This card preserves the original Claude 3.5 Sonnet edition. Anthropic’s original release describes writing, complex instruction following, coding and visual understanding, with a 200K-token context window. The dated model identifier distinguishes this edition from later Sonnet releases.
Provide the affected code, its role in the project and the behavior expected after the change. For a bug, include the triggering input and observed error. Ask for a focused proposal and a test that distinguishes it from the current behavior. For a migration, supply the original interface and the target conventions, then request a change that preserves the relevant behavior.
The manufacturer describes coding workflows in which the model receives tools for writing, editing and executing code. Those operations take place through the surrounding application and its configured tools. Keep the actual tool results with the conversation when asking the model to explain whether a change meets the task’s conditions.
Anthropic describes chart and graph interpretation and reading text from images among this edition’s visual capabilities. Label the supplied image and identify the question it should answer. A chart task can request the visible trend, the labeled values relevant to it and the part of the figure supporting the observation.
For a screenshot or document image, point to the relevant region and request the readable information. Supply written context when it explains why a detail matters. Retain image or page labels in the answer and check extracted values against the original source before using them in the next step.
A writing prompt can supply the audience, information to convey and intended artifact: a customer response, an explanation or a revised passage. State the desired length and the facts that must survive. For a context-sensitive reply, include the customer’s question and the relevant policy or product material.
Use the listed context for the source passages, relevant earlier decisions and the current request, leaving room for the result. Keep repeated background out of the brief. This edition produces content for the requested task; dedicated document, code or website displays belong to the application presenting that content.
", "aboutText": "This card preserves the original Claude 3.5 Sonnet edition. Anthropic’s original release describes writing, complex instruction following, coding and visual understanding, with a 200K-token context window. The dated model identifier distinguishes this edition from later Sonnet releases.\n\n### Work through a code change\n\nProvide the affected code, its role in the project and the behavior expected after the change. For a bug, include the triggering input and observed error. Ask for a focused proposal and a test that distinguishes it from the current behavior. For a migration, supply the original interface and the target conventions, then request a change that preserves the relevant behavior.\n\nThe manufacturer describes coding workflows in which the model receives tools for writing, editing and executing code. Those operations take place through the surrounding application and its configured tools. Keep the actual tool results with the conversation when asking the model to explain whether a change meets the task’s conditions.\n\n### Read visual material for a specific question\n\nAnthropic describes chart and graph interpretation and reading text from images among this edition’s visual capabilities. Label the supplied image and identify the question it should answer. A chart task can request the visible trend, the labeled values relevant to it and the part of the figure supporting the observation.\n\nFor a screenshot or document image, point to the relevant region and request the readable information. Supply written context when it explains why a detail matters. Retain image or page labels in the answer and check extracted values against the original source before using them in the next step.\n\n### Draft and revise with context\n\nA writing prompt can supply the audience, information to convey and intended artifact: a customer response, an explanation or a revised passage. State the desired length and the facts that must survive. For a context-sensitive reply, include the customer’s question and the relevant policy or product material.\n\nUse the listed context for the source passages, relevant earlier decisions and the current request, leaving room for the result. Keep repeated background out of the brief. This edition produces content for the requested task; dedicated document, code or website displays belong to the application presenting that content.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Preserve order while removing duplicates\n\nReview this Python function: def unique_ids(items): return list(set(items)). Input ['b', 'a', 'b', 'c'] must retain first-seen order. Propose a focused replacement that also handles an empty list. Return the function and two assertions; explain why the original does not promise the requested ordering.A replacement function, two assertions and a short explanation of ordering behavior.The sample must return b, a, c in that order, and empty input must return an empty list.claude-3-5-sonnet-20240620This original Claude 3.5 Sonnet edition handles writing, code work and visual understanding with a manufacturer-listed 200K context. Supply the relevant text, code or image and request a concrete artifact, such as a revision, proposed correction or chart explanation.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
This card preserves the original Claude 3.5 Sonnet edition. Anthropic’s original release describes writing, complex instruction following, coding and visual understanding, with a 200K-token context window. The dated model identifier distinguishes this edition from later Sonnet releases.
