Claude Opus 4.6 is intended for work where it is useful to keep extensive material in one context and reason after reading it. Anthropic highlights development in large codebases, code review, debugging, financial analysis and research. The model can find important details, connect information from different parts of the material and use it to solve a task sequentially.
In development, this means working on a plan, studying an unfamiliar system and finding errors in changes. In professional analysis, it means processing documents and preparing the content of spreadsheets, reports or presentations. For these scenarios, the model needs the task itself and the material from which conclusions should be drawn, rather than just a general topic name.
The model supports adaptive mode: it determines from the prompt's content when additional analysis is useful. The effort parameter sets the level of work; Opus 4.6 supports low, medium, high and max, with high as the default. According to Anthropic, deeper analysis can increase response time on simple tasks, so the selected level should reflect their complexity.
Older extended thinking with a manual budget is still supported, but is marked as deprecated. This version has an important distinction between modes: reasoning between tool calls is available in adaptive mode, but Opus 4.6 does not perform it in manual mode. With tools connected, adaptive analysis helps use the result of one action to choose the next.
Anthropic's current specification provides 1 million tokens of context and up to 128 thousand response tokens in ordinary mode. It accepts text and images, with text as its response. The reliable built-in knowledge cutoff is May 2025.
The manufacturer's standard visual input formats are JPEG/PNG/GIF/WebP, using the first frame of an animation. For native analysis, the longer dimension is 1568 pixels, with 1568 visual tokens; larger images are downscaled before analysis. File and parameter support depends on the application's method.
For extended conversations and agentic tasks, the manufacturer describes compaction of old context: earlier work is replaced by a summary as a specified threshold approaches. This capability is marked as experimental and requires application support; context size alone does not provide unlimited memory.
Opus 4.6 is designed for multi-step work with search, code and other available tools. The application environment must provide material reading, command execution or file production. Production actions, interface control and creation of a finished presentation depend on these tools; a text model does not receive them automatically.
Anthropic separately describes safety checks and protective measures for potentially dangerous cybersecurity tasks. For ordinary development, the model can help find and fix vulnerabilities, but the nature of a request affects whether it can answer. Conclusions drawn from a large body of material and proposed changes need to be checked against the actual data and execution results.
", "aboutText": "### A large task and the connections between its parts\n\nClaude Opus 4.6 is intended for work where it is useful to keep extensive material in one context and reason after reading it. Anthropic highlights development in large codebases, code review, debugging, financial analysis and research. The model can find important details, connect information from different parts of the material and use it to solve a task sequentially.\n\nIn development, this means working on a plan, studying an unfamiliar system and finding errors in changes. In professional analysis, it means processing documents and preparing the content of spreadsheets, reports or presentations. For these scenarios, the model needs the task itself and the material from which conclusions should be drawn, rather than just a general topic name.\n\n### Adaptive reasoning\n\nThe model supports adaptive mode: it determines from the prompt's content when additional analysis is useful. The `effort` parameter sets the level of work; Opus 4.6 supports `low`, `medium`, `high` and `max`, with `high` as the default. According to Anthropic, deeper analysis can increase response time on simple tasks, so the selected level should reflect their complexity.\n\nOlder extended thinking with a manual budget is still supported, but is marked as deprecated. This version has an important distinction between modes: reasoning between tool calls is available in adaptive mode, but Opus 4.6 does not perform it in manual mode. With tools connected, adaptive analysis helps use the result of one action to choose the next.\n\n### Context for sustained work\n\nAnthropic's current specification provides **1 million tokens of context** and up to **128 thousand response tokens in ordinary mode**. It accepts **text and images**, with text as its response. The reliable built-in knowledge cutoff is **May 2025**.