Source: https://ailoot.ai/models/en/qwen-qwen3-vl-30b-a3b-instruct/ Canonical: https://ailoot.ai/models/en/qwen-qwen3-vl-30b-a3b-instruct/ Language: en # Qwen: Qwen3 VL 30B A3B Instruct [Markdown](https://ailoot.ai/models/en/qwen-qwen3-vl-30b-a3b-instruct/index.md) · [JSON](https://ailoot.ai/models/en/qwen-qwen3-vl-30b-a3b-instruct/facts.json) ## [Qwen: Qwen3 VL 30B A3B Instruct](https://ailoot.ai/models/en/qwen-qwen3-vl-30b-a3b-instruct/) Qwen3-VL-30B-A3B-Instruct is Qwen’s instruction-focused mixture-of-experts vision-language edition for text, images and video. It returns text or code for visual-agent workflows, document reading, spatial questions and visual coding, with 32-language OCR and a native 262,144-token context. - [**Ailoot Ultra · OpenAI Chat** `POST /v1/chat/completions` `qwen/qwen3-vl-30b-a3b-instruct`](https://ailoot.ai/models/en/qwen-qwen3-vl-30b-a3b-instruct/#method-0) - [**Ailoot Ultra · Anthropic Messages** `POST /v1/messages` `qwen/qwen3-vl-30b-a3b-instruct`](https://ailoot.ai/models/en/qwen-qwen3-vl-30b-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. The 30B-A3B Instruct edition Qwen3-VL-30B-A3B-Instruct is the instruction-focused mixture-of-experts edition of Qwen3-VL. It combines text with images or video and produces text, including code. Preserve this exact Instruct identity rather than substituting the reasoning-enhanced Thinking edition or another family size. A useful request defines the visual evidence, the question and the concrete artifact expected in the answer. Visual agents and visual coding The maker describes recognizing computer or mobile interface elements, understanding their functions and invoking tools in a visual-agent workflow. Provide the screenshot and intended goal, then ask which visible control corresponds to the task and what supports the choice. The connected application executes actions and returns their results; visual recognition alone does not control the device. Visual coding includes diagram content and HTML, CSS or JavaScript generated from images or video. Supply a layout reference with functional constraints and request editable source. For a change to an existing interface, include the relevant code and expected interaction states. Separate the artifact from the explanation so a development workflow can render and inspect it. Generated source is a proposal to review, not proof of a working application. Spatial perception, context and video Qwen documents judgments about object positions, viewpoints and occlusions, with two-dimensional grounding and support for three-dimensional grounding. Identify the target and ask for the visible cues supporting the relationship. An image-based relationship should remain distinct from a calibrated physical measurement. The native context is 262,144 tokens. The family describes books and hours-long video, with second-level event indexing and expansion to about one million tokens through configuration. Preserve timing and frame order when asking about a sequence. The configured representation budget determines what material is processed, so the family’s broad long-video description should not become a promise of perfect recall of every frame. Multimodal reasoning, recognition and OCR The model supports STEM and mathematical visual questions, causal analysis and evidence-based explanations. Provide the complete statement and diagram labels and ask for the values used before the final conclusion. The family also expands recognition across people, products, landmarks, plants, animals and media characters. Check an identification result when the exact identity matters. OCR covers 32 languages and is designed to improve reading under low light, blur or tilt, including specialist vocabulary and document structure. Supply readable pages and request fields, quotations or a table that can be checked against the source. Text and visual understanding are combined, so the question can connect a passage with its figure and the user’s instructions. Architecture The published configuration identifies a vision-language mixture-of-experts model with 48 text layers, 128 experts and eight experts selected per token. Interleaved-MRoPE represents time, width and height; DeepStack combines features from multiple visual-encoder levels; text–timestamp alignment grounds events in temporal references. Use this 30B-A3B Instruct edition consistently when preparing visual tasks and comparing their results. Preparing inputs The family examples cover text-only tasks, single or multiple images and video. Label visual inputs and preserve their relationship. Image and video pixel budgets are separate, with video accounting for pixels across sampled frames. Explicit image dimensions are aligned to multiples of 32. The documented video default is two frames per second, with rate or frame-count controls in supported workflows. The media helper can retain video metadata and advises avoiding a second resize when it has already resized the material. For a document comparison, organize the answer by page label. For a clip, request the event, relevant interval and supporting evidence. Compare the returned fields, passages and event times with the original pages or clip. Generation guidance and limits The checkpoint generation file defaults to temperature 0.7, top\_p 0.8, top\_k 20 and repetition penalty 1.0. The family’s Instruct recipe additionally uses presence penalty 1.5 and a 32,768-token output allowance. That allowance is a producer recipe budget, not an independently established hard output maximum. The 8B Instruct card’s separate task