GPT-5-Codex is OpenAI’s version of GPT-5 optimized for agentic software engineering in Codex and comparable coding environments. Its training targets work such as creating projects, implementing features and tests, debugging, refactoring established codebases and reviewing changes. Reinforcement learning on real coding tasks develops instruction following, code style and iterative verification. The intended setting is a coding assistant with access to project context and development tools.
The model adjusts how much reasoning it devotes to a task. A small, clearly defined change can be handled as an interactive exchange, while a complex refactor can involve sustained analysis and repeated implementation steps. This adaptive behavior connects conversation with execution: the agent examines the project, proposes a change, receives results from its environment and continues working toward the stated outcome.
Code review is a distinct part of its specialization. OpenAI describes a reviewer that examines the codebase and dependencies, relates a proposed change to its intended behavior, and uses code execution and tests to investigate correctness. The useful output is a focused explanation of consequential defects and the affected code, rather than comments based only on the appearance of a diff. Project files and tool results provide the evidence for that analysis.
Visual input supports frontend work. Screenshots, wireframes and diagrams can convey the desired interface or an observed problem. In an environment with browser and screenshot tools, the agent can inspect the implementation and compare what it built with the supplied visual target. These are environment-assisted workflows: the model interprets information and selects actions, while the application provides the tools and executes them.
The published model specification lists text and image input and text output, a 400,000-token context window and up to 128,000 output tokens. It supports reasoning tokens, streaming, function calling and structured outputs. OpenAI documents the model through its Responses interface and identifies the unversioned name as an updating model label. Its documented knowledge cutoff is September 30, 2024. Audio and video are outside the listed input modalities; fine-tuning and predicted outputs are not supported in this configuration.
The system-card addendum separates model training from product controls. The model is trained to resist prompt-injection instructions encountered in coding material. Workspace isolation, network permissions and approval rules belong to the agent environment. This distinction matters when the assistant reads external documentation or executes commands: access is defined by the application, and its outputs provide the record of what actually happened.
For a coding assistant using Ailoot API, start with the behavior to achieve, the relevant files and a clear completion criterion. A suitable assignment might ask for a coordinated change, an explanation of affected dependencies and the checks needed to validate the result. Provide current project instructions and useful visual examples, then request an outcome that can be reviewed: changed code, a defect analysis or a tested implementation plan.
", "aboutText": "GPT-5-Codex is OpenAI’s version of GPT-5 optimized for agentic software engineering in Codex and comparable coding environments. Its training targets work such as creating projects, implementing features and tests, debugging, refactoring established codebases and reviewing changes. Reinforcement learning on real coding tasks develops instruction following, code style and iterative verification. The intended setting is a coding assistant with access to project context and development tools.\n\nThe model adjusts how much reasoning it devotes to a task. A small, clearly defined change can be handled as an interactive exchange, while a complex refactor can involve sustained analysis and repeated implementation steps. This adaptive behavior connects conversation with execution: the agent examines the project, proposes a change, receives results from its environment and continues working toward the stated outcome.\n\nCode review is a distinct part of its specialization. OpenAI describes a reviewer that examines the codebase and dependencies, relates a proposed change to its intended behavior, and uses code execution and tests to investigate correctness. The useful output is a focused explanation of consequential defects and the affected code, rather than comments based only on the appearance of a diff. Project files and tool results provide the evidence for that analysis.\n\nVisual input supports frontend work. Screenshots, wireframes and diagrams can convey the desired interface or an observed problem. In an environment with browser and screenshot tools, the agent can inspect the implementation and compare what it built with the supplied visual target. These are environment-assisted workflows: the model interprets information and selects actions, while the application provides the tools and executes them.\n\nThe published model specification lists text and image input and text output, a 400,000-token context window and up to 128,000 output tokens. It supports reasoning tokens, streaming, function calling and structured outputs. OpenAI documents the model through its Responses interface and identifies the unversioned name as an updating model label. Its documented knowledge cutoff is September 30, 2024. Audio and video are outside the listed input modalities; fine-tuning and predicted outputs are not supported in this configuration.\n\nThe system-card addendum separates model training from product controls. The model is trained to resist prompt-injection instructions encountered in coding material. Workspace isolation, network permissions and approval rules belong to the agent environment. This distinction matters when the assistant reads external documentation or executes commands: access is defined by the application, and its outputs provide the record of what actually happened.\n\nFor a coding assistant using Ailoot API, start with the behavior to achieve, the relevant files and a clear completion criterion. A suitable assignment might ask for a coordinated change, an explanation of affected dependencies and the checks needed to validate the result. Provide current project instructions and useful visual examples, then request an outcome that can be reviewed: changed code, a defect analysis or a tested implementation plan.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Review a discount patch for lost items\n\nReview this Python diff concept: old code totals all cart rows; new code filters rows with qty > 1 before summing price * qty, then applies a 10% discount. Requirement: discount every item, including single-quantity rows. Identify the consequential defect, propose the smallest correction and give a regression test with one single-quantity item.A focused defect finding, corrected calculation and a regression-test example with expected total.The regression includes qty=1 and demonstrates that its price contributes before the discount.gpt-5-codex-lowGPT-5-Codex is OpenAI’s GPT-5 variant for agentic software engineering: implementing features, debugging, refactoring and reviewing code in a tool-enabled environment. It combines text and image input with adaptive reasoning and a 400,000-token context.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
GPT-5-Codex is OpenAI’s version of GPT-5 optimized for agentic software engineering in Codex and comparable coding environments. Its training targets work such as creating projects, implementing features and tests, debugging, refactoring established codebases and reviewing changes. Reinforcement learning on real coding tasks develops instruction following, code style and iterative verification. The intended setting is a coding assistant with access to project context and development tools.
