Source: https://ailoot.ai/models/en/tongyi-zhiwen-qwenlong-l1-32b/ Canonical: https://ailoot.ai/models/en/tongyi-zhiwen-qwenlong-l1-32b/ Language: en # QwenLong L1 32B [Markdown](https://ailoot.ai/models/en/tongyi-zhiwen-qwenlong-l1-32b/index.md) · [JSON](https://ailoot.ai/models/en/tongyi-zhiwen-qwenlong-l1-32b/facts.json) ## [QwenLong L1 32B](https://ailoot.ai/models/en/tongyi-zhiwen-qwenlong-l1-32b/) QwenLong-L1-32B is Tongyi Lab’s text reasoning model for questions grounded in long documents. It combines a DeepSeek-R1-Distill-Qwen-32B starting point with document-focused reinforcement learning for numerical, logical and cross-document reasoning. - [**Ailoot Ultra · OpenAI Chat** `POST /v1/chat/completions` `Tongyi-Zhiwen/QwenLong-L1-32B`](https://ailoot.ai/models/en/tongyi-zhiwen-qwenlong-l1-32b/#method-0) - [**Ailoot Ultra · Anthropic Messages** `POST /v1/messages` `Tongyi-Zhiwen/QwenLong-L1-32B`](https://ailoot.ai/models/en/tongyi-zhiwen-qwenlong-l1-32b/#method-1) About the model This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below. Introduction: reasoning over long documents QwenLong-L1-32B is Tongyi Lab’s long-context text reasoning model. Its purpose is to answer questions whose evidence is spread through a document or across several documents, rather than simply repeat a nearby passage. It is built from DeepSeek-R1-Distill-Qwen-32B and further trained for document-grounded reasoning. Supply text and a question; the model generates a textual reasoning sequence and answer. Scanned pages, audio and video need an appropriate separate conversion step before this text workflow. How the long-context training works The QwenLong-L1 framework first warms up a reasoning policy with supervised fine-tuning, then increases context length through a reinforcement-learning curriculum. Difficulty-aware retrospective sampling adjusts which examples are revisited as training progresses. The maker combines rule-based and model-based binary outcome rewards, using group-relative policy optimization to encourage answers that remain grounded in long evidence. This describes the checkpoint’s training approach; it does not mean an individual answer is automatically verified by an external judge. The released edition This is the original QwenLong-L1-32B release. The separately named QwenLong-L1-32B-AWQ is an INT4-quantized edition and should retain its own identity. The published configuration for this model uses a Qwen2 causal-language-model architecture with 64 layers and bfloat16 weights. The model’s document specialization is more relevant to selection than treating the 32B name as proof that every general-purpose task will improve. Preparing a document question The maker’s example separates the document from the question with explicit text delimiters and requests a recognizable final-answer phrase after the reasoning. Follow that structure: provide the relevant document body, keep headings and tables readable, and ask one concrete question. For a report calculation, preserve units, reporting periods and table labels, then request the formula, the values used and a final result. For a policy comparison, label the documents A and B and request a table of matching rules, differences and the supporting section for each row. These are authored applications of the documented numerical and cross-document capabilities. The example parses reasoning and final-answer text separately. It uses a 10,000-token generation budget, temperature 0.7 and top\_p 0.95; those are example settings, not a declared maximum output length. The card also shows an optional identity-setting system message. Preserve this model’s own prompt guidance rather than importing a different checkpoint’s recommendations solely because it appears in the model’s ancestry. Processing long documents Tongyi Lab validates context lengths up to 131,072 tokens using YaRN and recommends scaling when combined input and output significantly exceed 32,768 tokens. Its examples describe a factor of four, or a smaller factor such as two for a 65,536-token workload. Static scaling can reduce performance on shorter text, so the card advises against enabling it when ordinary requests stay within 32,768 tokens. The published configuration lists 131,072 positions but no enabled rope\_scaling field; configuration and the maker’s conditional long-context procedure should therefore be checked together. A long input still needs room for reasoning and the answer. Start with the passages needed for the question, remove repeated headers and navigation text, and retain page or section identifiers. Ask for evidence references in the result so a reviewer can trace the conclusion back to the supplied material. A large context allowance helps place more evidence in one request; it does not