Source: https://ailoot.ai/models/en/qwen2-5-vl-3b-instruct/ Canonical: https://ailoot.ai/models/en/qwen2-5-vl-3b-instruct/ Language: en # Qwen2.5-VL-3b [Markdown](https://ailoot.ai/models/en/qwen2-5-vl-3b-instruct/index.md) · [JSON](https://ailoot.ai/models/en/qwen2-5-vl-3b-instruct/facts.json) ## [Qwen2.5-VL-3b](https://ailoot.ai/models/en/qwen2-5-vl-3b-instruct/) Qwen2.5-VL-3B-Instruct is Qwen’s compact instruction-tuned vision-language model for text, images and video. It returns text and structured visual information, including document fields, object coordinates and event descriptions, with dynamic resolution and time-aware video processing. - [**Ailoot Ultra · OpenAI Chat** `POST /v1/chat/completions` `qwen2.5-vl-3b-instruct`](https://ailoot.ai/models/en/qwen2-5-vl-3b-instruct/#method-0) - [**Ailoot Ultra · Anthropic Messages** `POST /v1/messages` `qwen2.5-vl-3b-instruct`](https://ailoot.ai/models/en/qwen2-5-vl-3b-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. Introduction: the instruction-tuned 3B edition Qwen2.5-VL-3B-Instruct is the 3B member of Qwen2.5-VL. It accepts text instructions with images or video and generates textual responses, including structured data. Choose it for a clearly defined visual question, a document-extraction task or an explanation that connects visible content with the user’s request. This exact size and release retain their own identity; features added specifically to other sizes or later Qwen generations should not be assumed to apply to this checkpoint. Visual understanding and agent workflows Qwen describes recognition of common objects and analysis of writing, charts, icons, graphics and page layouts. For a form, specify the required fields and the meaning of an empty value. For a chart, ask for the axes, units and visible values supporting the conclusion. A focused request makes the output easier to compare with the original image than a broad request to describe everything. The family also supports visual-agent reasoning and tool direction for computer or phone workflows. The model can interpret an interface and help decide an action; connected application tools perform that action. A screenshot review can ask which control corresponds to a task and what visible information supports the choice. That use of visual reasoning does not itself grant control of a device. Long video and event localization The maker describes understanding videos longer than one hour and identifying the segments relevant to a requested event. This is a family capability, not an unconditional media-upload limit or a guarantee that every frame is retained. Prepare the relevant clip, preserve its timing information and ask a precise question about the event, sequence or change. The model’s video-processing approach uses frame-rate information to align visual observations with absolute time. Localization and structured documents Qwen2.5-VL can return object locations as bounding boxes or points, with coordinates and attributes in JSON. For a localization task, name the target object and specify whether the result should be a point or a box. Keep the coordinate convention explicit in the consuming application rather than infer a physical measurement from an arbitrary photograph. The maker also documents structured extraction from scanned invoices, forms and tables. Provide a readable page and an output schema: for example, field name, extracted text and the visible region that supports it. Preserve units, decimal separators and table headings in the source. Treat uncertain or illegible content as a review case rather than asking the model to invent a missing value. These preparation examples apply documented capabilities; they are not guarantees of exact extraction. Architecture changes Dynamic image resolution is extended into video through variable frame-rate training. Updated multimodal positional encoding represents temporal sequence and absolute time, helping identify specific moments and changes in speed. The vision encoder uses window attention together with SwiGLU and RMSNorm. These changes describe how the model processes visual material; they do not establish a fixed latency for a particular application. Preparing images and video The official examples cover single images, multiple images, text-only conversations, video files and ordered frame lists. Label several visual inputs so a question can refer to each one. For an image comparison, request a table organized by the labels and restrict the result to visible differences. For a video timeline, preserve frame order and timing instead of presenting disconnected pictures as if they carried a known duration. The image processor’s documented default ranges from four to 16,384 visual tokens per image. Its example can target 256–1,280 tokens by setting pixel bounds. It maintains aspect ratio within those bounds, while explicit resized dimensions are aligned to multiples of 28. More detail consumes more computation and sequence space, so a small text field may need a higher-resolution crop while a broad scene summary may not. Those are maker processing controls; application upload options can differ. Context and substantive limits The published checkpoint configuration sets maximum positions to 128,000, while the accompanying long-text section still discusses a 32,768-token starting configuration. The guide describes YaRN extrapolation