Sonar Reasoning is Perplexity’s reasoning-enhanced search profile for questions that require interpreting evidence rather than returning a short isolated fact. Its maker identifies it separately from Sonar Reasoning Pro and the other Sonar search profiles. The useful result is a text answer connected to web-source evidence, with reasoning helping to compare material and develop a conclusion about the stated question.
Name the subject, the facts to establish and the criteria the answer should use. A product comparison can ask for the documented capabilities and limitations relevant to one workflow. A changing-event question should identify the event, time period and outcome to verify. Specify the intended artifact: a short explanation, an evidence table or a comparison organized around the decision being made.
Keep supplied background distinct from what the search should establish. If an important condition is missing, request that it remain open rather than being filled with a plausible assumption. A bounded question makes it easier to inspect whether the retrieved material actually answers it, especially when several sources discuss related but different cases.
The manufacturer describes Sonar Reasoning as part of its reasoning-enhanced search family. Use the returned material to compare alternatives, resolve an apparent disagreement or explain why a conclusion follows from the evidence. A source-backed reasoning answer should distinguish a page’s statement from the inference made by combining several pages.
For a requirements question, ask which source documents the feature and whether its conditions match the intended use. For a date-sensitive fact, preserve the source’s timing and the context in which it describes the event. A relevant-looking page can discuss an earlier version or a different case, so its relationship to the question matters more than the presence of a citation alone.
The producer’s Sonar response workflow associates an answer with source citations. Retain those references with the passages they support so another reader can inspect the evidence. A comparison table can keep the item, the stated capability, its supporting source and the condition that remains unresolved together. Check whether the cited material establishes the exact value or only a related claim.
A useful handoff can contain the original question, the synthesized answer and the sources used to support it. Preserve important names, numbers and source distinctions when carrying the result into a finished document. If the answer leaves a decisive criterion unresolved, narrow a follow-up around that criterion instead of treating the unsupported field as complete.
Sonar Reasoning and Reasoning Pro are separately named producer profiles. The later Agent API has its own models, tool primitives and request format, so those newer features should not be treated as this original profile’s native specification. Continue from the actual answer and its evidence, retaining the selected profile and task conditions when comparing results. The practical value is an assessable, source-connected reasoning artifact for a concrete research question.
", "aboutText": "Sonar Reasoning is Perplexity’s reasoning-enhanced search profile for questions that require interpreting evidence rather than returning a short isolated fact. Its maker identifies it separately from Sonar Reasoning Pro and the other Sonar search profiles. The useful result is a text answer connected to web-source evidence, with reasoning helping to compare material and develop a conclusion about the stated question.\n\n### Define the research question and result\n\nName the subject, the facts to establish and the criteria the answer should use. A product comparison can ask for the documented capabilities and limitations relevant to one workflow. A changing-event question should identify the event, time period and outcome to verify. Specify the intended artifact: a short explanation, an evidence table or a comparison organized around the decision being made.\n\nKeep supplied background distinct from what the search should establish. If an important condition is missing, request that it remain open rather than being filled with a plausible assumption. A bounded question makes it easier to inspect whether the retrieved material actually answers it, especially when several sources discuss related but different cases.\n\n### Combine reasoning with source evidence\n\nThe manufacturer describes Sonar Reasoning as part of its reasoning-enhanced search family. Use the returned material to compare alternatives, resolve an apparent disagreement or explain why a conclusion follows from the evidence. A source-backed reasoning answer should distinguish a page’s statement from the inference made by combining several pages.\n\nFor a requirements question, ask which source documents the feature and whether its conditions match the intended use. For a date-sensitive fact, preserve the source’s timing and the context in which it describes the event. A relevant-looking page can discuss an earlier version or a different case, so its relationship to the question matters more than the presence of a citation alone.\n\n### Keep citations with the completed answer\n\nThe producer’s Sonar response workflow associates an answer with source citations. Retain those references with the passages they support so another reader can inspect the evidence. A comparison table can keep the item, the stated capability, its supporting source and the condition that remains unresolved together. Check whether the cited material establishes the exact value or only a related claim.