Source: https://ailoot.ai/models/en/qwen-qwen3-next-80b-a3b-instruct/
Canonical: https://ailoot.ai/models/en/qwen-qwen3-next-80b-a3b-instruct/
Language: en

# Qwen: Qwen3 Next 80B A3B Instruct

[Markdown](https://ailoot.ai/models/en/qwen-qwen3-next-80b-a3b-instruct/index.md) · [JSON](https://ailoot.ai/models/en/qwen-qwen3-next-80b-a3b-instruct/facts.json)

## [Qwen: Qwen3 Next 80B A3B Instruct](https://ailoot.ai/models/en/qwen-qwen3-next-80b-a3b-instruct/)

Qwen3-Next-80B-A3B-Instruct is Qwen’s non-thinking text edition with 80 billion total and 3 billion activated parameters. It combines hybrid attention, sparse experts and tool calling with a native 262,144-token context, extendable to 1,010,000 through configuration.

- [**Ailoot Ultra · OpenAI Chat** `POST /v1/chat/completions` `qwen/qwen3-next-80b-a3b-instruct`](https://ailoot.ai/models/en/qwen-qwen3-next-80b-a3b-instruct/#method-0)
- [**Ailoot Ultra · Anthropic Messages** `POST /v1/messages` `qwen/qwen3-next-80b-a3b-instruct`](https://ailoot.ai/models/en/qwen-qwen3-next-80b-a3b-instruct/#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.

Qwen3-Next highlights: direct text instructions

Qwen3-Next-80B-A3B-Instruct is the instruct, non-thinking text edition of Qwen3-Next. It accepts text and returns text, including explanations, code proposals and requested document artifacts. It does not generate explicit thinking blocks. Keep it distinct from the Thinking edition and the coding-specialized Qwen3-Coder-Next; sharing a family architecture does not make their response contracts interchangeable.

The family introduces hybrid attention combining Gated DeltaNet and gated attention, high-sparsity experts, stability optimizations including zero-centered and weight-decayed layer normalization, and multi-token prediction. Those changes address the representation and training of long sequences. Their inference efficiency depends on implementation, so architecture figures should not become a fixed latency or throughput guarantee.

Model overview

The checkpoint has 80 billion parameters in total and activates three billion. It has 48 layers in a repeated three-DeltaNet-to-one-gated-attention expert pattern. The maker lists 512 experts, ten activated experts and one shared expert, with pretraining on 15 trillion tokens followed by post-training. Activated parameters are not the size of the complete checkpoint.

The native context is 262,144 tokens, with documented extension to 1,010,000. The maker’s deployment examples allow a smaller context, such as 32,768, when resource constraints prevent the larger setting from working. Text, conversation history, tool observations and generated content share the configured sequence allowance. The extension is an additional configuration path rather than a default unlimited input.

Using the instruct interface

This model supports only instruct, non-thinking mode. It can explain an algorithm, show calculations or justify a conclusion in ordinary text without producing a dedicated thinking block. Ask for the artifact’s format and boundaries: a complete function, a concise report, an evidence table or an answer with supporting steps. A request for a long explanation does not turn it into the separately named Thinking checkpoint.

For a document task, preserve section identifiers and define the questions the report must answer. For code, provide the relevant function or module, input constraints and expected output. For mathematics, give the complete statement and request a delimited final result. Compare the returned calculations, code or explanation with the supplied conditions and actual results.

Implementation and tools

The maker documents tool calling and recommends Qwen-Agent for templates and parsing, with configured or MCP-connected tools. A connected workflow can provide retrieval, inspection or permitted execution and return actual observations. State the tools, allowed actions and visible completion conditions, then ask the final report to distinguish proposals from real returned results.

Multi-token prediction is part of training, but its inference use is implementation-dependent and is not generally available in every lightweight workflow. A template or architecture feature does not itself establish access to current sources, files or a code environment. Preserve the exact model and conversation format used by the chosen interface.