establish perfect recall of every detail. What the document training covers DocQA-RL-1.6K contains 1,600 training problems across three reasoning domains: 600 numerical questions from long specialized documents such as financial reports; 600 logical multiple-choice questions about legal, financial, insurance and production documents; and 400 multi-hop examples that connect evidence across sources. This makes the model a relevant candidate for report arithmetic, rule interpretation and questions requiring several supporting passages. The training domains describe experience with those tasks, not professional certification or guaranteed conclusions. Practical output and limits Choose an answer that can be checked: a computed value with units, a rule comparison with section references, or a short conclusion with the chain of supporting facts. If the document omits a required date or definition, ask the model to identify the missing evidence instead of inventing it. The release materials focus on document question answering and do not establish a dedicated tool-calling contract or a separate hard output ceiling. Treat answers as grounded drafts and verify calculations, references and interpretations against the supplied documents. Practical task ### Connect evidence across report sections Technical example in English ``` Connect evidence across report sections Document section 1 says 240 units were shipped. Section 4 says 30 shipped units were returned before month end. Appendix A says net shipped units equal shipped minus returned units. Calculate net shipped units for the month, cite every section needed and state whether revenue can be calculated from these facts. End with a clearly labelled final answer. ``` #### Expected result ``` A source-linked formula and calculation, a revenue-information limit and a labelled final result. ``` #### Check the result ``` Check that all three source sections support the calculation and that a price or revenue value is not invented. ``` **Ailoot Ultra · OpenAI Chat** `POST /v1/chat/completions` `Tongyi-Zhiwen/QwenLong-L1-32B` [`Tongyi-Zhiwen/QwenLong-L1-32B`](https://ailoot.ai/models/en/#m=Tongyi-Zhiwen%2FQwenLong-L1-32B&route=Ultra&caller=Tongyi-Zhiwen%2FQwenLong-L1-32B&fmt=chat) Other /`Tongyi-Zhiwen/QwenLong-L1-32B` ### Overview QwenLong-L1-32B is Tongyi Lab’s text reasoning model for questions grounded in long documents. It combines a DeepSeek-R1-Distill-Qwen-32B starting point with document-focused reinforcement learning for numerical, logical and cross-document reasoning. 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 Tongyi-Zhiwen/QwenLong-L1-32B | Default— | | Field`messages` | Type`message[]` | RequirementRequired | Values & limits Minimum items: 1 | Default— | | Field`messages[].role` | Type`string` | RequirementRequired | Values & limits Examples: system, user, assistant, tool, function Conditions: These roles are explicitly represented by Source compatibility types; the exact model role set remains model-specific. Model-specific | Default— | | Field`messages[].content` | Type`string\|content_part[]\|null` | RequirementOptional | Values & limits | Default— | | Field`messages[].tool_calls` | Type`tool_call[]` | RequirementOptional | Values & limits | Default— | | Field`messages[].tool_call_id` | Type`string` | RequirementOptional | Values & limits Applies when: role=tool; use actual returned ID | Default— | | Field`messages[].name` | Type`string` | RequirementOptional | Values & limits | Default— | | Field`messages[].reasoning_content` | Type`string` | RequirementOptional | Values & limits Applies when: Round-trip actual returned reasoning where this model uses it | Default— | | Field`messages[].reasoning_details` | Type`object\|array` | RequirementOptional | Values & limits Model-specific | Default— | | Field`max_tokens` | Type`integer` | RequirementOptional | Values & limits Minimum: 1 Model-specific | Default— | | Field`max_completion_tokens` | Type`integer` | RequirementOptional | Values & limits Minimum: 1 Model-specific | Default— | | Field`stream` | Type`boolean` | RequirementOptional | Values & limits | Defaultfalse | | Field`stream_options.include_usage` | Type`boolean` | RequirementOptional | Values & limits Model-specific | Default— | | Field`temperature` | Type`number` | RequirementOptional | Values & limits Model-specific | Default— | | Field`top_p` | Type`number` | RequirementOptional | Values & limits Model-specific | Default— | | Field`stop` | Type`string\|string[]` | RequirementOptional | Values & limits Model-specific | Default— | | Field`tools` | Type`function_tool[]` | RequirementOptional | Values & limits Model-specific | Default— | | Field`tools[].type` | Type`string` | RequirementOptional | Values & limits function Model-specific | Default— | | Field`tools[].function.name` | Type`string` | RequirementRequired | Values & limits Applies when: function tool supplied | Default— | | Field`tools[].function.description` | Type`string` | RequirementOptional | Values & limits | Default— | | Field`tools[].function.parameters` | Type`JSON_schema` | RequirementOptional | Values & limits | Default— | | Field`tool_choice` | Type`string\|object` | RequirementOptional | Values & limits Model-specific | Default— | | Field`parallel_tool_calls` | Type`boolean` | RequirementOptional | Values & limits Model-specific | Default— | | Field`response_format` | Type`object` | RequirementOptional | Values & limits Model-specific | Default— | | Field`reasoning_effort` | Type`string` | RequirementOptional | Values & limits Model-specific | Default— | | Field`reasoning` | Type`object` | RequirementOptional | Values & limits Model-specific | Default— | | Field`service_tier` | Type`string` | RequirementOptional | Values & limits Model-specific | Default— | | Field`instructions` | Type`string` | RequirementOptional | Values & limits Compatibility field | Default— | | Field`functions` | Type`function[]` | RequirementOptional | Values & limits Model-specific | Default— | | Field`function_call` | Type`string\|object` | RequirementOptional | Values & limits Model-specific | Default— | | Field`prompt_cache_options` | Type`object` | RequirementOptional | Values & limits Model-specific | Default— | | Field`modalities` | Type`string[]` | RequirementOptional | Values & limits text | Default— | | Field`provider` | Type`object` | RequirementOptional | Values & limits Conditions: provider.options is rejected on this Ultra text route | Default— | | Field`cache_control.ttl` | Type`string` | RequirementOptional | Values & limits Conditions: 1h is rejected in actual prompt-cache locations | Default— | Input material combinations **Text** · UTF8 text `messages[].content` Method parameters and lifecycle | Parameter | Contract | | --- | --- | | Parameter`content_type` | Contractapplication/json | | Parameter`required` | Contractmodel, messages | | Parameter`material_types` | Contracttext | | Parameter`messages` | Contractmessage array with role/content shape | | Parameter`file_material` | Contractactual UTF-8 document inserted as literal text; no file ID inferred | | Parameter`optional_controls` | Contractstream, temperature, top\_p, stop, max\_tokens, max\_completion\_tokens, reasoning, response\_format, tools, tool\_choice | | Parameter`control_scope` | Contractmodel-specific; user function tools differ from hosted tools | | Parameter`output_modalities` | Contracttext | | Parameter`hosted_operations` | Contractfalse | | Parameter`multimodal_input_binding` | Contractnull | Lifecycle: `synchronous_or_streamed` Send exact model ID with messages. Context length is not an output cap. A text file may supply literal text; image/audio/file-object inputs need exact binding. ### Pricing Tariff references do not add request options. Supported parameters are listed for the selected method. Fast mode Not specified When fast mode is selected Flex mode Not specified When flex mode is selected Maximum reasoning effort Not specified When maximum reasoning effort uses the default adjustment #### Ailoot rates #### Prices by configuration | Configuration | Rate schedule | Charge | | --- | --- | --- | | ConfigurationBase token rates | Rate scheduleBase rate | Charge Input · Input tokens **$0.00028** / 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": "Tongyi-Zhiwen/QwenLong-L1-32B", "messages": [{"role": "user", "content": "Explain the difference between an observation and a hypothesis in three sentences."}]}') Path("request.json").write_text(json.dumps(p,ensure_ascii=False)) PY_REQUEST curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/chat/completions \ -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \ --data-binary @request.json -D response-headers.txt -o result.json python3 - <<'PY_RESULT' import json from pathlib import Path r=json.loads(Path("result.json").read_text()) for x in r.get("choices",[]): if x.get("message",{}).get("content"): print(x["message"]["content"]) PY_RESULT ``` ```python from openai import OpenAI import os if not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request") client=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0) result=client.chat.completions.create(model='Tongyi-Zhiwen/QwenLong-L1-32B',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}]) import base64, json from pathlib import Path def save_result(data, stem="result"): # Keep every field, including usage, IDs, reasoning and tool requests. Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8") print(json.dumps(data, ensure_ascii=False, indent=2)) def save_blob(encoded, suffix, label): raw = base64.b64decode(encoded, altchars=b"-_", validate=True) ext = "png" if raw.startswith(b"\x89PNG\r\n\x1a\n") else "jpg" if raw.startswith(b"\xff\xd8") else "webp" if raw[:4] == b"RIFF" and raw[8:12] == b"WEBP" else suffix file = Path(stem + "-" + label + "." + ext) file.write_bytes(raw) print("Saved", file) for index, block in enumerate(data.get("content") or []): print("Content", index, json.dumps(block, ensure_ascii=False)) for choice in data.get("choices") or []: print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False)) for index, item in enumerate(data.get("output") or []): print("Output", index, json.dumps(item, ensure_ascii=False)) if item.get("type") == "image_generation_call" and item.get("result"): save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index)) for ci, candidate in enumerate(data.get("candidates") or []): for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []): print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False)) blob = part.get("inlineData") or part.get("inline_data") if blob and blob.get("data"): mime = blob.get("mimeType") or blob.get("mime_type") suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin") save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi)) save_result(result.to_dict(mode="json")) ``` ```python from openai import OpenAI import os from pathlib import Path if not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request") client=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0) import base64, json from pathlib import Path def save_result(data, stem="result"): # Keep every field, including usage, IDs, reasoning and tool requests. Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8") print(json.dumps(data, ensure_ascii=False, indent=2)) def save_blob(encoded, suffix, label): raw = base64.b64decode(encoded, altchars=b"-_", validate=True) ext = "png" if raw.startswith(b"\x89PNG\r\n\x1a\n") else "jpg" if raw.startswith(b"\xff\xd8") else "webp" if raw[:4] == b"RIFF" and raw[8:12] == b"WEBP" else suffix file = Path(stem + "-" + label + "." + ext) file.write_bytes(raw) print("Saved", file) for index, block in enumerate(data.get("content") or []): print("Content", index, json.dumps(block, ensure_ascii=False)) for choice in data.get("choices") or []: print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False)) for index, item in enumerate(data.get("output") or []): print("Output", index, json.dumps(item, ensure_ascii=False)) if item.get("type") == "image_generation_call" and item.get("result"): save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index)) for ci, candidate in enumerate(data.get("candidates") or []): for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []): print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False)) blob = part.get("inlineData") or part.get("inline_data") if blob and blob.get("data"): mime = blob.get("mimeType") or blob.get("mime_type") suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin") save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi)) with client.chat.completions.stream(model='Tongyi-Zhiwen/QwenLong-L1-32B',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}]) as stream: with Path("stream-events.jsonl").open("w",encoding="utf-8") as log: for event in stream: payload=event.to_dict(mode="json") log.write(json.dumps(payload,ensure_ascii=False)+"\n"); log.flush() print(json.dumps(payload,ensure_ascii=False)) final=stream.get_final_completion() save_result(final.to_dict(mode="json"),"stream-result") ``` ### Receive and save results #### Ailoot Ultra · OpenAI Chat json or SSE ##### Keep all completed results Keep every complete returned response, file and content part. Keep every choice and its content/tool calls/reasoning. In SSE assemble deltas by choice/tool index, retain the final finish reason and usage. ##### If the original response needs checking Keep request/response IDs and headers. No generic text job retrieval URL is supplied. Response fields | Field | Type | | --- | --- | | Field`choices[].index` | Typeinteger | | Field`choices[].message.content` | Typestring\|null | | Field`choices[].message.tool_calls` | Typetool\_call\[\] | | Field`choices[].message.reasoning_content` | Typestring | | Field`choices[].message.reasoning_details` | Typeobject\|array | | Field`choices[].finish_reason` | Typestring\|null | | Field`usage` | Typeobject | Errors and next steps | Response | Condition | Next step | | --- | --- | --- | | Response400 · `invalid_request_error` | ConditionInvalid required fields, material representation or parameter value | Next stepCorrect the indicated fields before a new creation request. | ### Aliases #### Model aliases **Ailoot Ultra** [`Tongyi-Zhiwen/QwenLong-L1-32B`](https://ailoot.ai/models/en/#m=Tongyi-Zhiwen%2FQwenLong-L1-32B&route=Ultra&caller=Tongyi-Zhiwen%2FQwenLong-L1-32B&fmt=chat) #### Method