The model adjusts how much reasoning it devotes to a task. A small, clearly defined change can be handled as an interactive exchange, while a complex refactor can involve sustained analysis and repeated implementation steps. This adaptive behavior connects conversation with execution: the agent examines the project, proposes a change, receives results from its environment and continues working toward the stated outcome.
Code review is a distinct part of its specialization. OpenAI describes a reviewer that examines the codebase and dependencies, relates a proposed change to its intended behavior, and uses code execution and tests to investigate correctness. The useful output is a focused explanation of consequential defects and the affected code, rather than comments based only on the appearance of a diff. Project files and tool results provide the evidence for that analysis.
Visual input supports frontend work. Screenshots, wireframes and diagrams can convey the desired interface or an observed problem. In an environment with browser and screenshot tools, the agent can inspect the implementation and compare what it built with the supplied visual target. These are environment-assisted workflows: the model interprets information and selects actions, while the application provides the tools and executes them.
The published model specification lists text and image input and text output, a 400,000-token context window and up to 128,000 output tokens. It supports reasoning tokens, streaming, function calling and structured outputs. OpenAI documents the model through its Responses interface and identifies the unversioned name as an updating model label. Its documented knowledge cutoff is September 30, 2024. Audio and video are outside the listed input modalities; fine-tuning and predicted outputs are not supported in this configuration.
The system-card addendum separates model training from product controls. The model is trained to resist prompt-injection instructions encountered in coding material. Workspace isolation, network permissions and approval rules belong to the agent environment. This distinction matters when the assistant reads external documentation or executes commands: access is defined by the application, and its outputs provide the record of what actually happened.
For a coding assistant using Ailoot API, start with the behavior to achieve, the relevant files and a clear completion criterion. A suitable assignment might ask for a coordinated change, an explanation of affected dependencies and the checks needed to validate the result. Provide current project instructions and useful visual examples, then request an outcome that can be reviewed: changed code, a defect analysis or a tested implementation plan.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits gpt-5-codex-low | Default— |
Fieldmessages | Typemessage[] | RequirementRequired | Values & limits Minimum items: 1 | Default— |
Fieldmessages[].role | Typestring | RequirementRequired | Values & 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[].content | Typestring|content_part[]|null | RequirementOptional | Values & limits | Default— |
Fieldmessages[].tool_calls | Typetool_call[] | RequirementOptional | Values & limits | Default— |
Fieldmessages[].tool_call_id | Typestring | RequirementOptional | Values & limits Applies when: role=tool; use actual returned ID | Default— |
Fieldmessages[].name | Typestring | RequirementOptional | Values & limits | Default— |
Fieldmessages[].reasoning_content | Typestring | RequirementOptional | Values & limits Applies when: Round-trip actual returned reasoning where this model uses it | Default— |
Fieldmessages[].reasoning_details | Typeobject|array | RequirementOptional | Values & limits Model-specific | Default— |
Fieldmax_tokens | Typeinteger | RequirementOptional | Values & limits Minimum: 1 Model-specific | Default— |
Fieldmax_completion_tokens | Typeinteger | RequirementOptional | Values & limits Minimum: 1 Model-specific | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
Fieldstream_options.include_usage | Typeboolean | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtemperature | Typenumber | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtop_p | Typenumber | RequirementOptional | Values & limits Model-specific | Default— |
Fieldstop | Typestring|string[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtools | Typefunction_tool[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtools[].type | Typestring | RequirementOptional | Values & limits function Model-specific | Default— |
Fieldtools[].function.name | Typestring | RequirementRequired | Values & limits Applies when: function tool supplied | Default— |
Fieldtools[].function.description | Typestring | RequirementOptional | Values & limits | Default— |
Fieldtools[].function.parameters | TypeJSON_schema | RequirementOptional | Values & limits | Default— |
Fieldtool_choice | Typestring|object | RequirementOptional | Values & limits Model-specific | Default— |
Fieldparallel_tool_calls | Typeboolean | RequirementOptional | Values & limits Model-specific | Default— |
