needed to establish the result. This keeps the answer connected to the learner's actual difficulty instead of producing an unrelated full lesson.

For example, a learner may have divided by an expression without stating whether it can be zero. Supply the original equation and ask which cases must be considered before that transformation. The desired result can separate the allowed case, the exceptional case and the final solution set. This is a proposed mathematical task, with the answer form defined by the condition rather than by a generic request to explain algebra.

For a programming question, supply the algorithm or function and a concrete input. Ask how the state changes and why the returned result follows. A follow-up can request a revised implementation under an explicit constraint, such as retaining the public function signature or avoiding mutation of the input. Include the relevant helper code and expected behavior so that the explanation does not depend on an unspecified implementation.

For a logical comparison, identify the alternatives and the conditions each must satisfy. Request the assumptions used by each approach, the conflicts that rule out a choice and the remaining unresolved information. A compact decision table can then become the final artifact, while the surrounding prose explains how the supplied rules support it.

Prompting recommendations from DeepSeek

The maker recommends avoiding a system prompt and placing all instructions directly in the user prompt. Put the task, output requirements and necessary source material together, clearly identifying which text is the problem and which text is an instruction about the answer. Use the released tokenizer and conversation settings rather than assuming that the original Qwen formatting is interchangeable.

The documented temperature range is 0.5 to 0.7, with 0.6 recommended. DeepSeek connects this to reducing repetition and incoherent output. For mathematical questions, it recommends a worked response with the final result in a LaTeX box. It also describes cases where the model bypasses the reasoning block and recommends beginning the response with the opening reasoning marker to encourage the intended reasoning workflow.

A useful mathematical output can contain the chosen method, the justified transformations and a separate final expression. State whether the result should be exact or rounded and specify units when they matter. For code, ask for the explanation separately from the proposed implementation. For logic, ask for the final conclusion together with the conditions under which it holds. These requirements determine what the reader or application can actually use after the reasoning text.

Context and substantive boundaries

The current released configuration sets maximum positions to 131,072. That is this checkpoint's configuration value, not an inherited promise about every serving interface. The maker's 32,768-token generation setting is part of evaluation; it does not establish a separate hard maximum answer length. The output capacity of the original Qwen Instruct model should likewise not be transferred into this modified reasoning release without its own specification.

This is a text model. An image of a mathematical problem needs its symbols and conditions converted into text before the reasoning task, and executing proposed code needs a separate development or computation environment. Distillation describes training with R1 examples, rather than an automatic runtime call to the teacher or a built-in independent verifier.

Use the named 14B checkpoint for a defined worked-answer task and preserve the problem's notation, assumptions and target result. The practical aim is a solution the reader can inspect: which conditions apply, why each important step follows and what final expression, implementation or conclusion answers the original question.

Practical task

### Repair an algebraic division step

Technical example in English

```
Repair an algebraic division step

A student solves x(x - 3) = 2(x - 3) by dividing by x - 3 and concludes x = 2. Explain the missing case without changing the original equation. Separate the zero-divisor case from the nonzero case, then give the complete solution set and substitute each candidate back into the equation.
```

#### Expected result

```
A case split, a complete solution set and a substitution check for each candidate.
```

#### Check the result

```
The response checks the x - 3 = 0 case rather than silently dividing it away.
```

**Ailoot Ultra · OpenAI Chat** `POST /v1/chat/completions` `deepseek-ai/DeepSeek-R1-Distill-Qwen-14B`

[`deepseek-ai/DeepSeek-R1-Distill-Qwen-14B`](https://ailoot.ai/models/en/#m=deepseek-ai%2FDeepSeek-R1-Distill-Qwen-14B&route=Ultra&caller=deepseek-ai%2FDeepSeek-R1-Distill-Qwen-14B&fmt=chat)

DeepSeek /`deepseek-ai/DeepSeek-R1-Distill-Qwen-14B`

### Overview

DeepSeek-R1-Distill-Qwen-14B is a text reasoning checkpoint based on Qwen2.5-14B and examples generated by DeepSeek-R1. It supports worked mathematical, programming and logical answers, with DeepSeek's own tokenizer, prompting recommendations and release configuration.

