models

OpenAI: GPT-6 Astra Pro

Markdown · JSON

OpenAI: GPT-6 Astra Pro

GPT-6 Astra with Pro reasoning for demanding research, coding and professional work. Pro adds model work before the final answer; the base model accepts text and images and supports multi-step work in a tool-connected environment.

About the model

This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.

GPT-6 Astra Pro denotes OpenAI’s GPT-6 Astra with Pro reasoning. The base Astra model is designed for demanding reasoning, coding, research, computer use and professional work. It accepts text and images and produces text. Pro adds model work when developing the final answer, while reasoning effort controls the amount of analysis within the chosen execution mode. Pro changes the execution mode while GPT-6 Astra remains the underlying model.

The manufacturer’s Astra release describes workflows that turn relevant material into documents, spreadsheets, presentations and analysis. It emphasizes following templates and writing or visual style while selecting the context that matters to the task. For a professional document, provide the source material, the template requirements and the decision the reader needs to make. An illustrative request is to reconcile two sets of project requirements and produce a brief that separates agreed facts, conflicting evidence and the conditions for a recommendation.

For engineering or research work, specify the objective, the evidence to inspect and what would count as completion. A coding task can include the required behavior, related files and constraints; a research task can include the question and the criteria for weighing the supplied material. Astra’s documented computer-use and browsing capabilities connect the model’s decisions with an environment that supplies the corresponding tools. The environment’s actual outputs are evidence for the next step, distinct from the model’s proposed actions.

OpenAI’s GPT-6 Astra specification lists a 1,050,000-token context window, a maximum input of 922,000 tokens and a maximum output of 128,000 tokens, with a knowledge cutoff of April 30, 2026. Its reasoning-effort choices are low, medium, high, xhigh and max; none is unsupported. Reasoning tokens and the visible result both occupy context space. OpenAI documents Pro in the Responses API for difficult work that can accommodate additional latency and model activity; mode and effort remain independent settings.

The base model lists streaming, Structured Outputs, function calling, web and file search, image-generation tooling, code interpretation, hosted shell and patch operations, skills, computer use, MCP connections and tool search. Function calling uses the producer’s Responses interface. The GPT-6 guide also describes asynchronous tool work and steering an active turn. Applications execute their own operations, manage pending work and return the resulting information; the model’s own output remains text even when a separate tool creates another artifact.

The Astra release describes adapting to new requirements while keeping the broader goal in view. It also distinguishes defensive code review and patching from advanced exploit-development work, which the model may refuse, and describes automatic checks that can interrupt a task. Prepare a request around the intended result, permitted work and evidence, rather than treating extra reasoning as a substitute for missing source material. A useful final artifact states the conclusion or proposed change, its basis in the supplied information and any question that would materially alter the decision.

Practical task

Reconcile conflicting launch requirements

Technical example in English

Reconcile conflicting launch requirements

Write a decision brief using this template: Decision needed; Agreed facts; Conflict; Recommendation conditions. Product requirement P1 says launch must include CSV export. Security requirement S1 prohibits exporting customer email addresses. Current design D1 exports name and email only. Reconcile the requirements, propose a compliant export scope and list the decision still needed about useful fields.

Expected result

A brief under the four supplied headings, with P1, S1 and D1 references beside relevant findings.

Check the result

Check that the proposed scope preserves an export feature while excluding emails and does not invent an approved field list.
Ailoot Ultra · OpenAI Chat POST /v1/chat/completions openai/gpt-6-astra-pro

openai/gpt-6-astra-pro

OpenAI /openai/gpt-6-astra-pro

Overview

GPT-6 Astra with Pro reasoning for demanding research, coding and professional work. Pro adds model work before the final answer; the base model accepts text and images and supports multi-step work in a tool-connected environment.

