Claude Opus 4 is Anthropic’s original fourth-generation Opus model for complex problem-solving, coding, research and writing. Its maker describes sustained work on longer tasks and agent workflows. This entry retains the named 20250514 profile rather than applying the features of a later Opus edition.
Give the objective, relevant source material and the conditions that establish completion. A software task can provide the code area involved, expected behavior and the failure to resolve. A research or writing task can identify the audience, questions to answer and source passages that should support the result.
For work with several dependent steps, ask for a final artifact together with the observations needed to assess it. A proposed code change should remain connected to the behavior it addresses. A report should distinguish information supplied by the sources from the interpretation built from it.
The maker describes Opus 4 as a hybrid model with a direct-response mode and an extended-thinking mode. It also documents using tools during extended thinking and making tool calls in parallel. These capabilities support an application that alternates between a question, a selected tool action, its observation and a later answer.
The surrounding application supplies tool access and executes the actions. If a workflow requires a lookup, a code run or a file change, provide the relevant tool contract and inspect the returned observation. Choosing a tool in a response and completing the external action are separate stages of the workflow.
Anthropic demonstrates maintaining useful task information in files when developers give the application local-file access. Such a file can hold a project decision, a navigation note or information needed after several turns. Define what should be retained and keep it with the task’s artifacts, so a continuation has the source material it needs.
The application determines where files live and when they are read or written. A model’s ability to produce or update useful notes works through that configured access. For a longer task, keep the current objective and evidence clear when resuming, rather than relying on an unlabelled accumulation of earlier messages.
The named profile publishes a 200,000-token context. Organize relevant code, source passages and dialogue around the task, with room for the response. Select the thinking and tool settings actually exposed by the interface used for this edition. The maker’s launch description establishes the original model’s workflow capabilities; it does not make every later Opus control or application service part of this profile.
", "aboutText": "Claude Opus 4 is Anthropic’s original fourth-generation Opus model for complex problem-solving, coding, research and writing. Its maker describes sustained work on longer tasks and agent workflows. This entry retains the named 20250514 profile rather than applying the features of a later Opus edition.\n\n### Prepare a task with a checkable result\n\nGive the objective, relevant source material and the conditions that establish completion. A software task can provide the code area involved, expected behavior and the failure to resolve. A research or writing task can identify the audience, questions to answer and source passages that should support the result.\n\nFor work with several dependent steps, ask for a final artifact together with the observations needed to assess it. A proposed code change should remain connected to the behavior it addresses. A report should distinguish information supplied by the sources from the interpretation built from it.\n\n### Connect reasoning with tools\n\nThe maker describes Opus 4 as a hybrid model with a direct-response mode and an extended-thinking mode. It also documents using tools during extended thinking and making tool calls in parallel. These capabilities support an application that alternates between a question, a selected tool action, its observation and a later answer.\n\nThe surrounding application supplies tool access and executes the actions. If a workflow requires a lookup, a code run or a file change, provide the relevant tool contract and inspect the returned observation. Choosing a tool in a response and completing the external action are separate stages of the workflow.\n\n### Maintain task continuity with files\n\nAnthropic demonstrates maintaining useful task information in files when developers give the application local-file access. Such a file can hold a project decision, a navigation note or information needed after several turns. Define what should be retained and keep it with the task’s artifacts, so a continuation has the source material it needs.\n\nThe application determines where files live and when they are read or written. A model’s ability to produce or update useful notes works through that configured access. For a longer task, keep the current objective and evidence clear when resuming, rather than relying on an unlabelled accumulation of earlier messages.\n\n### Use the exact profile’s settings\n\nThe named profile publishes a 200,000-token context. Organize relevant code, source passages and dialogue around the task, with room for the response. Select the thinking and tool settings actually exposed by the interface used for this edition. The maker’s launch description establishes the original model’s workflow capabilities; it does not make every later Opus control or application service part of this profile.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Plan a staged file-format migration\n\nA service writes records as {name, size} and reads both those records and newer {name, bytes} records. New writers must use bytes while old readers remain deployed for one week. Propose a staged migration, identify the compatibility risk and give a rollback condition. Return the changes needed in reader, writer and tests.A staged migration note with a compatibility matrix, reader/writer changes, regression cases and rollback condition.Check that the plan addresses old readers before new-only writes begin and preserves existing records during rollback.claude-opus-4-20250514Claude Opus 4 is Anthropic’s model for complex coding, reasoning, research and writing. The 20250514 profile preserves the original Opus 4 identity for a task that needs connected steps and an application-controlled tool workflow.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Claude Opus 4 is Anthropic’s original fourth-generation Opus model for complex problem-solving, coding, research and writing. Its maker describes sustained work on longer tasks and agent workflows. This entry retains the named 20250514 profile rather than applying the features of a later Opus edition.
Give the objective, relevant source material and the conditions that establish completion. A software task can provide the code area involved, expected behavior and the failure to resolve. A research or writing task can identify the audience, questions to answer and source passages that should support the result.
