Claude Sonnet 4.6 combines coding, understanding of large context, agentic planning, professional information work and design. Anthropic highlights studying code before making changes and the ability to work through multiple task steps in sequence. This is useful for finding a problem in a large project, analyzing a long document and preparing report content.
The model can analyze information from contracts, research papers and other material in one request. A task may include extracting facts, reasoning from them and forming a result that brings several parts together. Described uses also include financial analysis and creation of interface code with attention to composition and presentation.
Sonnet 4.6 understands text and images; its own output is text. Anthropic describes computer-use scenarios where the model navigates from a screenshot, chooses mouse and keyboard actions, analyzes a complex table or works through a sequence of web-form steps. Actual execution requires application tools that provide the screen and perform the actions.
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.
Similarly, documents, spreadsheets and presentations can be analyzed or have their content prepared, but producing a finished file requires a suitable environment. Images help the model understand charts, diagrams and the visual structure of material; this does not turn it into an image generator.
This version supports adaptive reasoning and manual extended thinking. Manual mode is marked as deprecated; adaptive mode lets the model choose when additional analysis is useful, including between tool calls. The effort levels are low, medium, high and max, with high as the default. According to Anthropic, effort selection helps match the level of work and latency to the nature of the task.
The current specification provides 1 million tokens of context and up to 128 thousand response tokens in ordinary mode. For sustained work, the manufacturer also describes experimental compaction of old context into a summary. This feature requires application support and does not remove the model's context-window limits.
The reliable built-in knowledge cutoff is August 2025. Later information requires task material or available search. Connected tools can help find information, execute code and process discovered content; their availability and permitted actions are determined by the application.
Sonnet 4.6 was trained to resist hidden malicious instructions in external material, but complex visual environments and ambiguous tasks can still lead to errors. Explicit task requirements, verification of extracted facts and assessment of action results remain part of reliable work with the model.
", "aboutText": "### Code, documents and an action plan\n\nClaude Sonnet 4.6 combines coding, understanding of large context, agentic planning, professional information work and design. Anthropic highlights studying code before making changes and the ability to work through multiple task steps in sequence. This is useful for finding a problem in a large project, analyzing a long document and preparing report content.\n\nThe model can analyze information from contracts, research papers and other material in one request. A task may include extracting facts, reasoning from them and forming a result that brings several parts together. Described uses also include financial analysis and creation of interface code with attention to composition and presentation.\n\n### Actions in a visual environment\n\nSonnet 4.6 understands **text and images**; its own output is **text**. Anthropic describes computer-use scenarios where the model navigates from a screenshot, chooses mouse and keyboard actions, analyzes a complex table or works through a sequence of web-form steps. Actual execution requires application tools that provide the screen and perform the actions.\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\nSimilarly, documents, spreadsheets and presentations can be analyzed or have their content prepared, but producing a finished file requires a suitable environment. Images help the model understand charts, diagrams and the visual structure of material; this does not turn it into an image generator.\n\n### How analysis is controlled\n\nThis version supports adaptive reasoning and manual extended thinking. Manual mode is marked as deprecated; adaptive mode lets the model choose when additional analysis is useful, including between tool calls. The `effort` levels are `low`, `medium`, `high` and `max`, with `high` as the default. According to Anthropic, effort selection helps match the level of work and latency to the nature of the task.\n\nThe current specification provides **1 million tokens of context** and up to **128 thousand response tokens in ordinary mode**. For sustained work, the manufacturer also describes experimental compaction of old context into a summary. This feature requires application support and does not remove the model's context-window limits.\n\n### Points to consider when working\n\nThe reliable built-in knowledge cutoff is **August 2025**. Later information requires task material or available search. Connected tools can help find information, execute code and process discovered content; their availability and permitted actions are determined by the application.\n\nSonnet 4.6 was trained to resist hidden malicious instructions in external material, but complex visual environments and ambiguous tasks can still lead to errors. Explicit task requirements, verification of extracted facts and assessment of action results remain part of reliable work with the model.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "58d00dca909c54f5b85629dd73c76aeb57d5a25b7489f9500badf7afdec0eb4f", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "en", "methods": [ { "html": "anthropic/claude-sonnet-4.6:batchClaude Sonnet 4.6 helps with development, understanding long documents, planning and tasks involving visual data. Supports adaptive reasoning and multi-step tool use.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Claude Sonnet 4.6 combines coding, understanding of large context, agentic planning, professional information work and design. Anthropic highlights studying code before making changes and the ability to work through multiple task steps in sequence. This is useful for finding a problem in a large project, analyzing a long document and preparing report content.
The model can analyze information from contracts, research papers and other material in one request. A task may include extracting facts, reasoning from them and forming a result that brings several parts together. Described uses also include financial analysis and creation of interface code with attention to composition and presentation.
Sonnet 4.6 understands text and images; its own output is text. Anthropic describes computer-use scenarios where the model navigates from a screenshot, chooses mouse and keyboard actions, analyzes a complex table or works through a sequence of web-form steps. Actual execution requires application tools that provide the screen and perform the actions.
