Anthropic: Claude Opus 5

Claude Opus 5 is designed for thoughtful development, information analysis and multi-step professional work. Supports adaptive reasoning and result verification using application tools.

Ailoot Ultra · OpenAI Chat POST /v1/chat/completions claude-opus-5-thinking

About the model

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.

Finding the cause, checking and correcting

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.

An image as data for a program

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.

Controllable depth

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.

Scientific and sensitive tasks

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.

Ailoot Ultra · OpenAI Chat POST /v1/chat/completions claude-opus-5-thinking

claude-opus-5-thinking

Anthropic /claude-opus-5-thinking

Overview

Claude Opus 5 is designed for thoughtful development, information analysis and multi-step professional work. Supports adaptive reasoning and result verification using application tools.

Accepted inputs: Text
Results: Text

Available in Ailoot · inputs & methods

Ailoot Ultra · OpenAI Chat POST /v1/chat/completions

Input types: text
Output types: text
Output format: json or SSE

Field	Type	Requirement	Values & limits	Default
Field`model`	Type`string`	RequirementRequired	Values & limits claude-opus-5-thinking	Default—
Field`messages`	Type`message[]`	RequirementRequired	Values & limits Minimum items: 1	Default—
Field`messages[].role`	Type`string`	RequirementRequired	Values & limits Model-specific	Default—
Field`messages[].content`	Type`string`	RequirementOptional	Values & limits Applies when: Text conversation	Default—
Field`stream`	Type`boolean`	RequirementOptional	Values & limits	Defaultfalse

Input material combinations

Text · UTF8 text

messages[].content

Method parameters and lifecycle

Parameter	Contract
Parameter`content_type`	Contractapplication/json
Parameter`required`	Contractmodel, messages
Parameter`material_types`	Contracttext
Parameter`messages`	Contractmessage array with role/content shape
Parameter`file_material`	Contractactual UTF-8 document inserted as literal text; no file ID inferred
Parameter`optional_controls`	Contractstream
Parameter`control_scope`	ContractText conversation; stream is a boolean and defaults to false.
Parameter`output_modalities`	Contracttext
Parameter`hosted_operations`	Contractfalse
Parameter`multimodal_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.

Pricing

Tariff references do not add request options. Supported parameters are listed for the selected method.

Fast mode: Not specified When fast mode is selected
Flex mode: Not specified When flex mode is selected
Maximum reasoning effort: Not specified When maximum reasoning effort uses the default adjustment

Ailoot rates

Prices by configuration

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 →

Example requests

Ailoot Ultra · OpenAI Chat

curlPython SDK

set -eu
: "${AILOOT_API_KEY:?Set your Ailoot API key}"
python3 - <<'PY_REQUEST'
import json,os
from pathlib import Path
p=json.loads('{"model":"claude-opus-5-thinking","messages":[{"role":"user","content":"hello"}],"stream":false}')

Path("request.json").write_text(json.dumps(p,ensure_ascii=False))
PY_REQUEST
curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/chat/completions \
  -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \
  --data-binary @request.json -D response-headers.txt -o result.json
python3 - <<'PY_RESULT'
import json
from pathlib import Path
r=json.loads(Path("result.json").read_text())
for x in r.get("choices",[]):
    if x.get("message",{}).get("content"): print(x["message"]["content"])
PY_RESULT

from openai import OpenAI
import os

if not os.environ.get("AILOOT_API_KEY", "").strip(): raise SystemExit("Set AILOOT_API_KEY before making a request")
client=OpenAI(base_url="https://api.ailoot.ai/v1",api_key=os.environ["AILOOT_API_KEY"],max_retries=0)
result=client.chat.completions.create(model="claude-opus-5-thinking", messages=[{"role":"user","content":"hello"}], stream=False)
import base64, json
from pathlib import Path

def save_result(data, stem="result"):
    # Keep every field, including usage, IDs, reasoning and tool requests.
    Path(stem + ".json").write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8")
    print(json.dumps(data, ensure_ascii=False, indent=2))
    def save_blob(encoded, suffix, label):
        raw = base64.b64decode(encoded, altchars=b"-_", validate=True)
        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
        file = Path(stem + "-" + label + "." + ext)
        file.write_bytes(raw)
        print("Saved", file)
    for index, block in enumerate(data.get("content") or []):
        print("Content", index, json.dumps(block, ensure_ascii=False))
    for choice in data.get("choices") or []:
        print("Choice", choice.get("index"), json.dumps(choice.get("message", choice.get("delta", {})), ensure_ascii=False))
    for index, item in enumerate(data.get("output") or []):
        print("Output", index, json.dumps(item, ensure_ascii=False))
        if item.get("type") == "image_generation_call" and item.get("result"):
            save_blob(item["result"], item.get("output_format") or "bin", "output-" + str(index))
    for ci, candidate in enumerate(data.get("candidates") or []):
        for pi, part in enumerate((candidate.get("content") or {}).get("parts") or []):
            print("Candidate/part", ci, pi, json.dumps(part, ensure_ascii=False))
            blob = part.get("inlineData") or part.get("inline_data")
            if blob and blob.get("data"):
                mime = blob.get("mimeType") or blob.get("mime_type")
                suffix = {"image/png":"png", "image/jpeg":"jpg", "image/webp":"webp", "audio/wav":"wav", "audio/mpeg":"mp3", "video/mp4":"mp4"}.get(mime, "bin")
                save_blob(blob["data"], suffix, "candidate-" + str(ci) + "-part-" + str(pi))

save_result(result.to_dict(mode="json"))

Receive and save results

Ailoot Ultra · OpenAI Chat

json or SSE

Keep all completed results

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.

If the original response needs checking

Keep request/response IDs and headers. No generic text job retrieval URL is supplied.

Response fields

Field	Type
Field`choices[].index`	Typeinteger
Field`choices[].message.content`	Typestring\|null
Field`choices[].message.tool_calls`	Typetool_call[]
Field`choices[].message.reasoning_content`	Typestring
Field`choices[].message.reasoning_details`	Typeobject\|array
Field`choices[].finish_reason`	Typestring\|null
Field`usage`	Typeobject

Errors and next steps

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.

Aliases

Model aliases

Ailoot Ultra claude-opus-5-thinking

Method path aliases

/chat/completions

Related caller IDs

claude-opus-5-thinking

FAQ · Anthropic: Claude Opus 5

What inputs and results are available?: POST /v1/chat/completions · claude-opus-5-thinking: text → text
Which fields does the request use?: POST /v1/chat/completions · claude-opus-5-thinking: model, messages, stream
What limits apply?: POST /v1/chat/completions · claude-opus-5-thinking: messages.min_items: 1

Anthropic: Claude Opus 5