Anthropic Claude 3.7 Sonnet is a hybrid reasoning model: the same model can answer in standard mode or spend more time reflecting before it gives a final answer. Standard mode develops the capabilities of Claude 3.5 Sonnet; extended thinking adds step-by-step self-reflection for harder coding, mathematics, physics, and instruction-following tasks. The distinction is how much reasoning the model performs, rather than a switch to a separate reasoning model.
Anthropic's API design lets developers set a thinking budget to control the amount of reasoning work. Extra reflection competes with the space needed for the final response, so a complex task should specify both the problem and the deliverable. A code review might need a ranked list of defects and a small proposed patch, while a technical investigation might need assumptions, alternative explanations, and the evidence that would distinguish them. More thinking can improve a difficult answer, but it does not guarantee correctness or require the model to consume the entire available budget.
Coding and front-end web development are central strengths of this edition. Anthropic describes improvements in understanding complex codebases, planning code changes, advanced tool use, and full-stack updates. Use that capability with concrete project evidence: relevant source files, an error message, reproduction steps, the desired behaviour, and interfaces that must remain compatible. For a dashboard task, specify the data fields, user actions, layout constraints, and acceptance criteria. A useful result is a proposed implementation or explanation that can be checked against those inputs, not an unverified assertion that the application is finished.
Claude 3.7 Sonnet also supports agentic work in which an application provides tools and returns their results to the model. Anthropic demonstrates iterative function calls and computer interactions involving screen input, virtual mouse clicks, and keyboard actions. The model can use feedback to change its approach as a task progresses. Keep the objective and allowed actions explicit, and preserve a record of actual tool results. The separate Claude Code product adds a terminal-based coding environment; its file editing, command execution, and repository actions depend on that environment and are not capabilities that a text response performs by itself.
Extended thinking can expose a reasoning trace as well as the final answer. In this edition, Anthropic presents visible thinking in raw form and warns that it may contain incorrect, misleading, or incomplete intermediate ideas. The trace can be less conversational than the final response, and it is not guaranteed to faithfully explain every factor behind the model's decision. Treat it as material to inspect rather than proof that the conclusion is true. For practical review, ask the final answer to state its assumptions, separate observations from deductions, and identify what still needs checking.
The model is useful for debugging across several related files, planning a front-end change, comparing technical approaches, and working through a difficult analytical question. Prepare the smallest set of material that explains the problem, then ask for the particular output you need: a patch with rationale, a migration checklist, or a concise decision memo. For code, verify the proposed behaviour in the target project; for factual analysis, verify the evidence. This keeps the value of the model's deeper reasoning tied to a usable result rather than the length of its explanation.
", "aboutText": "Anthropic Claude 3.7 Sonnet is a hybrid reasoning model: the same model can answer in standard mode or spend more time reflecting before it gives a final answer. Standard mode develops the capabilities of Claude 3.5 Sonnet; extended thinking adds step-by-step self-reflection for harder coding, mathematics, physics, and instruction-following tasks. The distinction is how much reasoning the model performs, rather than a switch to a separate reasoning model.\n\nAnthropic's API design lets developers set a thinking budget to control the amount of reasoning work. Extra reflection competes with the space needed for the final response, so a complex task should specify both the problem and the deliverable. A code review might need a ranked list of defects and a small proposed patch, while a technical investigation might need assumptions, alternative explanations, and the evidence that would distinguish them. More thinking can improve a difficult answer, but it does not guarantee correctness or require the model to consume the entire available budget.\n\nCoding and front-end web development are central strengths of this edition. Anthropic describes improvements in understanding complex codebases, planning code changes, advanced tool use, and full-stack updates. Use that capability with concrete project evidence: relevant source files, an error message, reproduction steps, the desired behaviour, and interfaces that must remain compatible. For a dashboard task, specify the data fields, user actions, layout constraints, and acceptance criteria. A useful result is a proposed implementation or explanation that can be checked against those inputs, not an unverified assertion that the application is finished.