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
FieldTypeRequirementValues & limitsDefault
FieldmodelTypestringRequirementRequiredValues & limits
openai/gpt-6-astra-pro
Default—
FieldmessagesTypemessage[]RequirementRequiredValues & limits
Minimum items: 1
Default—
Fieldmessages[].roleTypestringRequirementRequiredValues & 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[].contentTypestring|content_part[]|nullRequirementOptionalValues & limitsDefault—
Fieldmessages[].tool_callsTypetool_call[]RequirementOptionalValues & limitsDefault—
Fieldmessages[].tool_call_idTypestringRequirementOptionalValues & limits
Applies when: role=tool; use actual returned ID
Default—
Fieldmessages[].nameTypestringRequirementOptionalValues & limitsDefault—
Fieldmessages[].reasoning_contentTypestringRequirementOptionalValues & limits
Applies when: Round-trip actual returned reasoning where this model uses it
Default—
Fieldmessages[].reasoning_detailsTypeobject|arrayRequirementOptionalValues & limits
Model-specific
Default—
Fieldmax_tokensTypeintegerRequirementOptionalValues & limits
Minimum: 1
Model-specific
Default—
Fieldmax_completion_tokensTypeintegerRequirementOptionalValues & limits
Minimum: 1
Model-specific
Default—
FieldstreamTypebooleanRequirementOptionalValues & limitsDefaultfalse
Fieldstream_options.include_usageTypebooleanRequirementOptionalValues & limits
Model-specific
Default—
FieldtemperatureTypenumberRequirementOptionalValues & limits
Model-specific
Default—
Fieldtop_pTypenumberRequirementOptionalValues & limits
Model-specific
Default—
FieldstopTypestring|string[]RequirementOptionalValues & limits
Model-specific
Default—
FieldtoolsTypefunction_tool[]RequirementOptionalValues & limits
Model-specific
Default—
Fieldtools[].typeTypestringRequirementOptionalValues & limits
function
Model-specific
Default—
Fieldtools[].function.nameTypestringRequirementRequiredValues & limits
Applies when: function tool supplied
Default—
Fieldtools[].function.descriptionTypestringRequirementOptionalValues & limitsDefault—
Fieldtools[].function.parametersTypeJSON_schemaRequirementOptionalValues & limitsDefault—
Fieldtool_choiceTypestring|objectRequirementOptionalValues & limits
Model-specific
Default—
Fieldparallel_tool_callsTypebooleanRequirementOptionalValues & limits
Model-specific
Default—
Fieldresponse_formatTypeobjectRequirementOptionalValues & limits
Model-specific
Default—
Fieldreasoning_effortTypestringRequirementOptionalValues & limits
Model-specific
Default—
FieldreasoningTypeobjectRequirementOptionalValues & limits
Model-specific
Default—
Fieldservice_tierTypestringRequirementOptionalValues & limits
Model-specific
Default—
FieldinstructionsTypestringRequirementOptionalValues & limits
Compatibility field
Default—
FieldfunctionsTypefunction[]RequirementOptionalValues & limits
Model-specific
Default—
Fieldfunction_callTypestring|objectRequirementOptionalValues & limits
Model-specific
Default—
Fieldprompt_cache_optionsTypeobjectRequirementOptionalValues & limits
Model-specific
Default—
FieldmodalitiesTypestring[]RequirementOptionalValues & limits
text
Default—
FieldproviderTypeobjectRequirementOptionalValues & limits
Conditions: provider.options is rejected on this Ultra text route
Default—
Fieldcache_control.ttlTypestringRequirementOptionalValues & limits
Conditions: 1h is rejected in actual prompt-cache locations
Default—
Input material combinations

Text · UTF8 text

messages[].content

Method parameters and lifecycle
ParameterContract
Parametercontent_typeContractapplication/json
ParameterrequiredContractmodel, messages
Parametermaterial_typesContracttext
ParametermessagesContractmessage array with role/content shape
Parameterfile_materialContractactual UTF-8 document inserted as literal text; no file ID inferred
Parameteroptional_controlsContractstream, temperature, top_p, stop, max_tokens, max_completion_tokens, reasoning, response_format, tools, tool_choice
Parametercontrol_scopeContractmodel-specific; user function tools differ from hosted tools
Parameteroutput_modalitiesContracttext
Parameterhosted_operationsContractfalse
Parametermultimodal_input_bindingContractnull