\n\nA useful handoff can contain the original question, the synthesized answer and the sources used to support it. Preserve important names, numbers and source distinctions when carrying the result into a finished document. If the answer leaves a decisive criterion unresolved, narrow a follow-up around that criterion instead of treating the unsupported field as complete.\n\n### Keep the named reasoning profile distinct\n\nSonar Reasoning and Reasoning Pro are separately named producer profiles. The later Agent API has its own models, tool primitives and request format, so those newer features should not be treated as this original profile’s native specification. Continue from the actual answer and its evidence, retaining the selected profile and task conditions when comparing results. The practical value is an assessable, source-connected reasoning artifact for a concrete research question.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Research a protocol distinction\n\nFind the IETF HTTP Semantics specification and explain how HEAD differs from GET for response content and metadata. Prepare a small evidence table with official source references, then give one practical use of HEAD. Distinguish what the specification requires from your example and leave any server-specific behavior unresolved.A source-backed comparison table and a clearly labelled application example with citations.Inspect the cited specification sections and check that each claim concerns the actual GET/HEAD distinction.sonar-reasoningSonar Reasoning combines Perplexity’s search with reasoning for source-based questions and comparisons. It produces a text answer connected to web evidence, with the original question and citations retained for review.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Sonar Reasoning is Perplexity’s reasoning-enhanced search profile for questions that require interpreting evidence rather than returning a short isolated fact. Its maker identifies it separately from Sonar Reasoning Pro and the other Sonar search profiles. The useful result is a text answer connected to web-source evidence, with reasoning helping to compare material and develop a conclusion about the stated question.
Name the subject, the facts to establish and the criteria the answer should use. A product comparison can ask for the documented capabilities and limitations relevant to one workflow. A changing-event question should identify the event, time period and outcome to verify. Specify the intended artifact: a short explanation, an evidence table or a comparison organized around the decision being made.
Keep supplied background distinct from what the search should establish. If an important condition is missing, request that it remain open rather than being filled with a plausible assumption. A bounded question makes it easier to inspect whether the retrieved material actually answers it, especially when several sources discuss related but different cases.
The manufacturer describes Sonar Reasoning as part of its reasoning-enhanced search family. Use the returned material to compare alternatives, resolve an apparent disagreement or explain why a conclusion follows from the evidence. A source-backed reasoning answer should distinguish a page’s statement from the inference made by combining several pages.
For a requirements question, ask which source documents the feature and whether its conditions match the intended use. For a date-sensitive fact, preserve the source’s timing and the context in which it describes the event. A relevant-looking page can discuss an earlier version or a different case, so its relationship to the question matters more than the presence of a citation alone.
The producer’s Sonar response workflow associates an answer with source citations. Retain those references with the passages they support so another reader can inspect the evidence. A comparison table can keep the item, the stated capability, its supporting source and the condition that remains unresolved together. Check whether the cited material establishes the exact value or only a related claim.
A useful handoff can contain the original question, the synthesized answer and the sources used to support it. Preserve important names, numbers and source distinctions when carrying the result into a finished document. If the answer leaves a decisive criterion unresolved, narrow a follow-up around that criterion instead of treating the unsupported field as complete.
Sonar Reasoning and Reasoning Pro are separately named producer profiles. The later Agent API has its own models, tool primitives and request format, so those newer features should not be treated as this original profile’s native specification. Continue from the actual answer and its evidence, retaining the selected profile and task conditions when comparing results. The practical value is an assessable, source-connected reasoning artifact for a concrete research question.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits sonar-reasoning | Default— |
Fieldmessages | Typemessage[] | RequirementRequired | Values & limits Minimum items: 1 | Default— |
Fieldmessages[].role | Typestring | 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— |
Fieldmessages[].content | Typestring|content_part[]|null | RequirementOptional | Values & limits | Default— |
Fieldmessages[].tool_calls | Typetool_call[] | RequirementOptional | Values & limits | Default— |
Fieldmessages[].tool_call_id | Typestring | RequirementOptional | Values & limits Applies when: role=tool; use actual returned ID | Default— |
Fieldmessages[].name | Typestring | RequirementOptional | Values & limits | Default— |
