Solar Mini 4 is Upstage’s compact mixture-of-experts model for repeated agent tasks. It has 35 billion total parameters and activates 3B per token. The producer specifies a 512K context and up to 128K output tokens, with English, Korean and Japanese input and output. The published training-data cutoff is February 2026.
Its main work areas include information retrieval, classification, structured output and tool use. A recurring extraction task should provide the source material and the fields needed by the application. For a classification task, define the categories and the evidence that distinguishes them. A useful result returns the required information without expanding the assignment into unrelated analysis.
The producer supports function calling and parallel calls. Define the available functions and their arguments; the model proposes calls, while the application executes them and returns results. Independent checks can be requested together, such as retrieving two pieces of state needed for the next decision. Keep each result associated with its call before continuing the conversation.
JSON mode and schema-based structured output serve different needs. JSON mode produces parseable JSON, while a supported schema controls field names and types. The manufacturer requires an object root, required properties and explicit rules for additional properties in its structured-output subset. Define how missing information should be represented and check the extracted values against the source, even when the format is valid.
Reasoning is off when effort is omitted in the producer’s current guide. None and minimal leave it off; low, medium, high, xhigh and max enable it. Reasoning consumes part of the output allocation, so a small budget can end before the final answer. Use the task’s dependent steps to decide whether thinking is useful, while retaining room for the artifact the application needs.
For a document pipeline, ask the model to compare the structured results from earlier processing, identify missing fields and apply the stated rules for the next step. The output can be a discrepancy record, a classification or a proposed action with its supporting data. The application decides what to execute and supplies the observed result for any follow-up.
Active parameter count describes computation, not the complete weight memory requirement. Upstage notes that the full 35B weights remain loaded in a local deployment and publishes separate quantized deployment conditions. Keep those configuration facts separate from a particular model selection’s execution environment.
", "aboutText": "Solar Mini 4 is Upstage’s compact mixture-of-experts model for repeated agent tasks. It has 35 billion total parameters and activates 3B per token. The producer specifies a 512K context and up to 128K output tokens, with English, Korean and Japanese input and output. The published training-data cutoff is February 2026.\n\nIts main work areas include information retrieval, classification, structured output and tool use. A recurring extraction task should provide the source material and the fields needed by the application. For a classification task, define the categories and the evidence that distinguishes them. A useful result returns the required information without expanding the assignment into unrelated analysis.\n\nThe producer supports function calling and parallel calls. Define the available functions and their arguments; the model proposes calls, while the application executes them and returns results. Independent checks can be requested together, such as retrieving two pieces of state needed for the next decision. Keep each result associated with its call before continuing the conversation.\n\nJSON mode and schema-based structured output serve different needs. JSON mode produces parseable JSON, while a supported schema controls field names and types. The manufacturer requires an object root, required properties and explicit rules for additional properties in its structured-output subset. Define how missing information should be represented and check the extracted values against the source, even when the format is valid.\n\nReasoning is off when effort is omitted in the producer’s current guide. None and minimal leave it off; low, medium, high, xhigh and max enable it. Reasoning consumes part of the output allocation, so a small budget can end before the final answer. Use the task’s dependent steps to decide whether thinking is useful, while retaining room for the artifact the application needs.\n\nFor a document pipeline, ask the model to compare the structured results from earlier processing, identify missing fields and apply the stated rules for the next step. The output can be a discrepancy record, a classification or a proposed action with its supporting data. The application decides what to execute and supplies the observed result for any follow-up.\n\nActive parameter count describes computation, not the complete weight memory requirement. Upstage notes that the full 35B weights remain loaded in a local deployment and publishes separate quantized deployment conditions. Keep those configuration facts separate from a particular model selection’s execution environment.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Reconcile extracted invoice fields\n\nCompare two text-extraction records for invoice IV-902. Record A: total 240 EUR, due date missing. Record B: total 240 EUR, due date 18 August. Source excerpt: 'Invoice IV-902. Amount due: EUR 240. Pay by 18 August.' Return the reconciled fields, the discrepancy and the source phrase supporting the due date. Do not add a year.A compact reconciled record with a discrepancy note and the quoted source phrase for the due date.The total remains EUR 240 and the date does not acquire an unsupported year.upstage/solar-mini4Upstage’s Solar Mini 4 is a 35B MoE text model with 3B active parameters for repeated retrieval, extraction and tool workflows. It supports a 512K context, up to 128K output tokens, structured responses and parallel tool calls.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Solar Mini 4 is Upstage’s compact mixture-of-experts model for repeated agent tasks. It has 35 billion total parameters and activates 3B per token. The producer specifies a 512K context and up to 128K output tokens, with English, Korean and Japanese input and output. The published training-data cutoff is February 2026.
Its main work areas include information retrieval, classification, structured output and tool use. A recurring extraction task should provide the source material and the fields needed by the application. For a classification task, define the categories and the evidence that distinguishes them. A useful result returns the required information without expanding the assignment into unrelated analysis.