Ultra-long text

The maker validates approximately one-million-token context with YaRN and gives a factor-of-four recipe based on 262,144 native positions. For 524,288-token workloads, it suggests a factor of two. Static scaling can lower shorter-text performance, so configuration should follow the actual need for longer input.

Keep long material organized by document, code path or section and remove unrelated repetitions. Ask the answer to identify its supporting passages or conditions. Reserve space for the response rather than treat the largest context number as complete recall of every supplied detail. The published 1,010,000 extension figure and the validation around one million remain scoped to the maker’s long-context workflow.

Generation guidance

Best Practices recommend temperature 0.7, top\_p 0.8, top\_k 20 and min\_p 0. Where supported, a presence penalty between zero and two can reduce repeated text, but higher values may mix languages or slightly reduce quality. The maker recommends a 16,384-token output allowance for most queries. This is a recommended generation budget rather than an independent hard maximum; it should not inherit the Thinking edition’s longer allowances.

Specify the result form explicitly, such as a mathematical delimiter or an answer field. Keep the final artifact easy to inspect and verify its calculations, quotations or code against the source. This text-only edition does not accept images, audio or video as its own input, so those materials need an appropriate separate representation before they can support the task.

Practical result

The useful output is a concrete text artifact grounded in the supplied conditions: a document explanation, code proposal or direct answer with the required supporting detail. Long context and tool calling can help carry more evidence through a workflow, while the non-thinking contract keeps this exact edition’s output style clear. Check the result in the real task environment rather than infer correctness from its length or confident wording.

Practical task

### Explain a section-specific decision

Technical example in English

```
Explain a section-specific decision

Answer only from these procedure sections. Section 2: standard purchases need a manager's approval. Section 4: emergency safety purchases may proceed first, but must be reported to that manager within one day. Case: a broken emergency exit light needs replacement today. Give a concise recommendation with section references and identify the follow-up obligation. Do not invent an approval already granted.
```

#### Expected result

```
A direct recommendation with supporting sections and the one-day reporting obligation.
```

#### Check the result

```
The emergency exception retains its reporting condition and is not generalised to all purchases.
```

**Ailoot Ultra · OpenAI Chat** `POST /v1/chat/completions` `qwen/qwen3-next-80b-a3b-instruct`

[`qwen/qwen3-next-80b-a3b-instruct`](https://ailoot.ai/models/en/#m=qwen%2Fqwen3-next-80b-a3b-instruct&route=Ultra&caller=qwen%2Fqwen3-next-80b-a3b-instruct&fmt=chat)

Alibaba /`qwen/qwen3-next-80b-a3b-instruct`

### Overview

Qwen3-Next-80B-A3B-Instruct is Qwen’s non-thinking text edition with 80 billion total and 3 billion activated parameters. It combines hybrid attention, sparse experts and tool calling with a native 262,144-token context, extendable to 1,010,000 through 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 qwen/qwen3-next-80b-a3b-instruct | 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

Standard rate When fast mode is selected

Flex mode

Standard rate When flex mode is selected

Maximum reasoning effort

Standard rate When maximum reasoning effort uses the default adjustment

#### Ailoot rates

#### Prices by configuration

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

| Configuration | Rate schedule | Context tokens, more than | Context tokens, up to and including | Charge |
| --- | --- | --- | --- | --- |
| ConfigurationBase token rates | Rate scheduleBase rate | Context tokens, more than— | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens **$0.000286** / 1K tokens Output · Output tokens **$0.0022** / 1K tokens Input · Cache read **$0.00014** / 1K tokens Input · Cache write **$0** / 1K tokens Input · Cache write, 1 hour **—** / 1K tokens |
| ConfigurationRequest-length tier 1 | Rate schedule— | Context tokens, more than0 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens **$0.000286** / 1K tokens Output · Output tokens **$0.0022** / 1K tokens Input · Cache read **$0.00014** / 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": "qwen/qwen3-next-80b-a3b-instruct", "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='qwen/qwen3-next-80b-a3b-instruct',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='qwen/qwen3-next-80b-a3b-instruct',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** [`qwen/qwen3-next-80b-a3b-instruct`](https://ailoot.ai/models/en/#m=qwen%2Fqwen3-next-80b-a3b-instruct&route=Ultra&caller=qwen%2Fqwen3-next-80b-a3b-instruct&fmt=chat)

#### Method path aliases

Use the method paths shown above.