Fieldmessages[].reasoning_content | Typestring | RequirementOptional | Values & limits Applies when: Round-trip actual returned reasoning where this model uses it | Default— |
Fieldmessages[].reasoning_details | Typeobject|array | RequirementOptional | Values & limits Model-specific | Default— |
Fieldmax_tokens | Typeinteger | RequirementOptional | Values & limits Minimum: 1 Model-specific | Default— |
Fieldmax_completion_tokens | Typeinteger | RequirementOptional | Values & limits Minimum: 1 Model-specific | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
Fieldstream_options.include_usage | Typeboolean | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtemperature | Typenumber | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtop_p | Typenumber | RequirementOptional | Values & limits Model-specific | Default— |
Fieldstop | Typestring|string[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtools | Typefunction_tool[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtools[].type | Typestring | RequirementOptional | Values & limits function Model-specific | Default— |
Fieldtools[].function.name | Typestring | RequirementRequired | Values & limits Applies when: function tool supplied | Default— |
Fieldtools[].function.description | Typestring | RequirementOptional | Values & limits | Default— |
Fieldtools[].function.parameters | TypeJSON_schema | RequirementOptional | Values & limits | Default— |
Fieldtool_choice | Typestring|object | RequirementOptional | Values & limits Model-specific | Default— |
Fieldparallel_tool_calls | Typeboolean | RequirementOptional | Values & limits Model-specific | Default— |
Fieldresponse_format | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldreasoning_effort | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldreasoning | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldservice_tier | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldinstructions | Typestring | RequirementOptional | Values & limits Compatibility field | Default— |
Fieldfunctions | Typefunction[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldfunction_call | Typestring|object | RequirementOptional | Values & limits Model-specific | Default— |
Fieldprompt_cache_options | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldmodalities | Typestring[] | RequirementOptional | Values & limits text | Default— |
Fieldprovider | Typeobject | RequirementOptional | Values & limits Conditions: provider.options is rejected on this Ultra text route | Default— |
Fieldcache_control.ttl | Typestring | RequirementOptional | Values & limits Conditions: 1h is rejected in actual prompt-cache locations | Default— |
Text · UTF8 text
messages[].content
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, messages |
Parametermaterial_types | Contracttext |
Parametermessages | Contractmessage array with role/content shape |
Parameterfile_material | Contractactual UTF-8 document inserted as literal text; no file ID inferred |
Parameteroptional_controls | Contractstream, temperature, top_p, stop, max_tokens, max_completion_tokens, reasoning, response_format, tools, tool_choice |
Parametercontrol_scope | Contractmodel-specific; user function tools differ from hosted tools |
Parameteroutput_modalities | Contracttext |
Parameterhosted_operations | Contractfalse |
Parametermultimodal_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.
Tariff references do not add request options. Supported parameters are listed for the selected method.
Match each charge to the conditions shown. Use one rate for each set of alternative configurations.
| Configuration | Rate schedule | Billing quantity | Charge |
|---|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Billing quantity— | Charge Input · Input tokens $0.0044 / 1K tokens Output · Output tokens $0.0176 / 1K tokens Input · Cache read $0 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
| ConfigurationPer request | Rate schedule— | Billing quantityone request | Charge Input $0.01 per request |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "sonar-reasoning", "messages": [{"role": "user", "content": "Explain the difference between an observation and a hypothesis in three sentences."}]}')\n\nPath("request.json").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/chat/completions \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nr=json.loads(Path("result.json").read_text())\nfor x in r.get("choices",[]):\n if x.get("message",{}).get("content"): print(x["message"]["content"])\nPY_RESULT\nfrom openai import OpenAI\nimport os\n\nif not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")\nclient=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)\nresult=client.chat.completions.create(model='sonar-reasoning',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}])\nimport base64, json\nfrom pathlib import Path\n\ndef save_result(data, stem="result"):\n # Keep every field, including usage, IDs, reasoning and tool requests.