Source: https://ailoot.ai/models/en/deepseek-ai-deepseek-r1-distill-qwen-32b/
Canonical: https://ailoot.ai/models/en/deepseek-ai-deepseek-r1-distill-qwen-32b/
Language: en

# DeepSeek R1 Distill Qwen 32B

[Markdown](https://ailoot.ai/models/en/deepseek-ai-deepseek-r1-distill-qwen-32b/index.md) · [JSON](https://ailoot.ai/models/en/deepseek-ai-deepseek-r1-distill-qwen-32b/facts.json)

## [DeepSeek R1 Distill Qwen 32B](https://ailoot.ai/models/en/deepseek-ai-deepseek-r1-distill-qwen-32b/)

DeepSeek-R1-Distill-Qwen-32B is a dense text reasoning model fine-tuned from Qwen2.5-32B with DeepSeek-R1 examples. It is intended for multi-step mathematical, coding and logical tasks, with maker-specific prompting and a 131,072-position release configuration.

- [**Ailoot Ultra · OpenAI Chat** `POST /v1/chat/completions` `deepseek-ai/DeepSeek-R1-Distill-Qwen-32B`](https://ailoot.ai/models/en/deepseek-ai-deepseek-r1-distill-qwen-32b/#method-0)
- [**Ailoot Ultra · Anthropic Messages** `POST /v1/messages` `deepseek-ai/DeepSeek-R1-Distill-Qwen-32B`](https://ailoot.ai/models/en/deepseek-ai-deepseek-r1-distill-qwen-32b/#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-32B is DeepSeek's 32B-size dense reasoning checkpoint built from Qwen2.5-32B. DeepSeek fine-tunes the base with reasoning examples generated by DeepSeek-R1. The result is a distinct text model for problems that require several connected steps, such as a mathematical derivation, an algorithmic question or reasoning through constraints.

How this distillation differs from the full R1 model

The maker's release separates DeepSeek-R1-Zero, DeepSeek-R1 and distilled models based on Qwen or Llama. The full R1 line develops reasoning through its own training pipeline; the distillation stage transfers patterns represented in R1-generated examples into separate dense checkpoints. This entry is the Qwen-based 32B distill edition, not the full R1 model, R1-Zero or the unmodified Qwen2.5-32B base.

The released Qwen distill models are fine-tuned with 800,000 curated samples. DeepSeek also changes their configurations and tokenizers and instructs users to keep those settings. The training origin explains the reasoning focus, while the named checkpoint and its own configuration determine the actual release. It does not mean that the larger teacher model is automatically consulted to answer each new question.

The 32B checkpoint's configuration uses a causal language-model architecture with 64 layers, 40 query attention heads and 8 key/value heads. It sets maximum positions to 131,072. This configuration figure concerns the released model; an example serving command or another member of the R1 family should not be treated as the same context setting. The model receives text and generates text rather than accepting an image or producing a visual or audio artifact.

Preparing a mathematical or coding task

For a mathematical problem, give the complete condition, define the variables and state the domain or assumptions. Identify whether the final result should be an exact expression, a numerical approximation or a proof of a claim. A useful request can ask for a derivation followed by the final expression and a check against the original conditions. The maker recommends a worked mathematical response with the final answer enclosed in a LaTeX box.

For an algorithm problem, provide the input range, required output and constraints on the solution. Ask for the approach, the implementation and an explanation of why it preserves the required property. If the problem concerns existing code, supply the relevant function, the input that produces the unexpected behavior and the expected result. The response can compare the actual path with the intended one and propose a correction; execution and verification of the program are separate application steps.

For a constraint problem, list the permitted choices and relationships explicitly. For example, a scheduling request can give the participants, availability and rules that make two assignments incompatible. Ask for a proposed assignment with the rule supporting each decision, and require any unresolved condition to remain visible. This makes the reasoning task inspectable instead of asking the model to choose without the underlying constraints.

Maker-specific usage recommendations

DeepSeek recommends putting all instructions in the user prompt and avoiding a separate system prompt for this series. This differs from a general chat template that relies on a system message to define the task. Place the requested answer format, the source material and the actual question together with clear boundaries.

The recommended temperature range is 0.5 to 0.7, with 0.6 preferred. The maker relates that recommendation to avoiding repetition or incoherent output. It also notes that some queries can produce an empty reasoning block and recommends beginning the output with the opening reasoning marker. These are recommendations for the released reasoning workflow, not a guarantee that one setting establishes correctness.

A practical answer contract can request an explanation of the method, the important justified steps and a separate final artifact. In a mathematical task, use the boxed expression for the final result. In a coding task, separate the code from its assumptions and example inputs. For a logical task, keep each conclusion connected to the rule or supplied fact that supports it. This allows the reasoning text and the result needed by the application to serve distinct purposes.

Context, output and integration boundaries

The released configuration's 131,072 maximum positions should be considered together with the full prompt and generated reasoning. The maker uses a 32,768-token generation setting in evaluation, but that experimental setting is not a published, separate output-capacity specification for this checkpoint. Do not derive an answer ceiling from a benchmark setup or copy the 8,192-token output figure of the original Qwen Instruct edition into the modified distill release.