Fieldresponse_format | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldreasoning_effort | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldreasoning | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldservice_tier | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldinstructions | Typestring | RequirementOptional | Values & limits Compatibility field | Default— |
Fieldfunctions | Typefunction[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldfunction_call | Typestring|object | RequirementOptional | Values & limits Model-specific | Default— |
Fieldprompt_cache_options | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldmodalities | Typestring[] | RequirementOptional | Values & limits text | Default— |
Fieldprovider | Typeobject | RequirementOptional | Values & limits Conditions: provider.options is rejected on this Ultra text route | Default— |
Fieldcache_control.ttl | Typestring | RequirementOptional | Values & limits Conditions: 1h is rejected in actual prompt-cache locations | Default— |
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, temperature, top_p, stop, max_tokens, max_completion_tokens, reasoning, response_format, tools, tool_choice |
Parametercontrol_scope | Contractmodel-specific; user function tools differ from hosted tools |
Parameteroutput_modalities | Contracttext |
Parameterhosted_operations | Contractfalse |
Parametermultimodal_input_binding | Contractnull |
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 $0.0025 / 1K tokens Output · Output tokens $0.02 / 1K tokens Input · Cache read $0 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "gpt-5-codex-low", "messages": [{"role": "user", "content": "Explain the difference between an observation and a hypothesis in three sentences."}]}')\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='gpt-5-codex-low',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}])\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"))\nfrom openai import OpenAI\nimport os\nfrom pathlib import Path\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)\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\nwith client.chat.completions.stream(model='gpt-5-codex-low',messages=[{"role":"user","content":"Explain observations and hypotheses in three sentences."}]) as stream:\n with Path("stream-events.jsonl").open("w",encoding="utf-8") as log:\n for event in stream:\n payload=event.to_dict(mode="json")\n log.write(json.dumps(payload,ensure_ascii=False)+"\\n"); log.flush()\n print(json.dumps(payload,ensure_ascii=False))\n final=stream.get_final_completion()\n save_result(final.to_dict(mode="json"),"stream-result")\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 gpt-5-codex-low
Use the method paths shown above.
gpt-5-codex-lowGPT-5-Codex is OpenAI’s GPT-5 variant for agentic software engineering: implementing features, debugging, refactoring and reviewing code in a tool-enabled environment. It combines text and image input with adaptive reasoning and a 400,000-token context.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
GPT-5-Codex is OpenAI’s version of GPT-5 optimized for agentic software engineering in Codex and comparable coding environments. Its training targets work such as creating projects, implementing features and tests, debugging, refactoring established codebases and reviewing changes. Reinforcement learning on real coding tasks develops instruction following, code style and iterative verification. The intended setting is a coding assistant with access to project context and development tools.
The model adjusts how much reasoning it devotes to a task. A small, clearly defined change can be handled as an interactive exchange, while a complex refactor can involve sustained analysis and repeated implementation steps. This adaptive behavior connects conversation with execution: the agent examines the project, proposes a change, receives results from its environment and continues working toward the stated outcome.
Code review is a distinct part of its specialization. OpenAI describes a reviewer that examines the codebase and dependencies, relates a proposed change to its intended behavior, and uses code execution and tests to investigate correctness. The useful output is a focused explanation of consequential defects and the affected code, rather than comments based only on the appearance of a diff. Project files and tool results provide the evidence for that analysis.
Visual input supports frontend work. Screenshots, wireframes and diagrams can convey the desired interface or an observed problem. In an environment with browser and screenshot tools, the agent can inspect the implementation and compare what it built with the supplied visual target. These are environment-assisted workflows: the model interprets information and selects actions, while the application provides the tools and executes them.
The published model specification lists text and image input and text output, a 400,000-token context window and up to 128,000 output tokens. It supports reasoning tokens, streaming, function calling and structured outputs. OpenAI documents the model through its Responses interface and identifies the unversioned name as an updating model label. Its documented knowledge cutoff is September 30, 2024. Audio and video are outside the listed input modalities; fine-tuning and predicted outputs are not supported in this configuration.