\n Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")\n print(json.dumps(data, ensure_ascii=False, indent=2))\n def save_blob(encoded, suffix, label):\n raw = base64.b64decode(encoded, altchars=b"-_", validate=True)\n 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\n file = Path(stem + "-" + label + "." + ext)\n file.write_bytes(raw)\n print("Saved", file)\n for index, block in enumerate(data.get("content") or []):\n print("Content", index, json.dumps(block, ensure_ascii=False))\n for choice in data.get("choices") or []:\n print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))\n for index, item in enumerate(data.get("output") or []):\n print("Output", index, json.dumps(item, ensure_ascii=False))\n if item.get("type") == "image_generation_call" and item.get("result"):\n save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))\n for ci, candidate in enumerate(data.get("candidates") or []):\n for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):\n print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))\n blob = part.get("inlineData") or part.get("inline_data")\n if blob and blob.get("data"):\n mime = blob.get("mimeType") or blob.get("mime_type")\n suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")\n save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))\n\nsave_result(result.to_dict(mode="json"))\nfrom openai import OpenAI\nimport os\nfrom pathlib import Path\n\nif not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")\nclient=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)\nimport base64, json\nfrom pathlib import Path\n\ndef save_result(data, stem="result"):\n # Keep every field, including usage, IDs, reasoning and tool requests.\n Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")\n print(json.dumps(data, ensure_ascii=False, indent=2))\n def save_blob(encoded, suffix, label):\n raw = base64.b64decode(encoded, altchars=b"-_", validate=True)\n 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\n file = Path(stem + "-" + label + "." + ext)\n file.write_bytes(raw)\n print("Saved", file)\n for index, block in enumerate(data.get("content") or []):\n print("Content", index, json.dumps(block, ensure_ascii=False))\n for choice in data.get("choices") or []:\n print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))\n for index, item in enumerate(data.get("output") or []):\n print("Output", index, json.dumps(item, ensure_ascii=False))\n if item.get("type") == "image_generation_call" and item.get("result"):\n save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))\n for ci, candidate in enumerate(data.get("candidates") or []):\n for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):\n print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))\n blob = part.get("inlineData") or part.get("inline_data")\n if blob and blob.get("data"):\n mime = blob.get("mimeType") or blob.get("mime_type")\n suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")\n save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))\n\nwith client.chat.completions.stream(model='sonar-reasoning',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}]) as stream:\n with Path("stream-events.jsonl").open("w",encoding="utf-8") as log:\n for event in stream:\n payload=event.to_dict(mode="json")\n log.write(json.dumps(payload,ensure_ascii=False)+"\\n"); log.flush()\n print(json.dumps(payload,ensure_ascii=False))\n final=stream.get_final_completion()\n save_result(final.to_dict(mode="json"),"stream-result")\njson or SSE
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.
Keep request/response IDs and headers. No generic text job retrieval URL is supplied.
| Field | Type |
|---|---|
Fieldchoices[].index | Typeinteger |
Fieldchoices[].message.content | Typestring|null |
Fieldchoices[].message.tool_calls | Typetool_call[] |
Fieldchoices[].message.reasoning_content | Typestring |
Fieldchoices[].message.reasoning_details | Typeobject|array |
Fieldchoices[].finish_reason | Typestring|null |
Fieldusage | Typeobject |
| 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. |
Ailoot Ultra sonar-reasoning
Use the method paths shown above.
sonar-reasoningSonar Reasoning combines Perplexity’s search with reasoning for source-based questions and comparisons. It produces a text answer connected to web evidence, with the original question and citations retained for review.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Sonar Reasoning is Perplexity’s reasoning-enhanced search profile for questions that require interpreting evidence rather than returning a short isolated fact. Its maker identifies it separately from Sonar Reasoning Pro and the other Sonar search profiles. The useful result is a text answer connected to web-source evidence, with reasoning helping to compare material and develop a conclusion about the stated question.
Name the subject, the facts to establish and the criteria the answer should use. A product comparison can ask for the documented capabilities and limitations relevant to one workflow. A changing-event question should identify the event, time period and outcome to verify. Specify the intended artifact: a short explanation, an evidence table or a comparison organized around the decision being made.
Keep supplied background distinct from what the search should establish. If an important condition is missing, request that it remain open rather than being filled with a plausible assumption. A bounded question makes it easier to inspect whether the retrieved material actually answers it, especially when several sources discuss related but different cases.
The manufacturer describes Sonar Reasoning as part of its reasoning-enhanced search family. Use the returned material to compare alternatives, resolve an apparent disagreement or explain why a conclusion follows from the evidence. A source-backed reasoning answer should distinguish a page’s statement from the inference made by combining several pages.
For a requirements question, ask which source documents the feature and whether its conditions match the intended use. For a date-sensitive fact, preserve the source’s timing and the context in which it describes the event. A relevant-looking page can discuss an earlier version or a different case, so its relationship to the question matters more than the presence of a citation alone.