Keep the inputs focused on the actual mathematical, programming or logical question, and state the desired final form. An external calculator, repository, document parser or search function needs to be supplied by the surrounding application; it is not included merely by selecting a text reasoning checkpoint. The useful result is a written solution or proposed implementation whose assumptions, method and final answer correspond to the supplied problem.

Practical task

### Solve a bounded scheduling problem

Technical example in English

```
Solve a bounded scheduling problem

Schedule three one-hour interviews in slots 09:00, 10:00 and 11:00. Ada cannot attend 09:00; Ben cannot attend 11:00; Chen must be before Ada. Each person gets one distinct slot. Give a valid schedule, explain how each constraint is satisfied and state whether another schedule is possible.
```

#### Expected result

```
A person-to-slot table, a constraint check and a justified uniqueness or non-uniqueness conclusion.
```

#### Check the result

```
Check all three constraints and distinct slots directly against the proposed schedule.
```

**Ailoot Ultra · OpenAI Chat** `POST /v1/chat/completions` `deepseek-ai/DeepSeek-R1-Distill-Qwen-32B`

[`deepseek-ai/DeepSeek-R1-Distill-Qwen-32B`](https://ailoot.ai/models/en/#m=deepseek-ai%2FDeepSeek-R1-Distill-Qwen-32B&route=Ultra&caller=deepseek-ai%2FDeepSeek-R1-Distill-Qwen-32B&fmt=chat)

DeepSeek /`deepseek-ai/DeepSeek-R1-Distill-Qwen-32B`

### Overview

DeepSeek-R1-Distill-Qwen-32B is a dense text reasoning model fine-tuned from Qwen2.5-32B with DeepSeek-R1 examples. It is intended for multi-step mathematical, coding and logical tasks, with maker-specific prompting and a 131,072-position 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-32B | 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 **$0.00036** / 1K tokens Output · Output tokens **$0.00036** / 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-32B", "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-32B',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-32B',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-32B`](https://ailoot.ai/models/en/#m=deepseek-ai%2FDeepSeek-R1-Distill-Qwen-32B&route=Ultra&caller=deepseek-ai%2FDeepSeek-R1-Distill-Qwen-32B&fmt=chat)

#### Method path aliases

Use the method paths shown above.

#### Related caller IDs

[`deepseek-ai/DeepSeek-R1-Distill-Qwen-32B`](https://ailoot.ai/models/en/#m=deepseek-ai%2FDeepSeek-R1-Distill-Qwen-32B&route=Ultra&caller=deepseek-ai%2FDeepSeek-R1-Distill-Qwen-32B&fmt=chat)

**Ailoot Ultra · Anthropic Messages** `POST /v1/messages` `deepseek-ai/DeepSeek-R1-Distill-Qwen-32B`

[`deepseek-ai/DeepSeek-R1-Distill-Qwen-32B`](https://ailoot.ai/models/en/#m=deepseek-ai%2FDeepSeek-R1-Distill-Qwen-32B&route=Ultra&caller=deepseek-ai%2FDeepSeek-R1-Distill-Qwen-32B&fmt=messages)

DeepSeek /`deepseek-ai/DeepSeek-R1-Distill-Qwen-32B`

### Overview

DeepSeek-R1-Distill-Qwen-32B is a dense text reasoning model fine-tuned from Qwen2.5-32B with DeepSeek-R1 examples. It is intended for multi-step mathematical, coding and logical tasks, with maker-specific prompting and a 131,072-position 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-32B | 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 **$0.00036** / 1K tokens Output · Output tokens **$0.00036** / 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-32B",
  "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-32B', '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-32B', '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-32B`](https://ailoot.ai/models/en/#m=deepseek-ai%2FDeepSeek-R1-Distill-Qwen-32B&route=Ultra&caller=deepseek-ai%2FDeepSeek-R1-Distill-Qwen-32B&fmt=messages)

#### Method path aliases

Use the method paths shown above.

#### Related caller IDs

[`deepseek-ai/DeepSeek-R1-Distill-Qwen-32B`](https://ailoot.ai/models/en/#m=deepseek-ai%2FDeepSeek-R1-Distill-Qwen-32B&route=Ultra&caller=deepseek-ai%2FDeepSeek-R1-Distill-Qwen-32B&fmt=messages)

FAQ · deepseek-ai/DeepSeek-R1-Distill-Qwen-32B

What inputs and results are available?

`POST /v1/chat/completions` · `deepseek-ai/DeepSeek-R1-Distill-Qwen-32B`: `text` → `text` `POST /v1/messages` · `deepseek-ai/DeepSeek-R1-Distill-Qwen-32B`: `text` → `json`, `sse`

Which fields does the request use?

`POST /v1/chat/completions` · `deepseek-ai/DeepSeek-R1-Distill-Qwen-32B`: `model`, `messages`, `stream`, `modalities`, `provider` `POST /v1/messages` · `deepseek-ai/DeepSeek-R1-Distill-Qwen-32B`: `model`, `messages`, `max_tokens`, `stream`

What limits apply?

`POST /v1/chat/completions` · `deepseek-ai/DeepSeek-R1-Distill-Qwen-32B`: `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.

[Choose a subscription](https://ailoot.ai/docs/en/billing/subscriptions/) · [View model prices](https://ailoot.ai/models/en/)

## 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.

Documents on the website

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.