Provide the affected code, its role in the project and the behavior expected after the change. For a bug, include the triggering input and observed error. Ask for a focused proposal and a test that distinguishes it from the current behavior. For a migration, supply the original interface and the target conventions, then request a change that preserves the relevant behavior.
The manufacturer describes coding workflows in which the model receives tools for writing, editing and executing code. Those operations take place through the surrounding application and its configured tools. Keep the actual tool results with the conversation when asking the model to explain whether a change meets the task’s conditions.
Anthropic describes chart and graph interpretation and reading text from images among this edition’s visual capabilities. Label the supplied image and identify the question it should answer. A chart task can request the visible trend, the labeled values relevant to it and the part of the figure supporting the observation.
For a screenshot or document image, point to the relevant region and request the readable information. Supply written context when it explains why a detail matters. Retain image or page labels in the answer and check extracted values against the original source before using them in the next step.
A writing prompt can supply the audience, information to convey and intended artifact: a customer response, an explanation or a revised passage. State the desired length and the facts that must survive. For a context-sensitive reply, include the customer’s question and the relevant policy or product material.
Use the listed context for the source passages, relevant earlier decisions and the current request, leaving room for the result. Keep repeated background out of the brief. This edition produces content for the requested task; dedicated document, code or website displays belong to the application presenting that content.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits claude-3-5-sonnet-20240620 | 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 $0.006 / 1K tokens Output · Output tokens $0.03 / 1K tokens Input · Cache read $0.0006 / 1K tokens Input · Cache write $0.0075 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "claude-3-5-sonnet-20240620", "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-3-5-sonnet-20240620',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-3-5-sonnet-20240620',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-3-5-sonnet-20240620
Use the method paths shown above.
claude-3-5-sonnet-20240620This original Claude 3.5 Sonnet edition handles writing, code work and visual understanding with a manufacturer-listed 200K context. Supply the relevant text, code or image and request a concrete artifact, such as a revision, proposed correction or chart explanation.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
This card preserves the original Claude 3.5 Sonnet edition. Anthropic’s original release describes writing, complex instruction following, coding and visual understanding, with a 200K-token context window. The dated model identifier distinguishes this edition from later Sonnet releases.
Provide the affected code, its role in the project and the behavior expected after the change. For a bug, include the triggering input and observed error. Ask for a focused proposal and a test that distinguishes it from the current behavior. For a migration, supply the original interface and the target conventions, then request a change that preserves the relevant behavior.
The manufacturer describes coding workflows in which the model receives tools for writing, editing and executing code. Those operations take place through the surrounding application and its configured tools. Keep the actual tool results with the conversation when asking the model to explain whether a change meets the task’s conditions.
Anthropic describes chart and graph interpretation and reading text from images among this edition’s visual capabilities. Label the supplied image and identify the question it should answer. A chart task can request the visible trend, the labeled values relevant to it and the part of the figure supporting the observation.
For a screenshot or document image, point to the relevant region and request the readable information. Supply written context when it explains why a detail matters. Retain image or page labels in the answer and check extracted values against the original source before using them in the next step.
A writing prompt can supply the audience, information to convey and intended artifact: a customer response, an explanation or a revised passage. State the desired length and the facts that must survive. For a context-sensitive reply, include the customer’s question and the relevant policy or product material.
Use the listed context for the source passages, relevant earlier decisions and the current request, leaving room for the result. Keep repeated background out of the brief. This edition produces content for the requested task; dedicated document, code or website displays belong to the application presenting that content.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits claude-3-5-sonnet-20240620 | 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 $0.006 / 1K tokens Output · Output tokens $0.03 / 1K tokens Input · Cache read $0.0006 / 1K tokens Input · Cache write $0.0075 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "claude-3-5-sonnet-20240620",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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-3-5-sonnet-20240620', '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-3-5-sonnet-20240620', '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-3-5-sonnet-20240620
Use the method paths shown above.
Technical example in English
Preserve order while removing duplicates\n\nReview this Python function: def unique_ids(items): return list(set(items)). Input ['b', 'a', 'b', 'c'] must retain first-seen order. Propose a focused replacement that also handles an empty list. Return the function and two assertions; explain why the original does not promise the requested ordering.A replacement function, two assertions and a short explanation of ordering behavior.The sample must return b, a, c in that order, and empty input must return an empty list.