Source: https://ailoot.ai/models/en/qwq-plus/
Canonical: https://ailoot.ai/models/en/qwq-plus/
Language: en

# QwQ-Plus

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

## [QwQ-Plus](https://ailoot.ai/models/en/qwq-plus/)

QwQ-Plus is Qwen’s Qwen2.5-based reasoning model, strengthened through reinforcement learning for mathematical, coding and logical text tasks. Its dedicated specification lists a 131,072-token context, 98,304-token maximum input and 8,192-token maximum output.

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

Overview: Qwen2.5-based reasoning

QwQ-Plus is the enhanced QwQ reasoning model built on Qwen2.5. Qwen describes reinforcement learning as the method used to strengthen its reasoning, particularly in mathematics and programming, together with general instruction-following tasks. It receives text and returns text. Select it when the work calls for an explained solution, comparison or code proposal that can be checked against the supplied conditions.

This is the QwQ-Plus model line. Its name does not establish the parameter count or weight identity of an open QwQ checkpoint, and a supplier name containing latest should not silently become a fixed dated revision. Use the exact requested model identity when comparing outputs or keeping a reproducible record of a task.

Preparing mathematical and logical work

Give the complete problem statement, definitions, units and allowed assumptions. Ask for the final answer in a clearly delimited form, then request the equations or logical steps needed to support it. For a constrained planning problem, specify the feasible choices and evaluation criteria separately, and request a table showing which conditions each choice satisfies. For an educational review, include the learner’s attempted solution and ask the model to identify the first unsupported step rather than rewrite the entire exercise without explanation.

These are practical applications of the model’s documented reasoning focus. The useful artifact is a checkable result: a value with units, a conclusion with premises, or a comparison tied to the stated criteria. More explanatory text does not by itself establish a correct conclusion, so keep assumptions visible and compare the final result with the original question.

Preparing programming work

Provide the relevant code, the input that exposes the problem, the observed output and the desired behavior. Ask for a proposed change, an explanation of why it addresses that input, and edge cases that still need checking. For an algorithm question, specify the input constraints and required output before asking for an approach. Distinguish generated code from code that has actually been executed in the application’s environment.

A useful response can separate the diagnosis, patch and expected behavior. When a small change is required, request a compact patch rather than a rewritten project. These examples are authored ways to apply QwQ-Plus’s mathematical and coding specialization; they are not an extra tool or execution capability.

Context and answer limits

The exact model specification gives a 131,072-token context window, a maximum input of 98,304 tokens and a maximum output of 8,192 tokens. These are separate producer limits. Do not turn the arithmetic difference between them into an undocumented guaranteed reasoning allowance. Source text, conversation history and the response must remain within the applicable configured bounds.

For a long specification or code excerpt, retain relevant section and function identifiers and ask the model to reference them in its explanation. Remove unrelated material before submission and reserve enough output space for the required conclusion. A large context window is useful for supplying more conditions together; it does not promise that every fact will be recalled or every dependency understood.

Tools, formatting and model-specific boundaries

The manufacturer’s deployment matrix lists function calling and web search in some configurations and marks them unsupported in another. They are therefore conditional capabilities, not features that can be promised from the model name alone. The same matrix supports batch inference in some manufacturer configurations. A tool-assisted workflow must provide the actual tools and feed their results back into the text task. This description does not establish an application’s tool availability.

Enforced structured outputs are marked unsupported. A prompt can still request a table or a JSON-shaped answer, but the application must inspect the result rather than treat that request as schema enforcement. The exact model page describes a reasoning specialization without supplying a per-model hybrid thinking-switch contract; controls documented for newer Qwen generations should not be imported into this edition.

Keep the final answer easy to review, check calculations against the stated units and inspect code proposals before execution. QwQ-Plus’s practical role is to work through a difficult text problem and return a useful explanation or artifact within its own documented limits.

Practical task

### Review a learner calculation

Technical example in English

```
Review a learner calculation

Review this learner solution: "A 30 percent discount on 80 gives 80 - 30 = 50." Identify the first incorrect step, explain the correct percentage operation and calculate the final price. Give a short check by adding the discount back to the sale price. Use only these numbers and no tax assumption.
```

#### Expected result

```
A concise correction identifying the unsupported subtraction, the calculation and a labelled final price.
```

#### Check the result

```
Verify the discount is 30 percent of 80 and the price plus discount returns 80.
```

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

[`qwq-plus`](https://ailoot.ai/models/en/#m=qwq-plus&route=Ultra&caller=qwq-plus&fmt=chat)

Alibaba /`qwq-plus`

### Overview

QwQ-Plus is Qwen’s Qwen2.5-based reasoning model, strengthened through reinforcement learning for mathematical, coding and logical text tasks. Its dedicated specification lists a 131,072-token context, 98,304-token maximum input and 8,192-token maximum output.

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 qwq-plus | 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 **$0.00046** / 1K tokens Output · Output tokens **$0.00116** / 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": "qwq-plus", "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='qwq-plus',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='qwq-plus',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** [`qwq-plus`](https://ailoot.ai/models/en/#m=qwq-plus&route=Ultra&caller=qwq-plus&fmt=chat)

#### Method path aliases

Use the method paths shown above.

#### Related caller IDs

[`qwq-plus`](https://ailoot.ai/models/en/#m=qwq-plus&route=Ultra&caller=qwq-plus&fmt=chat)

**Ailoot Ultra · Anthropic Messages** `POST /v1/messages` `qwq-plus`

[`qwq-plus`](https://ailoot.ai/models/en/#m=qwq-plus&route=Ultra&caller=qwq-plus&fmt=messages)

Alibaba /`qwq-plus`

### Overview

QwQ-Plus is Qwen’s Qwen2.5-based reasoning model, strengthened through reinforcement learning for mathematical, coding and logical text tasks. Its dedicated specification lists a 131,072-token context, 98,304-token maximum input and 8,192-token maximum output.

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 qwq-plus | 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 **$0.00046** / 1K tokens Output · Output tokens **$0.00116** / 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": "qwq-plus",
  "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': 'qwq-plus', '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': 'qwq-plus', '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** [`qwq-plus`](https://ailoot.ai/models/en/#m=qwq-plus&route=Ultra&caller=qwq-plus&fmt=messages)

#### Method path aliases

Use the method paths shown above.

#### Related caller IDs

[`qwq-plus`](https://ailoot.ai/models/en/#m=qwq-plus&route=Ultra&caller=qwq-plus&fmt=messages)

FAQ · QwQ-Plus

What inputs and results are available?

`POST /v1/chat/completions` · `qwq-plus`: `text` → `text` `POST /v1/messages` · `qwq-plus`: `text` → `json`, `sse`

Which fields does the request use?

`POST /v1/chat/completions` · `qwq-plus`: `model`, `messages`, `stream`, `modalities`, `provider` `POST /v1/messages` · `qwq-plus`: `model`, `messages`, `max_tokens`, `stream`

What limits apply?

`POST /v1/chat/completions` · `qwq-plus`: `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.