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
Standard rate When fast mode is selected
Flex mode
Standard rate When flex mode is selected
Maximum reasoning effort
Standard rate When maximum reasoning effort uses the default adjustment
Reasoning effort · high
Standard rate When reasoning effort is high
Reasoning effort · low
Standard rate When reasoning effort is low
Reasoning effort · max
Standard rate When reasoning effort is max
Reasoning effort · medium
Standard rate When reasoning effort is medium
Reasoning effort · xhigh
Standard rate When reasoning effort is xhigh

Ailoot rates

Prices by configuration

Match each charge to the conditions shown. Use one rate for each set of alternative configurations.

ConfigurationRate scheduleContext tokens, more thanContext tokens, up to and includingCharge
ConfigurationBase token ratesRate scheduleBase rateContext tokens, more than—Context tokens, up to and includingNo upper boundCharge
Input · Input tokens $0.02 / 1K tokens
Output · Output tokens $0.1 / 1K tokens
Input · Cache read $0.002 / 1K tokens
Input · Cache write $0.025 / 1K tokens
Input · Cache write, 1 hour — / 1K tokens
ConfigurationRequest-length tier 1Rate schedule—Context tokens, more than0Context tokens, up to and including272,000Charge
Input · Input tokens $0.02 / 1K tokens
Output · Output tokens $0.1 / 1K tokens
Input · Cache read $0.002 / 1K tokens
Input · Cache write $0.025 / 1K tokens
Input · Cache write, 1 hour — / 1K tokens
ConfigurationRequest-length tier 2Rate schedule—Context tokens, more than272,000Context tokens, up to and includingNo upper boundCharge
Input · Input tokens $0.04 / 1K tokens
Output · Output tokens $0.15 / 1K tokens
Input · Cache read $0.004 / 1K tokens
Input · Cache write $0.05 / 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 SDKPython stream
set -eu
: "${AILOOT_API_KEY:?Set your Ailoot API key}"
python3 - <<'PY_REQUEST'
import json,os
from pathlib import Path
p=json.loads('{"model": "openai/gpt-6-astra-pro", "messages": [{"role": "user", "content": "Explain the difference between an observation and a hypothesis in three sentences."}]}')

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='openai/gpt-6-astra-pro',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}])
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"))
from openai import OpenAI
import os
from pathlib import Path

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)
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))

with client.chat.completions.stream(model='openai/gpt-6-astra-pro',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}]) as stream:
    with Path("stream-events.jsonl").open("w",encoding="utf-8") as log:
        for event in stream:
            payload=event.to_dict(mode="json")
            log.write(json.dumps(payload,ensure_ascii=False)+"\n"); log.flush()
            print(json.dumps(payload,ensure_ascii=False))
    final=stream.get_final_completion()
    save_result(final.to_dict(mode="json"),"stream-result")

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
FieldType
Fieldchoices[].indexTypeinteger
Fieldchoices[].message.contentTypestring|null
Fieldchoices[].message.tool_callsTypetool_call[]
Fieldchoices[].message.reasoning_contentTypestring
Fieldchoices[].message.reasoning_detailsTypeobject|array
Fieldchoices[].finish_reasonTypestring|null
FieldusageTypeobject
Errors and next steps
ResponseConditionNext step
Response400 · invalid_request_errorConditionInvalid required fields, material representation or parameter valueNext stepCorrect the indicated fields before a new creation request.

Aliases

Model aliases

Ailoot Ultra openai/gpt-6-astra-pro

Method path aliases

Use the method paths shown above.

