Claude Opus 5 is an Anthropic model for regular professional work that requires studying information, making a reasoned choice and carrying out a task in sequence. The manufacturer highlights software engineering, multi-step business workflows, scientific work and visual data analysis. Its presentation centers on thoughtful execution and verification of its own results.
In development, Opus 5 can investigate the root cause of an error and use verification to assess a proposed change. If existing material is insufficient for comparison, described workflows include creating its own check, for example for parser behavior. Work is based on the data received: the model reads it, chooses an action, observes the result and corrects the problems found.
The application needs appropriate tools to execute commands, edit files and run checks. The model's initiative concerns its choice and sequence of actions in the provided environment; it does not mean independent access to a project or the ability to perform operations outside the application.
Opus 5 accepts text and images and produces text. In visual scenarios, the manufacturer demonstrates extracting geometry from an image and reconstructing a part through written code. Other examples include an interactive aerodynamic simulation and a cell illustration created through programming.
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.
These tasks demonstrate analysis and engineering working together: an image can be used as material, and the text result as code or a description of a solution. Running a simulation, drawing graphics and producing a finished file require environment tools; the model does not have a separate built-in image or video output.
Adaptive reasoning is enabled by default, with high as the default effort level. low, medium, high, xhigh and max are supported. Reasoning can be disabled at high and below, but is required at xhigh and max. Manual extended thinking with a separate budget is not provided for this version.
Changing effort affects the amount of reasoning but is not a reliable way to set the length of visible text. If a brief answer or a particular structure is needed, this should be specified in the instruction itself. Context is 1 million tokens, and the maximum response in ordinary mode is 128 thousand tokens; deep analysis needs room for both reasoning and final content.
The reliable built-in knowledge cutoff is May 2026. Among scientific uses, Anthropic describes structural biology, organic chemistry and bioinformatics. The manufacturer also notes substantial limitations in extended autonomous biological research; the ability to discuss scientific material does not mean that any research project can be completed autonomously.
Safety mechanisms allow bug finding in source code but restrict some sensitive tasks. Current information requires supplied material or search, and technical and scientific results need verification in their subject-specific environment.
", "aboutText": "Claude Opus 5 is an Anthropic model for regular professional work that requires studying information, making a reasoned choice and carrying out a task in sequence. The manufacturer highlights software engineering, multi-step business workflows, scientific work and visual data analysis. Its presentation centers on thoughtful execution and verification of its own results.\n\n### Finding the cause, checking and correcting\n\nIn development, Opus 5 can investigate the root cause of an error and use verification to assess a proposed change. If existing material is insufficient for comparison, described workflows include creating its own check, for example for parser behavior. Work is based on the data received: the model reads it, chooses an action, observes the result and corrects the problems found.\n\nThe application needs appropriate tools to execute commands, edit files and run checks. The model's initiative concerns its choice and sequence of actions in the provided environment; it does not mean independent access to a project or the ability to perform operations outside the application.\n\n### An image as data for a program\n\nOpus 5 accepts **text and images** and produces **text**. In visual scenarios, the manufacturer demonstrates extracting geometry from an image and reconstructing a part through written code. Other examples include an interactive aerodynamic simulation and a cell illustration created through programming.\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\nThese tasks demonstrate analysis and engineering working together: an image can be used as material, and the text result as code or a description of a solution. Running a simulation, drawing graphics and producing a finished file require environment tools; the model does not have a separate built-in image or video output.\n\n### Controllable depth\n\nAdaptive reasoning is enabled by default, with `high` as the default `effort` level. `low`, `medium`, `high`, `xhigh` and `max` are supported. Reasoning can be disabled at `high` and below, but is required at `xhigh` and `max`. Manual extended thinking with a separate budget is not provided for this version.\n\nChanging effort affects the amount of reasoning but is not a reliable way to set the length of visible text. If a brief answer or a particular structure is needed, this should be specified in the instruction itself. Context is **1 million tokens**, and the maximum response in ordinary mode is **128 thousand tokens**; deep analysis needs room for both reasoning and final content.\n\n### Scientific and sensitive tasks\n\nThe reliable built-in knowledge cutoff is **May 2026**. Among scientific uses, Anthropic describes structural biology, organic chemistry and bioinformatics. The manufacturer also notes substantial limitations in extended autonomous biological research; the ability to discuss scientific material does not mean that any research project can be completed autonomously.\n\nSafety mechanisms allow bug finding in source code but restrict some sensitive tasks. Current information requires supplied material or search, and technical and scientific results need verification in their subject-specific environment.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "d1b866b177d3b66f73eb952a74b3ee995f5c70bd525af3d174d3d94bc2e1cced", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "en", "methods": [ { "html": "claude-opus-5-thinkingClaude Opus 5 is designed for thoughtful development, information analysis and multi-step professional work. Supports adaptive reasoning and result verification using application tools.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Claude Opus 5 is an Anthropic model for regular professional work that requires studying information, making a reasoned choice and carrying out a task in sequence. The manufacturer highlights software engineering, multi-step business workflows, scientific work and visual data analysis. Its presentation centers on thoughtful execution and verification of its own results.