\n\nClaude 3.7 Sonnet also supports agentic work in which an application provides tools and returns their results to the model. Anthropic demonstrates iterative function calls and computer interactions involving screen input, virtual mouse clicks, and keyboard actions. The model can use feedback to change its approach as a task progresses. Keep the objective and allowed actions explicit, and preserve a record of actual tool results. The separate Claude Code product adds a terminal-based coding environment; its file editing, command execution, and repository actions depend on that environment and are not capabilities that a text response performs by itself.\n\nExtended thinking can expose a reasoning trace as well as the final answer. In this edition, Anthropic presents visible thinking in raw form and warns that it may contain incorrect, misleading, or incomplete intermediate ideas. The trace can be less conversational than the final response, and it is not guaranteed to faithfully explain every factor behind the model's decision. Treat it as material to inspect rather than proof that the conclusion is true. For practical review, ask the final answer to state its assumptions, separate observations from deductions, and identify what still needs checking.\n\nThe model is useful for debugging across several related files, planning a front-end change, comparing technical approaches, and working through a difficult analytical question. Prepare the smallest set of material that explains the problem, then ask for the particular output you need: a patch with rationale, a migration checklist, or a concise decision memo. For code, verify the proposed behaviour in the target project; for factual analysis, verify the evidence. This keeps the value of the model's deeper reasoning tied to a usable result rather than the length of its explanation.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Correct a front-end search reset\n\nA React search box stores query and page. Its handler is setQuery(event.target.value), so typing while on page 4 leaves results on page 4. Propose the smallest handler change that resets page to 1 for a changed query. Include a short interaction test and explain whether clearing the query should follow the same rule.A small handler patch, one interaction-test scenario and the clearing behavior stated explicitly.The test begins on page 4 and checks both the new query and page 1 after editing.claude-3-7-sonnet-latestAnthropic Claude 3.7 Sonnet combines standard responses and optional extended thinking in one model. It is built for practical reasoning, coding, and front-end development, with deeper reflection for difficult tasks and tool-directed workflows when an application supplies the tools.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Anthropic Claude 3.7 Sonnet is a hybrid reasoning model: the same model can answer in standard mode or spend more time reflecting before it gives a final answer. Standard mode develops the capabilities of Claude 3.5 Sonnet; extended thinking adds step-by-step self-reflection for harder coding, mathematics, physics, and instruction-following tasks. The distinction is how much reasoning the model performs, rather than a switch to a separate reasoning model.
Anthropic's API design lets developers set a thinking budget to control the amount of reasoning work. Extra reflection competes with the space needed for the final response, so a complex task should specify both the problem and the deliverable. A code review might need a ranked list of defects and a small proposed patch, while a technical investigation might need assumptions, alternative explanations, and the evidence that would distinguish them. More thinking can improve a difficult answer, but it does not guarantee correctness or require the model to consume the entire available budget.
Coding and front-end web development are central strengths of this edition. Anthropic describes improvements in understanding complex codebases, planning code changes, advanced tool use, and full-stack updates. Use that capability with concrete project evidence: relevant source files, an error message, reproduction steps, the desired behaviour, and interfaces that must remain compatible. For a dashboard task, specify the data fields, user actions, layout constraints, and acceptance criteria. A useful result is a proposed implementation or explanation that can be checked against those inputs, not an unverified assertion that the application is finished.
Claude 3.7 Sonnet also supports agentic work in which an application provides tools and returns their results to the model. Anthropic demonstrates iterative function calls and computer interactions involving screen input, virtual mouse clicks, and keyboard actions. The model can use feedback to change its approach as a task progresses. Keep the objective and allowed actions explicit, and preserve a record of actual tool results. The separate Claude Code product adds a terminal-based coding environment; its file editing, command execution, and repository actions depend on that environment and are not capabilities that a text response performs by itself.
Extended thinking can expose a reasoning trace as well as the final answer. In this edition, Anthropic presents visible thinking in raw form and warns that it may contain incorrect, misleading, or incomplete intermediate ideas. The trace can be less conversational than the final response, and it is not guaranteed to faithfully explain every factor behind the model's decision. Treat it as material to inspect rather than proof that the conclusion is true. For practical review, ask the final answer to state its assumptions, separate observations from deductions, and identify what still needs checking.