Related caller IDs

openai/gpt-6-astra-pro

Ailoot Ultra · Anthropic Messages POST /v1/messages openai/gpt-6-astra-pro

openai/gpt-6-astra-pro

OpenAI /openai/gpt-6-astra-pro

Overview

GPT-6 Astra with Pro reasoning for demanding research, coding and professional work. Pro adds model work before the final answer; the base model accepts text and images and supports multi-step work in a tool-connected environment.

Accepted inputs
Text
Results
Text

Available in Ailoot · inputs & methods

Ailoot Ultra · Anthropic Messages POST /v1/messages
Input types
text
Output types
text
Output format
json or SSE
FieldTypeRequirementValues & limitsDefault
FieldmodelTypestringRequirementRequiredValues & limits
openai/gpt-6-astra-pro
Default—
FieldmessagesTypemessage[]RequirementRequiredValues & limits
Minimum items: 1
Default—
Fieldmessages[].roleTypestringRequirementRequiredValues & limits
user, assistant
Default—
Fieldmessages[].contentTypestring|content_block[]RequirementRequiredValues & limitsDefault—
FieldsystemTypestring|content_block[]RequirementOptionalValues & limitsDefault—
Fieldmax_tokensTypeintegerRequirementRequiredValues & limits
Minimum: 1
Limit applies to: exact selected model/card ceiling
Default—
FieldstreamTypebooleanRequirementOptionalValues & limitsDefaultfalse
FieldtemperatureTypenumberRequirementOptionalValues & limits
Model-specific
Default—
Fieldtop_pTypenumberRequirementOptionalValues & limits
Model-specific
Default—
Fieldstop_sequencesTypestring[]RequirementOptionalValues & limits
Model-specific
Default—
FieldtoolsTypetool[]RequirementOptionalValues & limits
Model-specific
Default—
Fieldtools[].nameTypestringRequirementOptionalValues & limits
Applies when: tool supplied
Default—
Fieldtools[].descriptionTypestringRequirementOptionalValues & limitsDefault—
Fieldtools[].input_schemaTypeJSON_schemaRequirementOptionalValues & limitsDefault—
Fieldtool_choiceTypeobjectRequirementOptionalValues & limits
Model-specific
Default—
Fieldthinking.typeTypestringRequirementOptionalValues & limits
Model-specific
Default—
Fieldthinking.budget_tokensTypeintegerRequirementOptionalValues & limits
Model-specific
Default—
Fieldoutput_config.effortTypestringRequirementOptionalValues & limits
Model-specific
Default—
FieldmetadataTypeobjectRequirementOptionalValues & limitsDefault—
Fieldcache_controlTypeobjectRequirementOptionalValues & limits
Model-specific
Default—
Input material combinations

Text · UTF8 text

messages[].content or type=text block

Text + image

{type:image,source:{type:base64,media_type,data}} or source type=url

Method parameters and lifecycle
ParameterContract
Parametermaterial_typesContracttext
ParameterrequiredContractmodel, messages, max_tokens
Parameteroptional_controlsContractnull
Parametercontent_typeContractapplication/json
Parametertext_file_materialContractRead the actual UTF-8 document and put its text in the indicated input or message content string.

Lifecycle: exact retained published method

Send the exact model ID and required fields.

Keep every returned item and its original identifiers.

Pricing

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

Fast mode
Standard rate When fast mode is selected
Flex mode
Standard rate When flex mode is selected
Maximum reasoning effort
Standard rate When maximum reasoning effort uses the default adjustment
Reasoning effort · high
Standard rate When reasoning effort is high
Reasoning effort · low
Standard rate When reasoning effort is low
Reasoning effort · max
Standard rate When reasoning effort is max
Reasoning effort · medium
Standard rate When reasoning effort is medium
Reasoning effort · xhigh
Standard rate When reasoning effort is xhigh

Ailoot rates

Prices by configuration

Match each charge to the conditions shown. Use one rate for each set of alternative configurations.