path aliases Use the method paths shown above. #### Related caller IDs [`Tongyi-Zhiwen/QwenLong-L1-32B`](https://ailoot.ai/models/en/#m=Tongyi-Zhiwen%2FQwenLong-L1-32B&route=Ultra&caller=Tongyi-Zhiwen%2FQwenLong-L1-32B&fmt=chat) **Ailoot Ultra · Anthropic Messages** `POST /v1/messages` `Tongyi-Zhiwen/QwenLong-L1-32B` [`Tongyi-Zhiwen/QwenLong-L1-32B`](https://ailoot.ai/models/en/#m=Tongyi-Zhiwen%2FQwenLong-L1-32B&route=Ultra&caller=Tongyi-Zhiwen%2FQwenLong-L1-32B&fmt=messages) Other /`Tongyi-Zhiwen/QwenLong-L1-32B` ### Overview QwenLong-L1-32B is Tongyi Lab’s text reasoning model for questions grounded in long documents. It combines a DeepSeek-R1-Distill-Qwen-32B starting point with document-focused reinforcement learning for numerical, logical and cross-document reasoning. 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 Tongyi-Zhiwen/QwenLong-L1-32B | Default— | | Field`messages` | Type`message[]` | RequirementRequired | Values & limits | Default— | | Field`messages[].role` | Type`string` | RequirementRequired | Values & limits user, assistant | Default— | | Field`messages[].content` | Type`string` | RequirementRequired | Values & limits | Default— | | Field`max_tokens` | Type`integer` | RequirementRequired | Values & limits | Default— | | Field`stream` | Type`boolean` | RequirementOptional | Values & limits | Defaultfalse | Input material combinations **Text** · UTF-8 text `messages[].content string` Method parameters and lifecycle | Parameter | Contract | | --- | --- | | Parameter`content_type` | Contractapplication/json | | Parameter`required` | Contractmodel, messages, max\_tokens | | Parameter`optional_controls` | Contractstream | | Parameter`material_types` | Contracttext | | Parameter`text_file_material` | ContractRead the actual UTF-8 document and put its text in the indicated input or message content string. | Lifecycle: `synchronous_or_streamed` ### Pricing Tariff references do not add request options. Supported parameters are listed for the selected method. Fast mode Not specified When fast mode is selected Flex mode Not specified When flex mode is selected Maximum reasoning effort Not specified When maximum reasoning effort uses the default adjustment #### Ailoot rates #### Prices by configuration | Configuration | Rate schedule | Charge | | --- | --- | --- | | ConfigurationBase token rates | Rate scheduleBase rate | Charge Input · Input tokens **$0.00028** / 1K tokens Output · Output tokens **$0.0012** / 1K tokens Input · Cache read **$0** / 1K tokens Input · Cache write **$0** / 1K tokens Input · Cache write, 1 hour **—** / 1K tokens | Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. [How reservation and settlement work →](https://ailoot.ai/docs/en/billing/reserve/) ### Example requests #### Ailoot Ultra · Anthropic Messages Complete minimal JSON bodycurl · use your UTF-8 documentPython · save the complete result ```json { "model": "Tongyi-Zhiwen/QwenLong-L1-32B", "max_tokens": 128, "messages": [ { "role": "user", "content": "Summarize the incident report." } ], "stream": false } ``` ```bash set -eu : "${AILOOT_API_KEY:?Set your Ailoot API key}" : "${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}" export AILOOT_TEXT_FILE python3 - <<'PY_REQUEST' import json, os from pathlib import Path source_file = Path(os.environ['AILOOT_TEXT_FILE']) if not source_file.is_file(): raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document') text = source_file.read_text(encoding='utf-8') if not text.strip(): raise SystemExit('Choose a document containing text') payload = {'model': 'Tongyi-Zhiwen/QwenLong-L1-32B', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]} payload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\n\n' + text Path('request.json').write_text(json.dumps(payload, ensure_ascii=False), encoding='utf-8') PY_REQUEST curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/messages \ -H "x-api-key: $AILOOT_API_KEY" -H 'anthropic-version: 2023-06-01' -H 'Content-Type: application/json' \ --data-binary @request.json -D response-headers.txt -o result.json python3 - <<'PY_RESULT' import json from pathlib import Path result = json.loads(Path('result.json').read_text(encoding='utf-8')) print(json.dumps(result, ensure_ascii=False, indent=2)) PY_RESULT ``` ```python import json, os from pathlib import Path source_file = Path(os.environ['AILOOT_TEXT_FILE']) if not source_file.is_file(): raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document') text = source_file.read_text(encoding='utf-8') if not text.strip(): raise SystemExit('Choose a document containing text') payload = {'model': 'Tongyi-Zhiwen/QwenLong-L1-32B', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]} payload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\n\n' + text