The model is useful for debugging across several related files, planning a front-end change, comparing technical approaches, and working through a difficult analytical question. Prepare the smallest set of material that explains the problem, then ask for the particular output you need: a patch with rationale, a migration checklist, or a concise decision memo. For code, verify the proposed behaviour in the target project; for factual analysis, verify the evidence. This keeps the value of the model's deeper reasoning tied to a usable result rather than the length of its explanation.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits claude-3-7-sonnet-latest | 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-3-7-sonnet-latest", "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-3-7-sonnet-latest',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-3-7-sonnet-latest',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-3-7-sonnet-latest
Use the method paths shown above.
claude-3-7-sonnet-latestAnthropic Claude 3.7 Sonnet combines standard responses and optional extended thinking in one model. It is built for practical reasoning, coding, and front-end development, with deeper reflection for difficult tasks and tool-directed workflows when an application supplies the tools.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Anthropic Claude 3.7 Sonnet is a hybrid reasoning model: the same model can answer in standard mode or spend more time reflecting before it gives a final answer. Standard mode develops the capabilities of Claude 3.5 Sonnet; extended thinking adds step-by-step self-reflection for harder coding, mathematics, physics, and instruction-following tasks. The distinction is how much reasoning the model performs, rather than a switch to a separate reasoning model.
Anthropic's API design lets developers set a thinking budget to control the amount of reasoning work. Extra reflection competes with the space needed for the final response, so a complex task should specify both the problem and the deliverable. A code review might need a ranked list of defects and a small proposed patch, while a technical investigation might need assumptions, alternative explanations, and the evidence that would distinguish them. More thinking can improve a difficult answer, but it does not guarantee correctness or require the model to consume the entire available budget.
Coding and front-end web development are central strengths of this edition. Anthropic describes improvements in understanding complex codebases, planning code changes, advanced tool use, and full-stack updates. Use that capability with concrete project evidence: relevant source files, an error message, reproduction steps, the desired behaviour, and interfaces that must remain compatible. For a dashboard task, specify the data fields, user actions, layout constraints, and acceptance criteria. A useful result is a proposed implementation or explanation that can be checked against those inputs, not an unverified assertion that the application is finished.
Claude 3.7 Sonnet also supports agentic work in which an application provides tools and returns their results to the model. Anthropic demonstrates iterative function calls and computer interactions involving screen input, virtual mouse clicks, and keyboard actions. The model can use feedback to change its approach as a task progresses. Keep the objective and allowed actions explicit, and preserve a record of actual tool results. The separate Claude Code product adds a terminal-based coding environment; its file editing, command execution, and repository actions depend on that environment and are not capabilities that a text response performs by itself.
Extended thinking can expose a reasoning trace as well as the final answer. In this edition, Anthropic presents visible thinking in raw form and warns that it may contain incorrect, misleading, or incomplete intermediate ideas. The trace can be less conversational than the final response, and it is not guaranteed to faithfully explain every factor behind the model's decision. Treat it as material to inspect rather than proof that the conclusion is true. For practical review, ask the final answer to state its assumptions, separate observations from deductions, and identify what still needs checking.
The model is useful for debugging across several related files, planning a front-end change, comparing technical approaches, and working through a difficult analytical question. Prepare the smallest set of material that explains the problem, then ask for the particular output you need: a patch with rationale, a migration checklist, or a concise decision memo. For code, verify the proposed behaviour in the target project; for factual analysis, verify the evidence. This keeps the value of the model's deeper reasoning tied to a usable result rather than the length of its explanation.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits claude-3-7-sonnet-latest | 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 $0.0075 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "claude-3-7-sonnet-latest",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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-3-7-sonnet-latest', '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-3-7-sonnet-latest', '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-3-7-sonnet-latest
Use the method paths shown above.
Technical example in English
Correct a front-end search reset\n\nA React search box stores query and page. Its handler is setQuery(event.target.value), so typing while on page 4 leaves results on page 4. Propose the smallest handler change that resets page to 1 for a changed query. Include a short interaction test and explain whether clearing the query should follow the same rule.A small handler patch, one interaction-test scenario and the clearing behavior stated explicitly.The test begins on page 4 and checks both the new query and page 1 after editing.