ConfigurationRate scheduleContext tokens, more thanContext tokens, up to and includingCharge
ConfigurationBase token ratesRate scheduleBase rateContext tokens, more than—Context tokens, up to and includingNo upper boundCharge
Input · Input tokens $0.02 / 1K tokens
Output · Output tokens $0.1 / 1K tokens
Input · Cache read $0.002 / 1K tokens
Input · Cache write $0.025 / 1K tokens
Input · Cache write, 1 hour — / 1K tokens
ConfigurationRequest-length tier 1Rate schedule—Context tokens, more than0Context tokens, up to and including272,000Charge
Input · Input tokens $0.02 / 1K tokens
Output · Output tokens $0.1 / 1K tokens
Input · Cache read $0.002 / 1K tokens
Input · Cache write $0.025 / 1K tokens
Input · Cache write, 1 hour — / 1K tokens
ConfigurationRequest-length tier 2Rate schedule—Context tokens, more than272,000Context tokens, up to and includingNo upper boundCharge
Input · Input tokens $0.04 / 1K tokens
Output · Output tokens $0.15 / 1K tokens
Input · Cache read $0.004 / 1K tokens
Input · Cache write $0.05 / 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 · Anthropic Messages

Complete minimal JSON bodycurl · use your UTF-8 documentPython · save the complete result
{
  "model": "openai/gpt-6-astra-pro",
  "max_tokens": 4096,
  "messages": [
    {
      "role": "user",
      "content": "Summarize the incident report."
    }
  ]
}
set -eu
: "${AILOOT_API_KEY:?Set your Ailoot API key}"
: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}"
export AILOOT_TEXT_FILE
python3 - <<'PY_REQUEST'
import json, os
from pathlib import Path

source_file = Path(os.environ['AILOOT_TEXT_FILE'])
if not source_file.is_file():
    raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')
text = source_file.read_text(encoding='utf-8')
if not text.strip():
    raise SystemExit('Choose a document containing text')
payload = {'model': 'openai/gpt-6-astra-pro', 'max_tokens': 4096, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}
payload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\n\n' + text
Path('request.json').write_text(json.dumps(payload, ensure_ascii=False), encoding='utf-8')
PY_REQUEST
curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/messages \
  -H "x-api-key: $AILOOT_API_KEY" -H 'anthropic-version: 2023-06-01' -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

result = json.loads(Path('result.json').read_text(encoding='utf-8'))
print(json.dumps(result, ensure_ascii=False, indent=2))
PY_RESULT
import json, os
from pathlib import Path

source_file = Path(os.environ['AILOOT_TEXT_FILE'])
if not source_file.is_file():
    raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')
text = source_file.read_text(encoding='utf-8')
if not text.strip():
    raise SystemExit('Choose a document containing text')
payload = {'model': 'openai/gpt-6-astra-pro', 'max_tokens': 4096, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}
payload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\n\n' + text
from urllib.request import Request, urlopen
from urllib.error import HTTPError

api_key = os.environ['AILOOT_API_KEY']
if not api_key.strip():
    raise SystemExit('Set AILOOT_API_KEY before making a request')
headers = {'x-api-key': 'PLACEHOLDER_KEY', 'anthropic-version': '2023-06-01', 'Content-Type': 'application/json'}
headers['x-api-key'] = api_key
request = Request('https://api.ailoot.ai/v1/messages', data=json.dumps(payload, ensure_ascii=False).encode('utf-8'), headers=headers, method='POST')
try:
    with urlopen(request, timeout=120) as response:
        raw = response.read()
        Path('response-headers.txt').write_text(str(response.headers), encoding='utf-8')
except HTTPError as error:
    Path('result.json').write_bytes(error.read())
    Path('response-headers.txt').write_text(str(error.headers), encoding='utf-8')
    raise
Path('result.json').write_bytes(raw)
import json
from pathlib import Path

result = json.loads(Path('result.json').read_text(encoding='utf-8'))
print(json.dumps(result, ensure_ascii=False, indent=2))

Receive and save results

Ailoot Ultra · Anthropic Messages

json or SSE

Keep all completed results

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.