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.
Similarly, documents, spreadsheets and presentations can be analyzed or have their content prepared, but producing a finished file requires a suitable environment. Images help the model understand charts, diagrams and the visual structure of material; this does not turn it into an image generator.
This version supports adaptive reasoning and manual extended thinking. Manual mode is marked as deprecated; adaptive mode lets the model choose when additional analysis is useful, including between tool calls. The effort levels are low, medium, high and max, with high as the default. According to Anthropic, effort selection helps match the level of work and latency to the nature of the task.
The current specification provides 1 million tokens of context and up to 128 thousand response tokens in ordinary mode. For sustained work, the manufacturer also describes experimental compaction of old context into a summary. This feature requires application support and does not remove the model's context-window limits.
The reliable built-in knowledge cutoff is August 2025. Later information requires task material or available search. Connected tools can help find information, execute code and process discovered content; their availability and permitted actions are determined by the application.
Sonnet 4.6 was trained to resist hidden malicious instructions in external material, but complex visual environments and ambiguous tasks can still lead to errors. Explicit task requirements, verification of extracted facts and assessment of action results remain part of reliable work with the model.
POST /v1/batchestexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldendpoint | Typestring | RequirementRequired | Values & limits /v1/chat/completions | Default— |
Fieldmodel | Typestring | RequirementRequired | Values & limits anthropic/claude-sonnet-4.6:batch | Default— |
Fieldrequests | Typebatch_item[] | RequirementRequired | Values & limits Minimum items: 1 | Default— |
Fieldrequests[].custom_id | Typestring | RequirementRequired | Values & limits | Default— |
Fieldrequests[].body.messages | Typemessage[] | RequirementRequired | Values & limits | Default— |
Fieldcompletion_window | Typestring | RequirementOptional | Values & limits 24h | Default— |
Fieldprovider | Typeobject|null | RequirementOptional | Values & limits | Default— |
Fieldrequests[].body.model | Typestring | RequirementOptional | Values & limits anthropic/claude-sonnet-4.6:batch, anthropic/claude-sonnet-4.6 | Default— |
Fieldprovider.only | Typestring[] | RequirementOptional | Values & limits anthropic | Default— |
Fieldprovider.allow_fallbacks | Typeboolean|null | RequirementOptional | Values & limits false, null | Default— |
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractendpoint, model, requests |
Parametermaterial_types | Contracttext |
Parameterrepresentation | Contractinline requests[]; each item has custom_id and body |
Parameterrequest_endpoint_profile | Contract/v1/chat/completions |
Parameterfile_material | ContractCaller may read actual UTF-8 JSONL locally into requests[]. No upload purpose, input_file_id or file reference is used. Missing/empty customer material stops preparation before POST. |
Parameterserialization_order | Contractendpoint, model, provider (if supplied), completion_window (if supplied), requests last |
Parameteroptional_controls | Contractprovider.only, provider.allow_fallbacks, completion_window |
Parameterschema_scope | ContractText Chat Completions bodies in the documented inline Batch envelope. |
Lifecycle: asynchronous
GET /v1/batches/{operation_id} · actual original create response.id; same user/key/group
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 $0.003 / 1K tokens Output · Output tokens $0.015 / 1K tokens Input · Cache read $0.0003 / 1K tokens Input · Cache write $0.00375 / 1K tokens Input · Cache write, 1 hour $0.006 / 1K tokens |
| ConfigurationRequest-length tier 1 | Rate schedule— | Context tokens, more than0 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.003 / 1K tokens Output · Output tokens $0.015 / 1K tokens Input · Cache read $0.0003 / 1K tokens Input · Cache write $0.00375 / 1K tokens Input · Cache write, 1 hour $0.006 / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json\nfrom pathlib import Path\nbody=json.loads('{"endpoint":"/v1/chat/completions","model":"anthropic/claude-sonnet-4.6:batch","requests":[{"custom_id":"req-0001","body":{"messages":[{"role":"user","content":"Hello!"