\n\nThe manufacturer's standard visual input formats are JPEG/PNG/GIF/WebP, using the first frame of an animation. For native analysis, the longer dimension is 1568 pixels, with 1568 visual tokens; larger images are downscaled before analysis. File and parameter support depends on the application's method.\n\nFor extended conversations and agentic tasks, the manufacturer describes compaction of old context: earlier work is replaced by a summary as a specified threshold approaches. This capability is marked as experimental and requires application support; context size alone does not provide unlimited memory.\n\n### Tools and checking the result\n\nOpus 4.6 is designed for multi-step work with search, code and other available tools. The application environment must provide material reading, command execution or file production. Production actions, interface control and creation of a finished presentation depend on these tools; a text model does not receive them automatically.\n\nAnthropic separately describes safety checks and protective measures for potentially dangerous cybersecurity tasks. For ordinary development, the model can help find and fix vulnerabilities, but the nature of a request affects whether it can answer. Conclusions drawn from a large body of material and proposed changes need to be checked against the actual data and execution results.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Review an idempotency bug\n\nReview this payment-handler excerpt: if not exists(event.id): charge(event.amount); save(event.id). Two workers can process the same event concurrently. The database supports transactions and a unique event_id. Propose a minimal safe change, explain the crash window and give tests for concurrent delivery and a crash after charging. Do not assume the payment service supports idempotency.A focused review with the race, a proposed code or pseudocode patch, required payment-service guarantees and two test scenarios.Verify that the proposal handles the external charge boundary explicitly rather than claiming a database transaction alone prevents duplicate charges.anthropic/claude-opus-4.6Claude Opus 4.6 is designed for large codebases, debugging, research and analysis of long documents. Supports adaptive reasoning and a context window of up to 1 million tokens.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Claude Opus 4.6 is intended for work where it is useful to keep extensive material in one context and reason after reading it. Anthropic highlights development in large codebases, code review, debugging, financial analysis and research. The model can find important details, connect information from different parts of the material and use it to solve a task sequentially.
In development, this means working on a plan, studying an unfamiliar system and finding errors in changes. In professional analysis, it means processing documents and preparing the content of spreadsheets, reports or presentations. For these scenarios, the model needs the task itself and the material from which conclusions should be drawn, rather than just a general topic name.
The model supports adaptive mode: it determines from the prompt's content when additional analysis is useful. The effort parameter sets the level of work; Opus 4.6 supports low, medium, high and max, with high as the default. According to Anthropic, deeper analysis can increase response time on simple tasks, so the selected level should reflect their complexity.
Older extended thinking with a manual budget is still supported, but is marked as deprecated. This version has an important distinction between modes: reasoning between tool calls is available in adaptive mode, but Opus 4.6 does not perform it in manual mode. With tools connected, adaptive analysis helps use the result of one action to choose the next.
Anthropic's current specification provides 1 million tokens of context and up to 128 thousand response tokens in ordinary mode. It accepts text and images, with text as its response. The reliable built-in knowledge cutoff is May 2025.
The manufacturer's standard visual input formats are JPEG/PNG/GIF/WebP, using the first frame of an animation. For native analysis, the longer dimension is 1568 pixels, with 1568 visual tokens; larger images are downscaled before analysis. File and parameter support depends on the application's method.
For extended conversations and agentic tasks, the manufacturer describes compaction of old context: earlier work is replaced by a summary as a specified threshold approaches. This capability is marked as experimental and requires application support; context size alone does not provide unlimited memory.
Opus 4.6 is designed for multi-step work with search, code and other available tools. The application environment must provide material reading, command execution or file production. Production actions, interface control and creation of a finished presentation depend on these tools; a text model does not receive them automatically.