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 deepseek-ai/DeepSeek-R1-Distill-Qwen-14B | Default— |
| Field`messages` | Type`message[]` | RequirementRequired | Values & limits Minimum items: 1 | Default— |
| Field`messages[].role` | Type`string` | RequirementRequired | Values & limits Examples: system, user, assistant, tool, function Conditions: These roles are explicitly represented by Source compatibility types; the exact model role set remains model-specific. Model-specific | Default— |
| Field`messages[].content` | Type`string\|content_part[]\|null` | RequirementOptional | Values & limits | Default— |
| Field`messages[].tool_calls` | Type`tool_call[]` | RequirementOptional | Values & limits | Default— |
| Field`messages[].tool_call_id` | Type`string` | RequirementOptional | Values & limits Applies when: role=tool; use actual returned ID | Default— |
| Field`messages[].name` | Type`string` | RequirementOptional | Values & limits | Default— |
| Field`messages[].reasoning_content` | Type`string` | RequirementOptional | Values & limits Applies when: Round-trip actual returned reasoning where this model uses it | Default— |
| Field`messages[].reasoning_details` | Type`object\|array` | RequirementOptional | Values & limits Model-specific | Default— |
| Field`max_tokens` | Type`integer` | RequirementOptional | Values & limits Minimum: 1 Model-specific | Default— |
| Field`max_completion_tokens` | Type`integer` | RequirementOptional | Values & limits Minimum: 1 Model-specific | Default— |
| Field`stream` | Type`boolean` | RequirementOptional | Values & limits | Defaultfalse |
| Field`stream_options.include_usage` | Type`boolean` | RequirementOptional | Values & limits Model-specific | Default— |
| Field`temperature` | Type`number` | RequirementOptional | Values & limits Model-specific | Default— |
| Field`top_p` | Type`number` | RequirementOptional | Values & limits Model-specific | Default— |
| Field`stop` | Type`string\|string[]` | RequirementOptional | Values & limits Model-specific | Default— |
| Field`tools` | Type`function_tool[]` | RequirementOptional | Values & limits Model-specific | Default— |
| Field`tools[].type` | Type`string` | RequirementOptional | Values & limits function Model-specific | Default— |
| Field`tools[].function.name` | Type`string` | RequirementRequired | Values & limits Applies when: function tool supplied | Default— |
| Field`tools[].function.description` | Type`string` | RequirementOptional | Values & limits | Default— |
| Field`tools[].function.parameters` | Type`JSON_schema` | RequirementOptional | Values & limits | Default— |
| Field`tool_choice` | Type`string\|object` | RequirementOptional | Values & limits Model-specific | Default— |
| Field`parallel_tool_calls` | Type`boolean` | RequirementOptional | Values & limits Model-specific | Default— |
| Field`response_format` | Type`object` | RequirementOptional | Values & limits Model-specific | Default— |
| Field`reasoning_effort` | Type`string` | RequirementOptional | Values & limits Model-specific | Default— |
| Field`reasoning` | Type`object` | RequirementOptional | Values & limits Model-specific | Default— |
| Field`service_tier` | Type`string` | RequirementOptional | Values & limits Model-specific | Default— |
| Field`instructions` | Type`string` | RequirementOptional | Values & limits Compatibility field | Default— |
| Field`functions` | Type`function[]` | RequirementOptional | Values & limits Model-specific | Default— |
| Field`function_call` | Type`string\|object` | RequirementOptional | Values & limits Model-specific | Default— |
| Field`prompt_cache_options` | Type`object` | RequirementOptional | Values & limits Model-specific | Default— |
| Field`modalities` | Type`string[]` | RequirementOptional | Values & limits text | Default— |
| Field`provider` | Type`object` | RequirementOptional | Values & limits Conditions: provider.options is rejected on this Ultra text route | Default— |
| Field`cache_control.ttl` | Type`string` | RequirementOptional | Values & limits Conditions: 1h is rejected in actual prompt-cache locations | Default— |

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, temperature, top\_p, stop, max\_tokens, max\_completion\_tokens, reasoning, response\_format, tools, tool\_choice |
| Parameter`control_scope` | Contractmodel-specific; user function tools differ from hosted tools |
| Parameter`output_modalities` | Contracttext |
| Parameter`hosted_operations` | Contractfalse |
| Parameter`multimodal_input_binding` | Contractnull |

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

| Configuration | Rate schedule | Charge |
| --- | --- | --- |
| ConfigurationBase token rates | Rate scheduleBase rate | Charge Input · Input tokens **$0.0002** / 1K tokens Output · Output tokens **$0.0002** / 1K tokens Input · Cache read **$0** / 1K tokens Input · Cache write **$0** / 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 SDKPython stream

```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": "deepseek-ai/DeepSeek-R1-Distill-Qwen-14B", "messages": [{"role": "user", "content": "Explain the difference between an observation and a hypothesis in three sentences."}]}')

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='deepseek-ai/DeepSeek-R1-Distill-Qwen-14B',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}])
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"))
```

```python
from openai import OpenAI
import os
from pathlib import Path

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)
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))

with client.chat.completions.stream(model='deepseek-ai/DeepSeek-R1-Distill-Qwen-14B',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}]) as stream:
    with Path("stream-events.jsonl").open("w",encoding="utf-8") as log:
        for event in stream:
            payload=event.to_dict(mode="json")
            log.write(json.dumps(payload,ensure_ascii=False)+"\n"); log.flush()
            print(json.dumps(payload,ensure_ascii=False))
    final=stream.get_final_completion()
    save_result(final.to_dict(mode="json"),"stream-result")
```

### 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** [`deepseek-ai/DeepSeek-R1-Distill-Qwen-14B`](https://ailoot.ai/models/en/#m=deepseek-ai%2FDeepSeek-R1-Distill-Qwen-14B&route=Ultra&caller=deepseek-ai%2FDeepSeek-R1-Distill-Qwen-14B&fmt=chat)

#### Method path aliases

Use the method paths shown above.