If the original response needs checking

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

Response fields
FieldType
FieldidTypestring
FieldmodelTypestring
Fieldcontent[]Typetext|thinking|tool_use|other content block
Fieldstop_reasonTypestring|null
FieldusageTypeobject
Errors and next steps
ResponseConditionNext step
Response400 · invalid_request_errorConditionInvalid required fields, material representation or parameter valueNext stepCorrect the indicated fields before a new creation request.

Aliases

Model aliases

Ailoot Ultra openai/gpt-6-astra-pro

Method path aliases

Use the method paths shown above.

Related caller IDs

openai/gpt-6-astra-pro

Ailoot Ultra · OpenAI Responses POST /v1/responses openai/gpt-6-astra-pro

openai/gpt-6-astra-pro

OpenAI /openai/gpt-6-astra-pro

Overview

GPT-6 Astra with Pro reasoning for demanding research, coding and professional work. Pro adds model work before the final answer; the base model accepts text and images and supports multi-step work in a tool-connected environment.

Accepted inputs
Text
Results
Text

Available in Ailoot · inputs & methods

Ailoot Ultra · OpenAI Responses POST /v1/responses
Input types
text
Output types
text
Output format
json or SSE
FieldTypeRequirementValues & limitsDefault
FieldmodelTypestringRequirementRequiredValues & limits
openai/gpt-6-astra-pro
Default—
FieldinputTypestring|input_item[]RequirementRequiredValues & limitsDefault—
FieldinstructionsTypestringRequirementOptionalValues & limitsDefault—
Fieldinput[].typeTypestringRequirementOptionalValues & limits
message, reasoning, function_call, function_call_output
Default—
Fieldinput[].contentTypestring|content_part[]RequirementOptionalValues & limitsDefault—
Fieldinput[].call_idTypestringRequirementOptionalValues & limits
Applies when: function_call_output; retain actual returned call_id
Default—
Fieldinput[].outputTypestring|content_part[]RequirementOptionalValues & limits
Applies when: function_call_output
Default—
Fieldmax_output_tokensTypeintegerRequirementOptionalValues & limits
Minimum: 1
Model-specific
Default—
FieldstreamTypebooleanRequirementOptionalValues & limitsDefaultfalse
FieldtemperatureTypenumberRequirementOptionalValues & limits
Model-specific
Default—
Fieldtop_pTypenumberRequirementOptionalValues & limits
Model-specific
Default—
FieldtoolsTypetool[]RequirementOptionalValues & limits
Model-specific
Default—
Fieldtool_choiceTypestring|objectRequirementOptionalValues & limits
Model-specific
Default—
Fieldparallel_tool_callsTypebooleanRequirementOptionalValues & limits
Model-specific
Default—
Fieldreasoning.effortTypestringRequirementOptionalValues & limits
Model-specific
Default—
Fieldreasoning.summaryTypestringRequirementOptionalValues & limits
Model-specific
Default—
Fieldtext.formatTypeobjectRequirementOptionalValues & limits
Model-specific
Default—
Fieldtext.verbosityTypestringRequirementOptionalValues & limits
Model-specific
Default—
FieldincludeTypestring[]RequirementOptionalValues & limits
Model-specific
Default—
FieldstoreTypebooleanRequirementOptionalValues & limits
Model-specific
Default—
Fieldprevious_response_idTypestringRequirementNot supportedValues & limits
ID requirement: Only an actual response ID from a supported continuation
Conditions: Replay the actual conversation history with each request.
Default—
Fieldservice_tierTypestringRequirementOptionalValues & limits
Model-specific
Default—
Fieldprompt_cache_keyTypestringRequirementOptionalValues & limits
Model-specific
Default—
Fieldprompt_cache_optionsTypeobjectRequirementOptionalValues & limits
Model-specific
Default—
Input material combinations

Text · UTF8 text

input text or input_text part

Text + image

{type:input_image,image_url}

Method parameters and lifecycle
ParameterContract
Parametermaterial_typesContracttext
ParameterrequiredContractmodel, input
Parameteroptional_controlsContractnull
Parametercontent_typeContractapplication/json
Parametertext_file_materialContractRead the actual UTF-8 document and put its text in the indicated input or message content string.