Anthropic separately describes safety checks and protective measures for potentially dangerous cybersecurity tasks. For ordinary development, the model can help find and fix vulnerabilities, but the nature of a request affects whether it can answer. Conclusions drawn from a large body of material and proposed changes need to be checked against the actual data and execution results.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits anthropic/claude-opus-4.6 | Default— |
Fieldmessages | Typemessage[] | RequirementRequired | Values & limits Minimum items: 1 | Default— |
Fieldmessages[].role | Typestring | RequirementRequired | Values & limits Examples: system, user, assistant, tool, function Conditions: These roles are explicitly represented by Source compatibility types; the exact model role set remains model-specific. Model-specific | Default— |
Fieldmessages[].content | Typestring|content_part[]|null | RequirementOptional | Values & limits | Default— |
Fieldmessages[].tool_calls | Typetool_call[] | RequirementOptional | Values & limits | Default— |
Fieldmessages[].tool_call_id | Typestring | RequirementOptional | Values & limits Applies when: role=tool; use actual returned ID | Default— |
Fieldmessages[].name | Typestring | RequirementOptional | Values & limits | Default— |
Fieldmessages[].reasoning_content | Typestring | RequirementOptional | Values & limits Applies when: Round-trip actual returned reasoning where this model uses it | Default— |
Fieldmessages[].reasoning_details | Typeobject|array | RequirementOptional | Values & limits Model-specific | Default— |
Fieldmax_tokens | Typeinteger | RequirementOptional | Values & limits Minimum: 1 Model-specific | Default— |
Fieldmax_completion_tokens | Typeinteger | RequirementOptional | Values & limits Minimum: 1 Model-specific | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
Fieldstream_options.include_usage | Typeboolean | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtemperature | Typenumber | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtop_p | Typenumber | RequirementOptional | Values & limits Model-specific | Default— |
Fieldstop | Typestring|string[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtools | Typefunction_tool[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtools[].type | Typestring | RequirementOptional | Values & limits function Model-specific | Default— |
Fieldtools[].function.name | Typestring | RequirementRequired | Values & limits Applies when: function tool supplied | Default— |
Fieldtools[].function.description | Typestring | RequirementOptional | Values & limits | Default— |
Fieldtools[].function.parameters | TypeJSON_schema | RequirementOptional | Values & limits | Default— |
Fieldtool_choice | Typestring|object | RequirementOptional | Values & limits Model-specific | Default— |
Fieldparallel_tool_calls | Typeboolean | RequirementOptional | Values & limits Model-specific | Default— |
Fieldresponse_format | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldreasoning_effort | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldreasoning | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldservice_tier | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldinstructions | Typestring | RequirementOptional | Values & limits Compatibility field | Default— |
Fieldfunctions | Typefunction[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldfunction_call | Typestring|object | RequirementOptional | Values & limits Model-specific | Default— |
Fieldprompt_cache_options | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldmodalities | Typestring[] | RequirementOptional | Values & limits text | Default— |
Fieldprovider | Typeobject | RequirementOptional | Values & limits Conditions: provider.options is rejected on this Ultra text route | Default— |
Fieldcache_control.ttl | Typestring | RequirementOptional | Values & limits Conditions: 1h is rejected in actual prompt-cache locations | Default— |
Text · UTF8 text
messages[].content
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, messages |
Parametermaterial_types | Contracttext |
Parametermessages | Contractmessage array with role/content shape |
Parameterfile_material | Contractactual UTF-8 document inserted as literal text; no file ID inferred |
Parameteroptional_controls | Contractstream, temperature, top_p, stop, max_tokens, max_completion_tokens, reasoning, response_format, tools, tool_choice |
Parametercontrol_scope | Contractmodel-specific; user function tools differ from hosted tools |
Parameteroutput_modalities | Contracttext |
Parameterhosted_operations | Contractfalse |
Parametermultimodal_input_binding | Contractnull |
Lifecycle: synchronous_or_streamed
Send exact model ID with messages.
Context length is not an output cap.
A text file may supply literal text; image/audio/file-object inputs need exact binding.
Tariff references do not add request options. Supported parameters are listed for the selected method.
Match each charge to the conditions shown. Use one rate for each set of alternative configurations.
| Configuration | Rate schedule | Context tokens, more than | Context tokens, up to and including | Charge |
|---|---|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Context tokens, more than— | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.01 / 1K tokens Output · Output tokens $0.05 / 1K tokens Input · Cache read $0.001 / 1K tokens Input · Cache write $0.0125 / 1K tokens Input · Cache write, 1 hour $0.02 / 1K tokens |
| ConfigurationRequest-length tier 1 | Rate schedule— | Context tokens, more than0 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.01 / 1K tokens Output · Output tokens $0.05 / 1K tokens Input · Cache read $0.001 / 1K tokens Input · Cache write $0.0125 / 1K tokens Input · Cache write, 1 hour $0.02 / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "anthropic/claude-opus-4.6", "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='anthropic/claude-opus-4.6',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='anthropic/claude-opus-4.6',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 anthropic/claude-opus-4.6
Use the method paths shown above.
anthropic/claude-opus-4.6Claude Opus 4.6 is designed for large codebases, debugging, research and analysis of long documents. Supports adaptive reasoning and a context window of up to 1 million tokens.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Claude Opus 4.6 is intended for work where it is useful to keep extensive material in one context and reason after reading it. Anthropic highlights development in large codebases, code review, debugging, financial analysis and research. The model can find important details, connect information from different parts of the material and use it to solve a task sequentially.