For work with several dependent steps, ask for a final artifact together with the observations needed to assess it. A proposed code change should remain connected to the behavior it addresses. A report should distinguish information supplied by the sources from the interpretation built from it.
The maker describes Opus 4 as a hybrid model with a direct-response mode and an extended-thinking mode. It also documents using tools during extended thinking and making tool calls in parallel. These capabilities support an application that alternates between a question, a selected tool action, its observation and a later answer.
The surrounding application supplies tool access and executes the actions. If a workflow requires a lookup, a code run or a file change, provide the relevant tool contract and inspect the returned observation. Choosing a tool in a response and completing the external action are separate stages of the workflow.
Anthropic demonstrates maintaining useful task information in files when developers give the application local-file access. Such a file can hold a project decision, a navigation note or information needed after several turns. Define what should be retained and keep it with the task’s artifacts, so a continuation has the source material it needs.
The application determines where files live and when they are read or written. A model’s ability to produce or update useful notes works through that configured access. For a longer task, keep the current objective and evidence clear when resuming, rather than relying on an unlabelled accumulation of earlier messages.
The named profile publishes a 200,000-token context. Organize relevant code, source passages and dialogue around the task, with room for the response. Select the thinking and tool settings actually exposed by the interface used for this edition. The maker’s launch description establishes the original model’s workflow capabilities; it does not make every later Opus control or application service part of this profile.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits claude-opus-4-20250514 | 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.
| Configuration | Rate schedule | Charge |
|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Charge Input · Input tokens $0.03 / 1K tokens Output · Output tokens $0.15 / 1K tokens Input · Cache read $0.003 / 1K tokens Input · Cache write $0.0375 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "claude-opus-4-20250514", "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='claude-opus-4-20250514',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='claude-opus-4-20250514',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 claude-opus-4-20250514
Use the method paths shown above.
claude-opus-4-20250514Claude Opus 4 is Anthropic’s model for complex coding, reasoning, research and writing. The 20250514 profile preserves the original Opus 4 identity for a task that needs connected steps and an application-controlled tool workflow.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Claude Opus 4 is Anthropic’s original fourth-generation Opus model for complex problem-solving, coding, research and writing. Its maker describes sustained work on longer tasks and agent workflows. This entry retains the named 20250514 profile rather than applying the features of a later Opus edition.
Give the objective, relevant source material and the conditions that establish completion. A software task can provide the code area involved, expected behavior and the failure to resolve. A research or writing task can identify the audience, questions to answer and source passages that should support the result.
For work with several dependent steps, ask for a final artifact together with the observations needed to assess it. A proposed code change should remain connected to the behavior it addresses. A report should distinguish information supplied by the sources from the interpretation built from it.
The maker describes Opus 4 as a hybrid model with a direct-response mode and an extended-thinking mode. It also documents using tools during extended thinking and making tool calls in parallel. These capabilities support an application that alternates between a question, a selected tool action, its observation and a later answer.
The surrounding application supplies tool access and executes the actions. If a workflow requires a lookup, a code run or a file change, provide the relevant tool contract and inspect the returned observation. Choosing a tool in a response and completing the external action are separate stages of the workflow.
Anthropic demonstrates maintaining useful task information in files when developers give the application local-file access. Such a file can hold a project decision, a navigation note or information needed after several turns. Define what should be retained and keep it with the task’s artifacts, so a continuation has the source material it needs.
The application determines where files live and when they are read or written. A model’s ability to produce or update useful notes works through that configured access. For a longer task, keep the current objective and evidence clear when resuming, rather than relying on an unlabelled accumulation of earlier messages.
The named profile publishes a 200,000-token context. Organize relevant code, source passages and dialogue around the task, with room for the response. Select the thinking and tool settings actually exposed by the interface used for this edition. The maker’s launch description establishes the original model’s workflow capabilities; it does not make every later Opus control or application service part of this profile.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits claude-opus-4-20250514 | 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.
| Configuration | Rate schedule | Charge |
|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Charge Input · Input tokens $0.03 / 1K tokens Output · Output tokens $0.15 / 1K tokens Input · Cache read $0.003 / 1K tokens Input · Cache write $0.0375 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "claude-opus-4-20250514",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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': 'claude-opus-4-20250514', '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': 'claude-opus-4-20250514', '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 claude-opus-4-20250514
Use the method paths shown above.
Technical example in English
Plan a staged file-format migration\n\nA service writes records as {name, size} and reads both those records and newer {name, bytes} records. New writers must use bytes while old readers remain deployed for one week. Propose a staged migration, identify the compatibility risk and give a rollback condition. Return the changes needed in reader, writer and tests.A staged migration note with a compatibility matrix, reader/writer changes, regression cases and rollback condition.Check that the plan addresses old readers before new-only writes begin and preserves existing records during rollback.