Lifecycle: exact retained published method

Send the exact model ID and required fields.

Keep every returned item and its original identifiers.

Pricing

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

Fast mode
Standard rate When fast mode is selected
Flex mode
Standard rate When flex mode is selected
Maximum reasoning effort
Standard rate When maximum reasoning effort uses the default adjustment
Reasoning effort · high
Standard rate When reasoning effort is high
Reasoning effort · low
Standard rate When reasoning effort is low
Reasoning effort · max
Standard rate When reasoning effort is max
Reasoning effort · medium
Standard rate When reasoning effort is medium
Reasoning effort · xhigh
Standard rate When reasoning effort is xhigh

Ailoot rates

Prices by configuration

Match each charge to the conditions shown. Use one rate for each set of alternative configurations.

ConfigurationRate scheduleContext tokens, more thanContext tokens, up to and includingCharge
ConfigurationBase token ratesRate scheduleBase rateContext tokens, more than—Context tokens, up to and includingNo upper boundCharge
Input · Input tokens $0.02 / 1K tokens
Output · Output tokens $0.1 / 1K tokens
Input · Cache read $0.002 / 1K tokens
Input · Cache write $0.025 / 1K tokens
Input · Cache write, 1 hour — / 1K tokens
ConfigurationRequest-length tier 1Rate schedule—Context tokens, more than0Context tokens, up to and including272,000Charge
Input · Input tokens $0.02 / 1K tokens
Output · Output tokens $0.1 / 1K tokens
Input · Cache read $0.002 / 1K tokens
Input · Cache write $0.025 / 1K tokens
Input · Cache write, 1 hour — / 1K tokens
ConfigurationRequest-length tier 2Rate schedule—Context tokens, more than272,000Context tokens, up to and includingNo upper boundCharge
Input · Input tokens $0.04 / 1K tokens
Output · Output tokens $0.15 / 1K tokens
Input · Cache read $0.004 / 1K tokens
Input · Cache write $0.05 / 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 Responses

Complete minimal JSON bodycurl · use your UTF-8 documentPython · save the complete result
{
  "model": "openai/gpt-6-astra-pro",
  "input": [
    {
      "content": "Hello, how are you?",
      "role": "user",
      "type": "message"
    }
  ]
}
set -eu
: "${AILOOT_API_KEY:?Set your Ailoot API key}"
: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}"
export AILOOT_TEXT_FILE
python3 - <<'PY_REQUEST'
import json, os
from pathlib import Path

source_file = Path(os.environ['AILOOT_TEXT_FILE'])
if not source_file.is_file():
    raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')
text = source_file.read_text(encoding='utf-8')
if not text.strip():
    raise SystemExit('Choose a document containing text')
payload = {'model': 'openai/gpt-6-astra-pro', 'input': [{'content': 'Hello, how are you?', 'role': 'user', 'type': 'message'}]}
payload['input'][0]['content'] = 'Summarize this material and quote supporting source passages.\n\n' + text
Path('request.json').write_text(json.dumps(payload, ensure_ascii=False), encoding='utf-8')
PY_REQUEST
curl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/responses \
  -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

result = json.loads(Path('result.json').read_text(encoding='utf-8'))
print(json.dumps(result, ensure_ascii=False, indent=2))
PY_RESULT
import json, os
from pathlib import Path

source_file = Path(os.environ['AILOOT_TEXT_FILE'])
if not source_file.is_file():
    raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')
text = source_file.read_text(encoding='utf-8')
if not text.strip():
    raise SystemExit('Choose a document containing text')
payload = {'model': 'openai/gpt-6-astra-pro', 'input': [{'content': 'Hello, how are you?', 'role': 'user', 'type': 'message'}]}
payload['input'][0]['content'] = 'Summarize this material and quote supporting source passages.\n\n' + text
from urllib.request import Request, urlopen
from urllib.error import HTTPError