#### Related caller IDs

[`qwen/qwen3-next-80b-a3b-instruct`](https://ailoot.ai/models/en/#m=qwen%2Fqwen3-next-80b-a3b-instruct&route=Ultra&caller=qwen%2Fqwen3-next-80b-a3b-instruct&fmt=chat)

**Ailoot Ultra · Anthropic Messages** `POST /v1/messages` `qwen/qwen3-next-80b-a3b-instruct`

[`qwen/qwen3-next-80b-a3b-instruct`](https://ailoot.ai/models/en/#m=qwen%2Fqwen3-next-80b-a3b-instruct&route=Ultra&caller=qwen%2Fqwen3-next-80b-a3b-instruct&fmt=messages)

Alibaba /`qwen/qwen3-next-80b-a3b-instruct`

### Overview

Qwen3-Next-80B-A3B-Instruct is Qwen’s non-thinking text edition with 80 billion total and 3 billion activated parameters. It combines hybrid attention, sparse experts and tool calling with a native 262,144-token context, extendable to 1,010,000 through 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 qwen/qwen3-next-80b-a3b-instruct | 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

Standard rate When fast mode is selected

Flex mode

Standard rate When flex mode is selected

Maximum reasoning effort

Standard rate When maximum reasoning effort uses the default adjustment

#### Ailoot rates

#### Prices by configuration

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

| Configuration | Rate schedule | Context tokens, more than | Context tokens, up to and including | Charge |
| --- | --- | --- | --- | --- |
| ConfigurationBase token rates | Rate scheduleBase rate | Context tokens, more than— | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens **$0.000286** / 1K tokens Output · Output tokens **$0.0022** / 1K tokens Input · Cache read **$0.00014** / 1K tokens Input · Cache write **$0** / 1K tokens Input · Cache write, 1 hour **—** / 1K tokens |
| ConfigurationRequest-length tier 1 | Rate schedule— | Context tokens, more than0 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens **$0.000286** / 1K tokens Output · Output tokens **$0.0022** / 1K tokens Input · Cache read **$0.00014** / 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": "qwen/qwen3-next-80b-a3b-instruct",
  "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': 'qwen/qwen3-next-80b-a3b-instruct', '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': 'qwen/qwen3-next-80b-a3b-instruct', '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** [`qwen/qwen3-next-80b-a3b-instruct`](https://ailoot.ai/models/en/#m=qwen%2Fqwen3-next-80b-a3b-instruct&route=Ultra&caller=qwen%2Fqwen3-next-80b-a3b-instruct&fmt=messages)

#### Method path aliases

Use the method paths shown above.

#### Related caller IDs

[`qwen/qwen3-next-80b-a3b-instruct`](https://ailoot.ai/models/en/#m=qwen%2Fqwen3-next-80b-a3b-instruct&route=Ultra&caller=qwen%2Fqwen3-next-80b-a3b-instruct&fmt=messages)

FAQ · Qwen: Qwen3 Next 80B A3B Instruct

What inputs and results are available?

`POST /v1/chat/completions` · `qwen/qwen3-next-80b-a3b-instruct`: `text` → `text` `POST /v1/messages` · `qwen/qwen3-next-80b-a3b-instruct`: `text` → `json`, `sse`

Which fields does the request use?

`POST /v1/chat/completions` · `qwen/qwen3-next-80b-a3b-instruct`: `model`, `messages`, `stream`, `modalities`, `provider` `POST /v1/messages` · `qwen/qwen3-next-80b-a3b-instruct`: `model`, `messages`, `max_tokens`, `stream`

What limits apply?

`POST /v1/chat/completions` · `qwen/qwen3-next-80b-a3b-instruct`: `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.