allowances should not be assigned automatically to this edition. The family’s extension procedure uses YaRN, suggesting a factor of two or three when expanding the interleaved positional scheme from 256K to about 1M. Reserve space for the result alongside text and represented visuals, and verify OCR, calculated conclusions and generated code against the original evidence. Keep the Instruct generation contract distinct from a Thinking checkpoint’s settings; an application-specific hybrid toggle is not established by the edition name alone. Practical task ### Generate source from a written layout Technical example in English ``` Generate source from a written layout Produce self-contained HTML and CSS from this written layout: a heading “Upload report”, a file input and a status line below it. On screens narrower than 480 pixels, controls fill the available width. Include empty, selected-file and error text examples, accessible labels and no external assets. Return editable source with a short state explanation. ``` #### Expected result ``` An HTML/CSS source artifact and a short explanation of the three requested states. ``` #### Check the result ``` Check that the file input has an associated label, narrow layouts fit their container and every requested state has distinct text. ``` **Ailoot Ultra · OpenAI Chat** `POST /v1/chat/completions` `qwen/qwen3-vl-30b-a3b-instruct` [`qwen/qwen3-vl-30b-a3b-instruct`](https://ailoot.ai/models/en/#m=qwen%2Fqwen3-vl-30b-a3b-instruct&route=Ultra&caller=qwen%2Fqwen3-vl-30b-a3b-instruct&fmt=chat) Alibaba /`qwen/qwen3-vl-30b-a3b-instruct` ### Overview Qwen3-VL-30B-A3B-Instruct is Qwen’s instruction-focused mixture-of-experts vision-language edition for text, images and video. It returns text or code for visual-agent workflows, document reading, spatial questions and visual coding, with 32-language OCR and a native 262,144-token context. 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-vl-30b-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 **$0.0003** / 1K tokens Output · Output tokens **$0.0012** / 1K tokens Input · Cache read **$0** / 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 **$0.0003** / 1K tokens Output · Output tokens **$0.0012** / 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": "qwen/qwen3-vl-30b-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-vl-30b-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-vl-30b-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-vl-30b-a3b-instruct`](https://ailoot.ai/models/en/#m=qwen%2Fqwen3-vl-30b-a3b-instruct&route=Ultra&caller=qwen%2Fqwen3-vl-30b-a3b-instruct&fmt=chat) #### Method path aliases Use the method paths shown above. #### Related caller IDs [`qwen/qwen3-vl-30b-a3b-instruct`](https://ailoot.ai/models/en/#m=qwen%2Fqwen3-vl-30b-a3b-instruct&route=Ultra&caller=qwen%2Fqwen3-vl-30b-a3b-instruct&fmt=chat) **Ailoot Ultra · Anthropic Messages** `POST /v1/messages` `qwen/qwen3-vl-30b-a3b-instruct` [`qwen/qwen3-vl-30b-a3b-instruct`](https://ailoot.ai/models/en/#m=qwen%2Fqwen3-vl-30b-a3b-instruct&route=Ultra&caller=qwen%2Fqwen3-vl-30b-a3b-instruct&fmt=messages) Alibaba /`qwen/qwen3-vl-30b-a3b-instruct` ### Overview Qwen3-VL-30B-A3B-Instruct is Qwen’s instruction-focused mixture-of-experts vision-language edition for text, images and video. It returns text or code for visual-agent workflows, document reading, spatial questions and visual coding, with 32-language OCR and a native 262,144-token context. 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-vl-30b-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 **$0.0003** / 1K tokens Output · Output tokens **$0.0012** / 1K tokens Input · Cache read **$0** / 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 **$0.0003** / 1K tokens Output · Output tokens **$0.0012** / 1K tokens Input · Cache read **$0** / 1K tokens Input · Cache write **$0** / 1K tokens Input · Cache write, 1 hour **—** / 1K tokens | Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. [How reservation and settlement work →](https://ailoot.ai/docs/en/billing/reserve/) ### Example requests #### Ailoot Ultra · Anthropic Messages Complete minimal JSON bodycurl · use your UTF-8 documentPython · save the complete result ```json { "model": "qwen/qwen3-vl-30b-a3b-instruct", "max_tokens": 128, "messages": [ { "role": "user", "content": "Summarize the incident report." } ], "stream": false } ``` ```bash set -eu : "${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-vl-30b-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-vl-30b-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-vl-30b-a3b-instruct`](https://ailoot.ai/models/en/#m=qwen%2Fqwen3-vl-30b-a3b-instruct&route=Ultra&caller=qwen%2Fqwen3-vl-30b-a3b-instruct&fmt=messages) #### Method path aliases Use the method paths shown above. #### Related caller IDs [`qwen/qwen3-vl-30b-a3b-instruct`](https://ailoot.ai/models/en/#m=qwen%2Fqwen3-vl-30b-a3b-instruct&route=Ultra&caller=qwen%2Fqwen3-vl-30b-a3b-instruct&fmt=messages) FAQ · Qwen: Qwen3 VL 30B A3B Instruct What inputs and results are available? `POST /v1/chat/completions` · `qwen/qwen3-vl-30b-a3b-instruct`: `text` → `text` `POST /v1/messages` · `qwen/qwen3-vl-30b-a3b-instruct`: `text` → `json`, `sse` Which fields does the request use? `POST /v1/chat/completions` · `qwen/qwen3-vl-30b-a3b-instruct`: `model`, `messages`, `stream`, `modalities`, `provider` `POST /v1/messages` · `qwen/qwen3-vl-30b-a3b-instruct`: `model`, `messages`, `max_tokens`, `stream` What limits apply? `POST /v1/chat/completions` · `qwen/qwen3-vl-30b-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.