The system-card addendum separates model training from product controls. The model is trained to resist prompt-injection instructions encountered in coding material. Workspace isolation, network permissions and approval rules belong to the agent environment. This distinction matters when the assistant reads external documentation or executes commands: access is defined by the application, and its outputs provide the record of what actually happened.
For a coding assistant using Ailoot API, start with the behavior to achieve, the relevant files and a clear completion criterion. A suitable assignment might ask for a coordinated change, an explanation of affected dependencies and the checks needed to validate the result. Provide current project instructions and useful visual examples, then request an outcome that can be reviewed: changed code, a defect analysis or a tested implementation plan.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits gpt-5-codex-low | Default— |
Fieldmessages | Typemessage[] | RequirementRequired | Values & limits | Default— |
Fieldmessages[].role | Typestring | RequirementRequired | Values & limits user, assistant | Default— |
Fieldmessages[].content | Typestring | RequirementRequired | Values & limits | Default— |
Fieldmax_tokens | Typeinteger | RequirementRequired | Values & limits | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
Text · UTF-8 text
messages[].content string
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, messages, max_tokens |
Parameteroptional_controls | Contractstream |
Parametermaterial_types | Contracttext |
Parametertext_file_material | ContractRead the actual UTF-8 document and put its text in the indicated input or message content string. |
Lifecycle: synchronous_or_streamed
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 $0.0025 / 1K tokens Output · Output tokens $0.02 / 1K tokens Input · Cache read $0 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "gpt-5-codex-low",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\n: "${AILOOT_TEXT_FILE:?Set your actual UTF-8 document}"\nexport AILOOT_TEXT_FILE\npython3 - <<'PY_REQUEST'\nimport json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'gpt-5-codex-low', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nPath('request.json').write_text(json.dumps(payload, ensure_ascii=False), encoding='utf-8')\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/messages \\\n -H "x-api-key: $AILOOT_API_KEY" -H 'anthropic-version: 2023-06-01' -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\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\nPY_RESULT\nimport json, os\nfrom pathlib import Path\n\nsource_file = Path(os.environ['AILOOT_TEXT_FILE'])\nif not source_file.is_file():\n raise SystemExit('Set AILOOT_TEXT_FILE to your actual UTF-8 document')\ntext = source_file.read_text(encoding='utf-8')\nif not text.strip():\n raise SystemExit('Choose a document containing text')\npayload = {'model': 'gpt-5-codex-low', 'max_tokens': 128, 'messages': [{'role': 'user', 'content': 'Summarize the incident report.'}]}\npayload['messages'][0]['content'] = 'Summarize this material and quote supporting source passages.\\n\\n' + text\nfrom urllib.request import Request, urlopen\nfrom urllib.error import HTTPError\n\napi_key = os.environ['AILOOT_API_KEY']\nif not api_key.strip():\n raise SystemExit('Set AILOOT_API_KEY before making a request')\nheaders = {'x-api-key': 'PLACEHOLDER_KEY', 'anthropic-version': '2023-06-01', 'Content-Type': 'application/json'}\nheaders['x-api-key'] = api_key\nrequest = Request('https://api.ailoot.ai/v1/messages', data=json.dumps(payload, ensure_ascii=False).encode('utf-8'), headers=headers, method='POST')\ntry:\n with urlopen(request, timeout=120) as response:\n raw = response.read()\n Path('response-headers.txt').write_text(str(response.headers), encoding='utf-8')\nexcept HTTPError as error:\n Path('result.json').write_bytes(error.read())\n Path('response-headers.txt').write_text(str(error.headers), encoding='utf-8')\n raise\nPath('result.json').write_bytes(raw)\nimport json\nfrom pathlib import Path\n\nresult = json.loads(Path('result.json').read_text(encoding='utf-8'))\nprint(json.dumps(result, ensure_ascii=False, indent=2))\nJSON or SSE
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.
Keep request/response IDs and headers. No generic text job retrieval URL is supplied.
| Field | Type |
|---|---|
Fieldid | Typestring |
Fieldmodel | Typestring |
Fieldcontent[] | Typetext|thinking|tool_use|other content block |
Fieldstop_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 gpt-5-codex-low
Use the method paths shown above.
Technical example in English
Review a discount patch for lost items\n\nReview this Python diff concept: old code totals all cart rows; new code filters rows with qty > 1 before summing price * qty, then applies a 10% discount. Requirement: discount every item, including single-quantity rows. Identify the consequential defect, propose the smallest correction and give a regression test with one single-quantity item.A focused defect finding, corrected calculation and a regression-test example with expected total.The regression includes qty=1 and demonstrates that its price contributes before the discount.