In development, Opus 5 can investigate the root cause of an error and use verification to assess a proposed change. If existing material is insufficient for comparison, described workflows include creating its own check, for example for parser behavior. Work is based on the data received: the model reads it, chooses an action, observes the result and corrects the problems found.
The application needs appropriate tools to execute commands, edit files and run checks. The model's initiative concerns its choice and sequence of actions in the provided environment; it does not mean independent access to a project or the ability to perform operations outside the application.
Opus 5 accepts text and images and produces text. In visual scenarios, the manufacturer demonstrates extracting geometry from an image and reconstructing a part through written code. Other examples include an interactive aerodynamic simulation and a cell illustration created through programming.
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.
These tasks demonstrate analysis and engineering working together: an image can be used as material, and the text result as code or a description of a solution. Running a simulation, drawing graphics and producing a finished file require environment tools; the model does not have a separate built-in image or video output.
Adaptive reasoning is enabled by default, with high as the default effort level. low, medium, high, xhigh and max are supported. Reasoning can be disabled at high and below, but is required at xhigh and max. Manual extended thinking with a separate budget is not provided for this version.
Changing effort affects the amount of reasoning but is not a reliable way to set the length of visible text. If a brief answer or a particular structure is needed, this should be specified in the instruction itself. Context is 1 million tokens, and the maximum response in ordinary mode is 128 thousand tokens; deep analysis needs room for both reasoning and final content.
The reliable built-in knowledge cutoff is May 2026. Among scientific uses, Anthropic describes structural biology, organic chemistry and bioinformatics. The manufacturer also notes substantial limitations in extended autonomous biological research; the ability to discuss scientific material does not mean that any research project can be completed autonomously.
Safety mechanisms allow bug finding in source code but restrict some sensitive tasks. Current information requires supplied material or search, and technical and scientific results need verification in their subject-specific environment.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits claude-opus-5-thinking | Default— |
Fieldmessages | Typemessage[] | RequirementRequired | Values & limits Minimum items: 1 | Default— |
Fieldmessages[].role | Typestring | RequirementRequired | Values & limits Model-specific | Default— |
Fieldmessages[].content | Typestring | RequirementOptional | Values & limits Applies when: Text conversation | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
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 |
Parametercontrol_scope | ContractText conversation; stream is a boolean and defaults to false. |
Parameteroutput_modalities | Contracttext |
Parameterhosted_operations | Contractfalse |
Parametermultimodal_input_binding | Contractnull |
Method aliases: /chat/completions
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 $0.01 / 1K tokens Output · Output tokens $0.05 / 1K tokens Input · Cache read $0.001 / 1K tokens Input · Cache write $0.0125 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model":"claude-opus-5-thinking","messages":[{"role":"user","content":"hello"}],"stream":false}')\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-opus-5-thinking", messages=[{"role":"user","content":"hello"}], stream=False)\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"))\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-opus-5-thinking
/chat/completions