Anthropic develops Claude Opus 4.7 for complex, extended development tasks. Its presentation emphasizes carrying out work in sequence, close attention to instructions and finding ways to check its own result before the final message. The model is suited to investigating a difficult code problem and preparing professional material that requires both a technical solution and clear presentation.
Opus 4.7 follows wording literally. Prompts are best expressed through explicit boundaries: which parts should be changed, what should be checked and what the result should be. If a condition applies to every part of the work, it should be stated that way. The manufacturer also highlights the use of file-based memory notes for long tasks, but reading and storing such notes requires application tools.
The model accepts text and images and produces text. Visual input supports resolution up to 2576 pixels on the longer dimension, approximately 3.75 megapixels. This is useful for dense screenshots, extracting information from complex diagrams and tasks with detailed visual references.
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 2576 pixels, with 4784 visual tokens; larger images are downscaled before analysis. File and parameter support depends on the application's method.
High resolution helps preserve detail but does not make visual understanding infallible. Output can be an explanation, a data structure or code; creating or editing the image itself is not an output capability of the model. Interface control requires tools that show the screen to the model and perform actions in the application.
Context is 1 million tokens, and the maximum response in ordinary mode is 128 thousand tokens. Adaptive reasoning is supported; manual extended thinking with budget_tokens is absent in this version. The default effort level is high, with low, medium, xhigh and max also available. xhigh provides a separate setting between high and max for extended engineering and agentic work.
At low effort levels, the model limits its work particularly strictly to what was requested. For complex research, the manufacturer recommends leaving enough room for reasoning and the response. This version has an updated tokenizer, so the same amount of text does not always have the token length familiar from earlier versions.
The reliable built-in knowledge cutoff is January 2026. Current research, document work and code execution use material and tools provided by the application. Opus 4.7 has safety mechanisms that can block prohibited or high-risk cybersecurity requests. Its attention to checking results is useful, but final changes and conclusions still need to be verified in the specific environment.
", "aboutText": "### Careful engineering work\n\nAnthropic develops Claude Opus 4.7 for complex, extended development tasks. Its presentation emphasizes carrying out work in sequence, close attention to instructions and finding ways to check its own result before the final message. The model is suited to investigating a difficult code problem and preparing professional material that requires both a technical solution and clear presentation.\n\nOpus 4.7 follows wording literally. Prompts are best expressed through explicit boundaries: which parts should be changed, what should be checked and what the result should be. If a condition applies to every part of the work, it should be stated that way. The manufacturer also highlights the use of file-based memory notes for long tasks, but reading and storing such notes requires application tools.\n\n### Reading dense diagrams and interface screenshots\n\nThe model accepts **text and images** and produces **text**. Visual input supports resolution up to **2576 pixels on the longer dimension**, approximately **3.75 megapixels**. This is useful for dense screenshots, extracting information from complex diagrams and tasks with detailed visual references.\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 2576 pixels, with 4784 visual tokens; larger images are downscaled before analysis. File and parameter support depends on the application's method.\n\nHigh resolution helps preserve detail but does not make visual understanding infallible. Output can be an explanation, a data structure or code; creating or editing the image itself is not an output capability of the model. Interface control requires tools that show the screen to the model and perform actions in the application.\n\n### Task depth and capacity\n\nContext is **1 million tokens**, and the maximum response in ordinary mode is **128 thousand tokens**. Adaptive reasoning is supported; manual extended thinking with `budget_tokens` is absent in this version. The default `effort` level is `high`, with `low`, `medium`, `xhigh` and `max` also available. `xhigh` provides a separate setting between `high` and `max` for extended engineering and agentic work.\n\nAt low effort levels, the model limits its work particularly strictly to what was requested. For complex research, the manufacturer recommends leaving enough room for reasoning and the response. This version has an updated tokenizer, so the same amount of text does not always have the token length familiar from earlier versions.\n\n### Working conditions\n\nThe reliable built-in knowledge cutoff is **January 2026**. Current research, document work and code execution use material and tools provided by the application. Opus 4.7 has safety mechanisms that can block prohibited or high-risk cybersecurity requests. Its attention to checking results is useful, but final changes and conclusions still need to be verified in the specific environment.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "2da3025fe80fa2f96b4da0f15c1efa6f0359690b2dd095294c7ad1c1853d88b1", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "en", "methods": [ { "html": "anthropic/claude-opus-4.7:batchClaude Opus 4.7 is designed for complex engineering work and analysis of documents and finely detailed images. Places particular emphasis on following instructions precisely and checking results.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Anthropic develops Claude Opus 4.7 for complex, extended development tasks. Its presentation emphasizes carrying out work in sequence, close attention to instructions and finding ways to check its own result before the final message. The model is suited to investigating a difficult code problem and preparing professional material that requires both a technical solution and clear presentation.
Opus 4.7 follows wording literally. Prompts are best expressed through explicit boundaries: which parts should be changed, what should be checked and what the result should be. If a condition applies to every part of the work, it should be stated that way. The manufacturer also highlights the use of file-based memory notes for long tasks, but reading and storing such notes requires application tools.
The model accepts text and images and produces text. Visual input supports resolution up to 2576 pixels on the longer dimension, approximately 3.75 megapixels. This is useful for dense screenshots, extracting information from complex diagrams and tasks with detailed visual references.
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 2576 pixels, with 4784 visual tokens; larger images are downscaled before analysis. File and parameter support depends on the application's method.
High resolution helps preserve detail but does not make visual understanding infallible. Output can be an explanation, a data structure or code; creating or editing the image itself is not an output capability of the model. Interface control requires tools that show the screen to the model and perform actions in the application.
Context is 1 million tokens, and the maximum response in ordinary mode is 128 thousand tokens. Adaptive reasoning is supported; manual extended thinking with budget_tokens is absent in this version. The default effort level is high, with low, medium, xhigh and max also available. xhigh provides a separate setting between high and max for extended engineering and agentic work.
At low effort levels, the model limits its work particularly strictly to what was requested. For complex research, the manufacturer recommends leaving enough room for reasoning and the response. This version has an updated tokenizer, so the same amount of text does not always have the token length familiar from earlier versions.
The reliable built-in knowledge cutoff is January 2026. Current research, document work and code execution use material and tools provided by the application. Opus 4.7 has safety mechanisms that can block prohibited or high-risk cybersecurity requests. Its attention to checking results is useful, but final changes and conclusions still need to be verified in the specific environment.
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-opus-4.7: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-opus-4.7:batch, anthropic/claude-opus-4.7 | 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 $0.005 / 1K tokens Output · Output tokens $0.025 / 1K tokens Input · Cache read $0.0005 / 1K tokens Input · Cache write $0.00625 / 1K tokens Input · Cache write, 1 hour $0.01 / 1K tokens |
| ConfigurationRequest-length tier 1 | Rate schedule— | Context tokens, more than0 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.005 / 1K tokens Output · Output tokens $0.025 / 1K tokens Input · Cache read $0.0005 / 1K tokens Input · Cache write $0.00625 / 1K tokens Input · Cache write, 1 hour $0.01 / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${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-opus-4.7: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-opus-4.7:batch', 'anthropic/claude-opus-4.7']: raise ValueError("body.model must be an exact documented caller or wire ID")\n return {"endpoint":"/v1/chat/completions", "model":"anthropic/claude-opus-4.7: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-opus-4.7:batch
Use the method paths shown above.