api_key = os.environ['AILOOT_API_KEY']
if not api_key.strip():
    raise SystemExit('Set AILOOT_API_KEY before making a request')
headers = {'Authorization': 'Bearer PLACEHOLDER_KEY', 'Content-Type': 'application/json'}
headers['Authorization'] = 'Bearer ' + api_key
request = Request('https://api.ailoot.ai/v1/responses', data=json.dumps(payload, ensure_ascii=False).encode('utf-8'), headers=headers, method='POST')
try:
    with urlopen(request, timeout=120) as response:
        raw = response.read()
        Path('response-headers.txt').write_text(str(response.headers), encoding='utf-8')
except HTTPError as error:
    Path('result.json').write_bytes(error.read())
    Path('response-headers.txt').write_text(str(error.headers), encoding='utf-8')
    raise
Path('result.json').write_bytes(raw)
import json
from pathlib import Path

result = json.loads(Path('result.json').read_text(encoding='utf-8'))
print(json.dumps(result, ensure_ascii=False, indent=2))

Receive and save results

Ailoot Ultra · OpenAI Responses

json or SSE

Keep all completed results

Keep every complete returned response, file and content part.

Keep every output item and content part, actual function call_id, encrypted reasoning and usage. In streaming retain complete event/item identity.

If the original response needs checking

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

Response fields
FieldType
FieldidTypestring
FieldstatusTypestring
Fieldoutput[]Typemessage|reasoning|function_call|other item
Fieldoutput[].content[]Typecontent part
FieldusageTypeobject
Errors and next steps
ResponseConditionNext step
Response400 · invalid_request_errorConditionInvalid required fields, material representation or parameter valueNext stepCorrect the indicated fields before a new creation request.

Aliases

Model aliases

Ailoot Ultra openai/gpt-6-astra-pro

Method path aliases

Use the method paths shown above.

Related caller IDs

openai/gpt-6-astra-pro

FAQ · OpenAI: GPT-6 Astra Pro
What inputs and results are available?

POST /v1/chat/completions · openai/gpt-6-astra-pro: text → text

POST /v1/messages · openai/gpt-6-astra-pro: text → text

POST /v1/responses · openai/gpt-6-astra-pro: text → text

Which fields does the request use?

POST /v1/chat/completions · openai/gpt-6-astra-pro: model, messages, stream, modalities, provider

POST /v1/messages · openai/gpt-6-astra-pro: model, messages, system, max_tokens, stream, metadata

POST /v1/responses · openai/gpt-6-astra-pro: model, input, instructions, stream

What limits apply?

POST /v1/chat/completions · openai/gpt-6-astra-pro: messages.min_items: 1

POST /v1/messages · openai/gpt-6-astra-pro: messages.min_items: 1; max_tokens.minimum: 1

More details

Connect Ailoot to Claude Code, OpenCode, Hermes or your own app.

The company and its project portfolio

The company and its project portfolio: millions of customers; monthly sales in the billions.

Buy tokens at a discount.

Subscription discounts for regular use. Promo codes offer up to 50% off token purchases.

Subscription
Choose a discounted plan for your regular requests.
Promo code: up to 50% off
Enter your code at checkout. The price updates to reflect the current promotion.
Spending in your history
The model, tokens and amount are recorded for each request.

Choose a subscription · View model prices

Connect the API to a product you sell.

Use Ailoot in SaaS, apps and bots under its official service offer and licence agreement. Request a reseller licence for resale.

Commercial application
Add text, image, video and audio generation to paid features in your service.
Reseller licence
Send a description of your project and how you provide access. Request written terms for use and resale.
Documents on the website
The service offer, licence agreement and data processing policy are available before you connect.

Request a reseller licence · Service offer and licence agreement