}],"max_tokens":16}}]}')\nPath("batch-request.json").write_text(json.dumps(body,ensure_ascii=False),encoding="utf-8")\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/batches \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n --data-binary @batch-request.json -D batch-response-headers.txt -o batch-create-response.json\nimport json, os\nfrom pathlib import Path\nfrom urllib.request import Request, urlopen\n\ndef prepare_requests(customer_path):\n path = Path(customer_path)\n if not path.is_file(): raise ValueError("Customer JSONL file is missing")\n items = [json.loads(line) for line in path.read_text(encoding="utf-8").splitlines() if line.strip()]\n if not items: raise ValueError("Customer JSONL contains no requests")\n ids = set()\n for item in items:\n if set(item) != {"custom_id", "body"}: raise ValueError("Each item needs custom_id and body")\n cid = item["custom_id"]\n if not isinstance(cid, str) or not cid.strip() or cid in ids: raise ValueError("custom_id must be nonempty and unique")\n ids.add(cid)\n if not isinstance(item["body"], dict) or not item["body"].get("messages"): raise ValueError("Supply actual text messages")\n supplied_model = item["body"].get("model")\n if supplied_model is not None and supplied_model not in ['anthropic/claude-sonnet-4.6:batch', 'anthropic/claude-sonnet-4.6']: raise ValueError("body.model must be an exact documented caller or wire ID")\n return {"endpoint":"/v1/chat/completions", "model":"anthropic/claude-sonnet-4.6:batch", "requests":items}\n\nif __name__ == "__main__":\n material = os.environ.get("AILOOT_BATCH_REQUESTS_JSONL", "")\n if not material: raise SystemExit("Set AILOOT_BATCH_REQUESTS_JSONL to your actual customer file")\n body = prepare_requests(material)\n key = os.environ.get("AILOOT_API_KEY", "")\n if not key: raise SystemExit("Set AILOOT_API_KEY")\n request = Request("https://api.ailoot.ai/v1/batches", data=json.dumps(body, ensure_ascii=False).encode("utf-8"), headers={"Authorization":"Bearer " + key, "Content-Type":"application/json"}, method="POST")\n with urlopen(request) as response:\n raw = response.read()\n Path("batch-create-response.json").write_bytes(raw)\n print(raw.decode("utf-8"))\nimport json\nfrom pathlib import Path\nraw = Path("batch-status-response.json").read_bytes()\nresponse = json.loads(raw)\nif response.get("status") != "completed":\n raise SystemExit("Keep the original operation; read its status without resubmitting")\nresults = response.get("results")\nif not isinstance(results, list) or not results:\n raise SystemExit("Preserve the original response; completed results are missing")\nPath("batch-results.jsonl").write_text("".join(json.dumps(item, ensure_ascii=False) + "\\n" for item in results), encoding="utf-8")\n# Preserve batch-status-response.json with aggregate usage and every result/error.\ninline results[].response.body JSON
GET /v1/batches/{operation_id}set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\nBATCH_OPERATION_ID="$(python3 - <<'PY_ID'\nimport json\nfrom pathlib import Path\nfrom urllib.parse import quote\nresponse = json.loads(Path("batch-create-response.json").read_text(encoding="utf-8"))\noperation = response.get("id")\nif not isinstance(operation, str) or not operation: raise SystemExit("No actual original operation ID; preserve the response and do not resubmit")\nprint(quote(operation, safe=""))\nPY_ID\n)"\ncurl --fail-with-body --silent --show-error "https://api.ailoot.ai/v1/batches/$BATCH_OPERATION_ID" \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -o batch-status-response.json\nKeep every complete returned response, file and content part.
Preserve the full original status JSON, every result/custom_id and complete response or error body. Save completed results[] as JSONL without filtering.
Preserve the original operation ID. 503 result_pending_check has X-Should-Retry:false; never resubmit an unknown original batch.
| Field | Type |
|---|---|
Fieldresults[].custom_id | Typestring |
Fieldresults[].response.body.choices[].message.content | Typestring|null |
Fieldresults[].error | Typeobject|null |
Fieldusage | Typeobject|null |
Fieldid | Typestring |
Fieldmodel | Typestring |
Fieldendpoint | Typestring |
Fieldstatus | Typestring |
Fieldrequest_counts | Typeobject |
Fieldresults[].id | Typestring |
Fieldresults[].response.status_code | Typeinteger |
Fieldresults[].response.request_id | Typestring |
| Response | Condition | Next step |
|---|---|---|
Response400 · invalid_request_error | ConditionInvalid documented inline envelope, duplicate keys, item identity, or configured model capabilities conflict. | Next stepCorrect the indicated fields before any new submission. |
Response401 · authentication_error | ConditionInvalid API key. | Next stepUse the authorized Ailoot key. |
Response403 · permission_error | ConditionBatch group unavailable. | Next stepCheck key/group entitlement. |
Response422 · invalid_request_error | ConditionBatch uses balance billing; subscription creation is rejected. | Next stepUse the authorized balance-billed group. |
Response422 · price_unavailable | ConditionBatch input cannot be quoted. | Next stepPreserve response; use documented family input. |
Response402 · billing_error | ConditionBatch reserve unavailable. | Next stepResolve admission before a new request. |
Response503 · price_unavailable | ConditionBatch method price/native rates unavailable. | Next stepPreserve response; do not substitute prices. |
Response503 · api_error | ConditionExact supplier route unavailable. | Next stepPreserve response for route repair. |
Response503 · result_pending_check | ConditionOriginal result requires checking; X-Should-Retry:false. | Next stepKeep original operation_id/id and read its status; never repeat original POST. |
Response404 · not_found | ConditionBatch status operation absent or owned by another identity. | Next stepUse exact original id and same user/key/group. |
Ailoot Ultra anthropic/claude-sonnet-4.6:batch
Use the method paths shown above.