from urllib.request import Request, urlopen from urllib.error import HTTPError api_key = os.environ['AILOOT_API_KEY'] if not api_key.strip(): raise SystemExit('Set AILOOT_API_KEY before making a request') headers = {'x-api-key': 'PLACEHOLDER_KEY', 'anthropic-version': '2023-06-01', 'Content-Type': 'application/json'} headers['x-api-key'] = api_key request = Request('https://api.ailoot.ai/v1/messages', data=json.dumps(payload, ensure_ascii=False).encode('utf-8'), headers=headers, method='POST') try: with urlopen(request, timeout=120) as response: raw = response.read() Path('response-headers.txt').write_text(str(response.headers), encoding='utf-8') except HTTPError as error: Path('result.json').write_bytes(error.read()) Path('response-headers.txt').write_text(str(error.headers), encoding='utf-8') raise Path('result.json').write_bytes(raw) import json from pathlib import Path result = json.loads(Path('result.json').read_text(encoding='utf-8')) print(json.dumps(result, ensure_ascii=False, indent=2)) ``` ### Receive and save results #### Ailoot Ultra · Anthropic Messages JSON or SSE ##### Keep all completed results Keep every complete returned response, file and content part. Keep all content blocks, thinking signatures and actual tool-use IDs. Assemble all SSE block deltas; a first text block is not the complete output. ##### If the original response needs checking Keep request/response IDs and headers. No generic text job retrieval URL is supplied. Response fields | Field | Type | | --- | --- | | Field`id` | Typestring | | Field`model` | Typestring | | Field`content[]` | Typetext\|thinking\|tool\_use\|other content block | | Field`stop_reason` | Typestring\|null | | Field`usage` | Typeobject | Errors and next steps | Response | Condition | Next step | | --- | --- | --- | | Response400 · `invalid_request_error` | ConditionInvalid required fields, material representation or parameter value | Next stepCorrect the indicated fields before a new creation request. | ### Aliases #### Model aliases **Ailoot Ultra** [`Tongyi-Zhiwen/QwenLong-L1-32B`](https://ailoot.ai/models/en/#m=Tongyi-Zhiwen%2FQwenLong-L1-32B&route=Ultra&caller=Tongyi-Zhiwen%2FQwenLong-L1-32B&fmt=messages) #### Method path aliases Use the method paths shown above. #### Related caller IDs [`Tongyi-Zhiwen/QwenLong-L1-32B`](https://ailoot.ai/models/en/#m=Tongyi-Zhiwen%2FQwenLong-L1-32B&route=Ultra&caller=Tongyi-Zhiwen%2FQwenLong-L1-32B&fmt=messages) FAQ · Tongyi-Zhiwen/QwenLong-L1-32B What inputs and results are available? `POST /v1/chat/completions` · `Tongyi-Zhiwen/QwenLong-L1-32B`: `text` → `text` `POST /v1/messages` · `Tongyi-Zhiwen/QwenLong-L1-32B`: `text` → `json`, `sse` Which fields does the request use? `POST /v1/chat/completions` · `Tongyi-Zhiwen/QwenLong-L1-32B`: `model`, `messages`, `stream`, `modalities`, `provider` `POST /v1/messages` · `Tongyi-Zhiwen/QwenLong-L1-32B`: `model`, `messages`, `max_tokens`, `stream` What limits apply? `POST /v1/chat/completions` · `Tongyi-Zhiwen/QwenLong-L1-32B`: `messages.min_items`: `1` Connect Ailoot to Claude Code, OpenCode, Hermes or your own app. ## The company and its project portfolio The company and its project portfolio: millions of customers; monthly sales in the billions. ## Buy tokens at a discount. Subscription discounts for regular use. Promo codes offer up to 50% off token purchases. Subscription Choose a discounted plan for your regular requests. Promo code: up to 50% off Enter your code at checkout. The price updates to reflect the current promotion. Spending in your history The model, tokens and amount are recorded for each request. [Choose a subscription](https://ailoot.ai/docs/en/billing/subscriptions/) · [View model prices](https://ailoot.ai/models/en/) ## Connect the API to a product you sell. Use Ailoot in SaaS, apps and bots under its official service offer and licence agreement. Request a reseller licence for resale. Commercial application Add text, image, video and audio generation to paid features in your service. Reseller licence Send a description of your project and how you provide access. Request written terms for use and resale. Documents on the website The service offer, licence agreement and data processing policy are available before you connect. [Request a reseller licence](mailto:support@ailoot.ai) · [Service offer and licence agreement](https://ailoot.ai/legal/offer?lang=en) ## Ailoot · The company and its project portfolio Ailoot combines established operating experience, a large client base, stable service and ongoing KPI control. The company and its project portfolio: millions of customers; monthly sales in the billions. Ailoot brings that operating experience to API access for AI features in customer products, agents, applications and automations.