#### Related caller IDs

[`deepseek-ai/DeepSeek-R1-Distill-Qwen-14B`](https://ailoot.ai/models/en/#m=deepseek-ai%2FDeepSeek-R1-Distill-Qwen-14B&route=Ultra&caller=deepseek-ai%2FDeepSeek-R1-Distill-Qwen-14B&fmt=chat)

**Ailoot Ultra · Anthropic Messages** `POST /v1/messages` `deepseek-ai/DeepSeek-R1-Distill-Qwen-14B`

[`deepseek-ai/DeepSeek-R1-Distill-Qwen-14B`](https://ailoot.ai/models/en/#m=deepseek-ai%2FDeepSeek-R1-Distill-Qwen-14B&route=Ultra&caller=deepseek-ai%2FDeepSeek-R1-Distill-Qwen-14B&fmt=messages)

DeepSeek /`deepseek-ai/DeepSeek-R1-Distill-Qwen-14B`

### Overview

DeepSeek-R1-Distill-Qwen-14B is a text reasoning checkpoint based on Qwen2.5-14B and examples generated by DeepSeek-R1. It supports worked mathematical, programming and logical answers, with DeepSeek's own tokenizer, prompting recommendations and release configuration.

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 deepseek-ai/DeepSeek-R1-Distill-Qwen-14B | 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

| Configuration | Rate schedule | Charge |
| --- | --- | --- |
| ConfigurationBase token rates | Rate scheduleBase rate | Charge Input · Input tokens **$0.0002** / 1K tokens Output · Output tokens **$0.0002** / 1K tokens Input · Cache read **$0** / 1K tokens Input · Cache write **$0** / 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": "deepseek-ai/DeepSeek-R1-Distill-Qwen-14B",
  "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': 'deepseek-ai/DeepSeek-R1-Distill-Qwen-14B', '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': 'deepseek-ai/DeepSeek-R1-Distill-Qwen-14B', '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** [`deepseek-ai/DeepSeek-R1-Distill-Qwen-14B`](https://ailoot.ai/models/en/#m=deepseek-ai%2FDeepSeek-R1-Distill-Qwen-14B&route=Ultra&caller=deepseek-ai%2FDeepSeek-R1-Distill-Qwen-14B&fmt=messages)

#### Method path aliases

Use the method paths shown above.

#### Related caller IDs

[`deepseek-ai/DeepSeek-R1-Distill-Qwen-14B`](https://ailoot.ai/models/en/#m=deepseek-ai%2FDeepSeek-R1-Distill-Qwen-14B&route=Ultra&caller=deepseek-ai%2FDeepSeek-R1-Distill-Qwen-14B&fmt=messages)

FAQ · deepseek-ai/DeepSeek-R1-Distill-Qwen-14B

What inputs and results are available?

`POST /v1/chat/completions` · `deepseek-ai/DeepSeek-R1-Distill-Qwen-14B`: `text` → `text` `POST /v1/messages` · `deepseek-ai/DeepSeek-R1-Distill-Qwen-14B`: `text` → `json`, `sse`

Which fields does the request use?

`POST /v1/chat/completions` · `deepseek-ai/DeepSeek-R1-Distill-Qwen-14B`: `model`, `messages`, `stream`, `modalities`, `provider` `POST /v1/messages` · `deepseek-ai/DeepSeek-R1-Distill-Qwen-14B`: `model`, `messages`, `max_tokens`, `stream`

What limits apply?

`POST /v1/chat/completions` · `deepseek-ai/DeepSeek-R1-Distill-Qwen-14B`: `messages.min_items`: `1`

Connect Ailoot to Claude Code, OpenCode, Hermes or your own app.

## The company and its project portfolio

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

## Buy tokens at a discount.

Subscription discounts for regular use. Promo codes offer up to 50% off token purchases.

Subscription

Choose a discounted plan for your regular requests.

Promo code: up to 50% off

Enter your code at checkout. The price updates to reflect the current promotion.

Spending in your history

The model, tokens and amount are recorded for each request.

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## Connect the API to a product you sell.

Use Ailoot in SaaS, apps and bots under its official service offer and licence agreement. Request a reseller licence for resale.

Commercial application

Add text, image, video and audio generation to paid features in your service.

Reseller licence

Send a description of your project and how you provide access. Request written terms for use and resale.

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The service offer, licence agreement and data processing policy are available before you connect.

[Request a reseller licence](mailto:support@ailoot.ai) · [Service offer and licence agreement](https://ailoot.ai/legal/offer?lang=en)

## 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.





Ailoot brings that operating experience to API access for AI features in customer products, agents, applications and automations.