In development, this means working on a plan, studying an unfamiliar system and finding errors in changes. In professional analysis, it means processing documents and preparing the content of spreadsheets, reports or presentations. For these scenarios, the model needs the task itself and the material from which conclusions should be drawn, rather than just a general topic name.
The model supports adaptive mode: it determines from the prompt's content when additional analysis is useful. The effort parameter sets the level of work; Opus 4.6 supports low, medium, high and max, with high as the default. According to Anthropic, deeper analysis can increase response time on simple tasks, so the selected level should reflect their complexity.
Older extended thinking with a manual budget is still supported, but is marked as deprecated. This version has an important distinction between modes: reasoning between tool calls is available in adaptive mode, but Opus 4.6 does not perform it in manual mode. With tools connected, adaptive analysis helps use the result of one action to choose the next.
Anthropic's current specification provides 1 million tokens of context and up to 128 thousand response tokens in ordinary mode. It accepts text and images, with text as its response. The reliable built-in knowledge cutoff is May 2025.
The manufacturer's standard visual input formats are JPEG/PNG/GIF/WebP, using the first frame of an animation. For native analysis, the longer dimension is 1568 pixels, with 1568 visual tokens; larger images are downscaled before analysis. File and parameter support depends on the application's method.
For extended conversations and agentic tasks, the manufacturer describes compaction of old context: earlier work is replaced by a summary as a specified threshold approaches. This capability is marked as experimental and requires application support; context size alone does not provide unlimited memory.
Opus 4.6 is designed for multi-step work with search, code and other available tools. The application environment must provide material reading, command execution or file production. Production actions, interface control and creation of a finished presentation depend on these tools; a text model does not receive them automatically.
Anthropic separately describes safety checks and protective measures for potentially dangerous cybersecurity tasks. For ordinary development, the model can help find and fix vulnerabilities, but the nature of a request affects whether it can answer. Conclusions drawn from a large body of material and proposed changes need to be checked against the actual data and execution results.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits anthropic/claude-opus-4.6 | Default— |
Fieldmessages | Typemessage[] | RequirementRequired | Values & limits | Default— |
Fieldmessages[].role | Typestring | RequirementRequired | Values & limits user, assistant | Default— |
Fieldmessages[].content | Typestring | RequirementRequired | Values & limits | Default— |
Fieldmax_tokens | Typeinteger | RequirementRequired | Values & limits | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
Text · UTF-8 text
messages[].content string
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, messages, max_tokens |
Parameteroptional_controls | Contractstream |
Parametermaterial_types | Contracttext |
Parametertext_file_material | ContractRead the actual UTF-8 document and put its text in the indicated input or message content string. |
Lifecycle: synchronous_or_streamed
Tariff references do not add request options. Supported parameters are listed for the selected method.
Match each charge to the conditions shown. Use one rate for each set of alternative configurations.
| Configuration | Rate schedule | Context tokens, more than | Context tokens, up to and including | Charge |
|---|---|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Context tokens, more than— | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.01 / 1K tokens Output · Output tokens $0.05 / 1K tokens Input · Cache read $0.001 / 1K tokens Input · Cache write $0.0125 / 1K tokens Input · Cache write, 1 hour $0.02 / 1K tokens |
| ConfigurationRequest-length tier 1 | Rate schedule— | Context tokens, more than0 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.01 / 1K tokens Output · Output tokens $0.05 / 1K tokens Input · Cache read $0.001 / 1K tokens Input · Cache write $0.0125 / 1K tokens Input · Cache write, 1 hour $0.02 / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "anthropic/claude-opus-4.6",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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': 'anthropic/claude-opus-4.6', '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': 'anthropic/claude-opus-4.6', '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 anthropic/claude-opus-4.6
Use the method paths shown above.
Technical example in English
Review an idempotency bug\n\nReview this payment-handler excerpt: if not exists(event.id): charge(event.amount); save(event.id). Two workers can process the same event concurrently. The database supports transactions and a unique event_id. Propose a minimal safe change, explain the crash window and give tests for concurrent delivery and a crash after charging. Do not assume the payment service supports idempotency.A focused review with the race, a proposed code or pseudocode patch, required payment-service guarantees and two test scenarios.Verify that the proposal handles the external charge boundary explicitly rather than claiming a database transaction alone prevents duplicate charges.