The producer’s Sonar response workflow associates an answer with source citations. Retain those references with the passages they support so another reader can inspect the evidence. A comparison table can keep the item, the stated capability, its supporting source and the condition that remains unresolved together. Check whether the cited material establishes the exact value or only a related claim.
A useful handoff can contain the original question, the synthesized answer and the sources used to support it. Preserve important names, numbers and source distinctions when carrying the result into a finished document. If the answer leaves a decisive criterion unresolved, narrow a follow-up around that criterion instead of treating the unsupported field as complete.
Sonar Reasoning and Reasoning Pro are separately named producer profiles. The later Agent API has its own models, tool primitives and request format, so those newer features should not be treated as this original profile’s native specification. Continue from the actual answer and its evidence, retaining the selected profile and task conditions when comparing results. The practical value is an assessable, source-connected reasoning artifact for a concrete research question.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits sonar-reasoning | Default— |
Fieldmessages | Typemessage[] | RequirementRequired | Values & limits | Default— |
Fieldmessages[].role | Typestring | RequirementRequired | Values & limits user, assistant | Default— |
Fieldmessages[].content | Typestring | RequirementRequired | Values & limits | Default— |
Fieldmax_tokens | Typeinteger | RequirementRequired | Values & limits | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
Text · UTF-8 text
messages[].content string
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, messages, max_tokens |
Parameteroptional_controls | Contractstream |
Parametermaterial_types | Contracttext |
Parametertext_file_material | ContractRead the actual UTF-8 document and put its text in the indicated input or message content string. |
Lifecycle: synchronous_or_streamed
Tariff references do not add request options. Supported parameters are listed for the selected method.
Match each charge to the conditions shown. Use one rate for each set of alternative configurations.
| Configuration | Rate schedule | Billing quantity | Charge |
|---|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Billing quantity— | Charge Input · Input tokens $0.0044 / 1K tokens Output · Output tokens $0.0176 / 1K tokens Input · Cache read $0 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
| ConfigurationPer request | Rate schedule— | Billing quantityone request | Charge Input $0.01 per request |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "sonar-reasoning",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\n: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'sonar-reasoning', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nPath('request.json').write_text(json.dumps(payload, ensure_ascii=False), encoding='utf-8')\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/messages \\\n -H "x-api-key: $AILOOT_API_KEY" -H 'anthropic-version: 2023-06-01' -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\nPY_RESULT\nimport json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'sonar-reasoning', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nfrom urllib.request import Request, urlopen\nfrom urllib.error import HTTPError\n\napi_key = os.environ['AILOOT_API_KEY']\nif not api_key.strip():\n raise SystemExit('Set AILOOT_API_KEY before making a request')\nheaders = {'x-api-key': 'PLACEHOLDER_KEY', 'anthropic-version': '2023-06-01', 'Content-Type': 'application/json'}\nheaders['x-api-key'] = api_key\nrequest = Request('https://api.ailoot.ai/v1/messages', data=json.dumps(payload, ensure_ascii=False).encode('utf-8'), headers=headers, method='POST')\ntry:\n with urlopen(request, timeout=120) as response:\n raw = response.read()\n Path('response-headers.txt').write_text(str(response.headers), encoding='utf-8')\nexcept HTTPError as error:\n Path('result.json').write_bytes(error.read())\n Path('response-headers.txt').write_text(str(error.headers), encoding='utf-8')\n raise\nPath('result.json').write_bytes(raw)\nimport json\nfrom pathlib import Path\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\nJSON or SSE
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.
Keep request/response IDs and headers. No generic text job retrieval URL is supplied.
| Field | Type |
|---|---|
Fieldid | Typestring |
Fieldmodel | Typestring |
Fieldcontent[] | Typetext|thinking|tool_use|other content block |
Fieldstop_reason | Typestring|null |
Fieldusage | Typeobject |
| 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. |
Ailoot Ultra sonar-reasoning
Use the method paths shown above.
Technical example in English
Research a protocol distinction\n\nFind the IETF HTTP Semantics specification and explain how HEAD differs from GET for response content and metadata. Prepare a small evidence table with official source references, then give one practical use of HEAD. Distinguish what the specification requires from your example and leave any server-specific behavior unresolved.A source-backed comparison table and a clearly labelled application example with citations.Inspect the cited specification sections and check that each claim concerns the actual GET/HEAD distinction.