but warns that it can significantly affect temporal and spatial localization and does not recommend that method for those tasks. It separately notes that video positional encoding can allow a larger position setting, giving 64K as an example. These different configuration descriptions should not be collapsed into a single universally validated input ceiling. Text, visual material and the answer share a finite sequence budget. Keep relevant content legible, reserve room for the result and check extracted text, coordinates and event times against the original. The card’s 128-token generation example is illustrative and does not establish a separate hard output maximum. This model returns textual analysis or structured information; its vision-language capabilities do not mean it generates images or video. Practical task ### Organize a recorded event timeline Technical example in English ``` Organize a recorded event timeline Use only these text annotations from a clip: 00:02 worker picks up a box; 00:05 worker places the box on the scale; 00:08 scale display is obscured; 00:11 worker removes the box. Return the event sequence, the first annotated placement time and whether the weight is established. Preserve timestamps without claiming frame-level inspection. ``` #### Expected result ``` An ordered event table, a placement-time field and an unresolved weight field. ``` #### Check the result ``` Check that obscured weight remains unknown and the placement timestamp is attributed to the supplied annotation. ``` **Ailoot Ultra · OpenAI Chat** `POST /v1/chat/completions` `qwen2.5-vl-3b-instruct` [`qwen2.5-vl-3b-instruct`](https://ailoot.ai/models/en/#m=qwen2.5-vl-3b-instruct&route=Ultra&caller=qwen2.5-vl-3b-instruct&fmt=chat) Alibaba /`qwen2.5-vl-3b-instruct` ### Overview Qwen2.5-VL-3B-Instruct is Qwen’s compact instruction-tuned vision-language model for text, images and video. It returns text and structured visual information, including document fields, object coordinates and event descriptions, with dynamic resolution and time-aware video processing. 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 qwen2.5-vl-3b-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 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 **$0.0004** / 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": "qwen2.5-vl-3b-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='qwen2.5-vl-3b-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='qwen2.5-vl-3b-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** [`qwen2.5-vl-3b-instruct`](https://ailoot.ai/models/en/#m=qwen2.5-vl-3b-instruct&route=Ultra&caller=qwen2.5-vl-3b-instruct&fmt=chat) #### Method path aliases Use the method paths shown above. #### Related caller IDs [`qwen2.5-vl-3b-instruct`](https://ailoot.ai/models/en/#m=qwen2.5-vl-3b-instruct&route=Ultra&caller=qwen2.5-vl-3b-instruct&fmt=chat) **Ailoot Ultra · Anthropic Messages** `POST /v1/messages` `qwen2.5-vl-3b-instruct` [`qwen2.5-vl-3b-instruct`](https://ailoot.ai/models/en/#m=qwen2.5-vl-3b-instruct&route=Ultra&caller=qwen2.5-vl-3b-instruct&fmt=messages) Alibaba /`qwen2.5-vl-3b-instruct` ### Overview Qwen2.5-VL-3B-Instruct is Qwen’s compact instruction-tuned vision-language model for text, images and video. It returns text and structured visual information, including document fields, object coordinates and event descriptions, with dynamic resolution and time-aware video processing. 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 qwen2.5-vl-3b-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 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 **$0.0004** / 1K tokens Output · Output tokens **$0.0012** / 1K tokens Input · Cache read **$0** / 1K tokens Input · Cache write **$0** / 1K tokens Input · Cache write, 1 hour **—** / 1K tokens | Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. [How reservation and settlement work →](https://ailoot.ai/docs/en/billing/reserve/) ### Example requests #### Ailoot Ultra · Anthropic Messages Complete minimal JSON bodycurl · use your UTF-8 documentPython · save the complete result ```json { "model": "qwen2.5-vl-3b-instruct", "max_tokens": 128, "messages": [ { "role": "user", "content": "Summarize the incident report." } ], "stream": false } ``` ```bash set -eu : "${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': 'qwen2.5-vl-3b-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': 'qwen2.5-vl-3b-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** [`qwen2.5-vl-3b-instruct`](https://ailoot.ai/models/en/#m=qwen2.5-vl-3b-instruct&route=Ultra&caller=qwen2.5-vl-3b-instruct&fmt=messages) #### Method path aliases Use the method paths shown above. #### Related caller IDs [`qwen2.5-vl-3b-instruct`](https://ailoot.ai/models/en/#m=qwen2.5-vl-3b-instruct&route=Ultra&caller=qwen2.5-vl-3b-instruct&fmt=messages) FAQ · Qwen2.5-VL-3b What inputs and results are available? `POST /v1/chat/completions` · `qwen2.5-vl-3b-instruct`: `text` → `text` `POST /v1/messages` · `qwen2.5-vl-3b-instruct`: `text` → `json`, `sse` Which fields does the request use? `POST /v1/chat/completions` · `qwen2.5-vl-3b-instruct`: `model`, `messages`, `stream`, `modalities`, `provider` `POST /v1/messages` · `qwen2.5-vl-3b-instruct`: `model`, `messages`, `max_tokens`, `stream` What limits apply? `POST /v1/chat/completions` · `qwen2.5-vl-3b-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.