The producer supports function calling and parallel calls. Define the available functions and their arguments; the model proposes calls, while the application executes them and returns results. Independent checks can be requested together, such as retrieving two pieces of state needed for the next decision. Keep each result associated with its call before continuing the conversation.
JSON mode and schema-based structured output serve different needs. JSON mode produces parseable JSON, while a supported schema controls field names and types. The manufacturer requires an object root, required properties and explicit rules for additional properties in its structured-output subset. Define how missing information should be represented and check the extracted values against the source, even when the format is valid.
Reasoning is off when effort is omitted in the producer’s current guide. None and minimal leave it off; low, medium, high, xhigh and max enable it. Reasoning consumes part of the output allocation, so a small budget can end before the final answer. Use the task’s dependent steps to decide whether thinking is useful, while retaining room for the artifact the application needs.
For a document pipeline, ask the model to compare the structured results from earlier processing, identify missing fields and apply the stated rules for the next step. The output can be a discrepancy record, a classification or a proposed action with its supporting data. The application decides what to execute and supplies the observed result for any follow-up.
Active parameter count describes computation, not the complete weight memory requirement. Upstage notes that the full 35B weights remain loaded in a local deployment and publishes separate quantized deployment conditions. Keep those configuration facts separate from a particular model selection’s execution environment.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits upstage/solar-mini4 | 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 | Context tokens, more than | Context tokens, up to and including | Charge |
|---|---|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Context tokens, more than— | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.0002 / 1K tokens Output · Output tokens $0.0008 / 1K tokens Input · Cache read $0.00002 / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
| ConfigurationRequest-length tier 1 | Rate schedule— | Context tokens, more than0 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.0002 / 1K tokens Output · Output tokens $0.0008 / 1K tokens Input · Cache read $0.00002 / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "upstage/solar-mini4", "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='upstage/solar-mini4',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='upstage/solar-mini4',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 upstage/solar-mini4
Use the method paths shown above.
upstage/solar-mini4Upstage’s Solar Mini 4 is a 35B MoE text model with 3B active parameters for repeated retrieval, extraction and tool workflows. It supports a 512K context, up to 128K output tokens, structured responses and parallel tool calls.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Solar Mini 4 is Upstage’s compact mixture-of-experts model for repeated agent tasks. It has 35 billion total parameters and activates 3B per token. The producer specifies a 512K context and up to 128K output tokens, with English, Korean and Japanese input and output. The published training-data cutoff is February 2026.
Its main work areas include information retrieval, classification, structured output and tool use. A recurring extraction task should provide the source material and the fields needed by the application. For a classification task, define the categories and the evidence that distinguishes them. A useful result returns the required information without expanding the assignment into unrelated analysis.
The producer supports function calling and parallel calls. Define the available functions and their arguments; the model proposes calls, while the application executes them and returns results. Independent checks can be requested together, such as retrieving two pieces of state needed for the next decision. Keep each result associated with its call before continuing the conversation.
JSON mode and schema-based structured output serve different needs. JSON mode produces parseable JSON, while a supported schema controls field names and types. The manufacturer requires an object root, required properties and explicit rules for additional properties in its structured-output subset. Define how missing information should be represented and check the extracted values against the source, even when the format is valid.
Reasoning is off when effort is omitted in the producer’s current guide. None and minimal leave it off; low, medium, high, xhigh and max enable it. Reasoning consumes part of the output allocation, so a small budget can end before the final answer. Use the task’s dependent steps to decide whether thinking is useful, while retaining room for the artifact the application needs.
For a document pipeline, ask the model to compare the structured results from earlier processing, identify missing fields and apply the stated rules for the next step. The output can be a discrepancy record, a classification or a proposed action with its supporting data. The application decides what to execute and supplies the observed result for any follow-up.
Active parameter count describes computation, not the complete weight memory requirement. Upstage notes that the full 35B weights remain loaded in a local deployment and publishes separate quantized deployment conditions. Keep those configuration facts separate from a particular model selection’s execution environment.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits upstage/solar-mini4 | 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 | Context tokens, more than | Context tokens, up to and including | Charge |
|---|---|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Context tokens, more than— | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.0002 / 1K tokens Output · Output tokens $0.0008 / 1K tokens Input · Cache read $0.00002 / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
| ConfigurationRequest-length tier 1 | Rate schedule— | Context tokens, more than0 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.0002 / 1K tokens Output · Output tokens $0.0008 / 1K tokens Input · Cache read $0.00002 / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "upstage/solar-mini4",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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': 'upstage/solar-mini4', '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': 'upstage/solar-mini4', '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 upstage/solar-mini4
Use the method paths shown above.
Technical example in English
Reconcile extracted invoice fields\n\nCompare two text-extraction records for invoice IV-902. Record A: total 240 EUR, due date missing. Record B: total 240 EUR, due date 18 August. Source excerpt: 'Invoice IV-902. Amount due: EUR 240. Pay by 18 August.' Return the reconciled fields, the discrepancy and the source phrase supporting the due date. Do not add a year.A compact reconciled record with a discrepancy note and the quoted source phrase for the due date.The total remains EUR 240 and the date does not acquire an unsupported year.