Grok 2 Vision 1212 is the visual-input edition of xAI’s Grok 2 family, identified by the grok-2-vision-1212 name. The manufacturer presents Grok 2 as a model for text and visual understanding, with tasks that include document questions and mathematical reasoning from visual material. The later 1212 release announcement describes improvements in instruction following and multilingual behaviour. This card concerns the named vision edition and its text result.
The producer’s model metadata lists text and images as input and text as output. A practical task therefore starts with both the visual evidence and a precise question. For a document page, specify the field, section or relationship you need explained; for a chart, identify the labels and quantities that matter; for a screenshot, describe the user’s goal and the element you want examined. The aim is to relate visible content to the request, rather than ask for an undirected description of everything in the picture.
An illustrative document request is: “Read the headings and labelled fields on this page. Return the visible values, flag entries that cannot be read confidently, and explain which passage supports your answer.” A chart request might ask for the relationship between two series and a distinction between directly visible values and interpretation. For a product photograph, ask for visible features and a comparison with the supplied written specification. These are preparation and output examples based on the manufacturer’s visual task families, not promises that every detail can be recovered from every image.
The named-model example also records support for structured outputs and function calling. For extraction, define the desired result: for instance, a list of fields, their visible text and an uncertainty note. For a tool-assisted task, supply the permitted tool descriptions and the information needed for an operation. The application performs the operation and returns its result; the model can then use that information in the next textual answer. The vision feature itself is the ability to interpret the supplied image, while the external operation belongs to the surrounding application.
This edition is listed without a separate reasoning mode in that producer example. It can still answer questions requiring relationships and explanations, but the explicit extended-thinking modes described for other Grok releases should be kept separate. The same example lists a maximum prompt length of 32,768 tokens. That is the published input field for the named model, not a separately documented maximum answer length. The read primary sources do not establish an exact global image-count, image-resolution or output-token ceiling for Vision 1212, so those values should come from a specific request specification instead of being borrowed from a newer vision model.
The fixed 1212 name also matters when choosing an edition. Grok 2’s text-only release and its image-generation products serve different inputs and outputs. The result described here is a written answer about visual material. Features shown in the Grok application, such as additional search or image-generation experiences, are separate integrations around the family and should be distinguished from the capabilities of this named model entry.
Prepare legible pages and give each image a label when comparing material. State which part of the image is relevant, include any necessary definitions or units in the question, and ask the answer to preserve the original names. For a useful final artifact, choose an explanation, an extraction table or a list of observations linked to the evidence. If the image is ambiguous, retain that uncertainty in the output and check consequential readings against the original material.
", "aboutText": "Grok 2 Vision 1212 is the visual-input edition of xAI’s Grok 2 family, identified by the grok-2-vision-1212 name. The manufacturer presents Grok 2 as a model for text and visual understanding, with tasks that include document questions and mathematical reasoning from visual material. The later 1212 release announcement describes improvements in instruction following and multilingual behaviour. This card concerns the named vision edition and its text result.\n\nThe producer’s model metadata lists text and images as input and text as output. A practical task therefore starts with both the visual evidence and a precise question. For a document page, specify the field, section or relationship you need explained; for a chart, identify the labels and quantities that matter; for a screenshot, describe the user’s goal and the element you want examined. The aim is to relate visible content to the request, rather than ask for an undirected description of everything in the picture.\n\nAn illustrative document request is: “Read the headings and labelled fields on this page. Return the visible values, flag entries that cannot be read confidently, and explain which passage supports your answer.” A chart request might ask for the relationship between two series and a distinction between directly visible values and interpretation. For a product photograph, ask for visible features and a comparison with the supplied written specification. These are preparation and output examples based on the manufacturer’s visual task families, not promises that every detail can be recovered from every image.\n\nThe named-model example also records support for structured outputs and function calling. For extraction, define the desired result: for instance, a list of fields, their visible text and an uncertainty note. For a tool-assisted task, supply the permitted tool descriptions and the information needed for an operation. The application performs the operation and returns its result; the model can then use that information in the next textual answer. The vision feature itself is the ability to interpret the supplied image, while the external operation belongs to the surrounding application.\n\nThis edition is listed without a separate reasoning mode in that producer example. It can still answer questions requiring relationships and explanations, but the explicit extended-thinking modes described for other Grok releases should be kept separate. The same example lists a maximum prompt length of 32,768 tokens. That is the published input field for the named model, not a separately documented maximum answer length. The read primary sources do not establish an exact global image-count, image-resolution or output-token ceiling for Vision 1212, so those values should come from a specific request specification instead of being borrowed from a newer vision model.\n\nThe fixed 1212 name also matters when choosing an edition. Grok 2’s text-only release and its image-generation products serve different inputs and outputs. The result described here is a written answer about visual material. Features shown in the Grok application, such as additional search or image-generation experiences, are separate integrations around the family and should be distinguished from the capabilities of this named model entry.\n\nPrepare legible pages and give each image a label when comparing material. State which part of the image is relevant, include any necessary definitions or units in the question, and ask the answer to preserve the original names. For a useful final artifact, choose an explanation, an extraction table or a list of observations linked to the evidence. If the image is ambiguous, retain that uncertainty in the output and check consequential readings against the original material.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Interpret a transcribed sales chart\n\nUse this chart transcription: January sales 80 units, February 100, March 90. The vertical axis is units, not currency. Write a three-sentence chart caption covering the peak, the change from January to March and one conclusion about revenue that cannot be drawn from these values.A three-sentence caption separating stated sales values, a calculated change and the revenue-information limit.Check that the caption retains units and does not infer revenue or prices from unit counts.grok-2-vision-1212Grok 2 Vision 1212 is xAI’s image-understanding edition for questions about documents, charts, photographs and screenshots. It combines a written question with visual input and returns a text answer, with structured-output and function-calling features documented for the named edition.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Grok 2 Vision 1212 is the visual-input edition of xAI’s Grok 2 family, identified by the grok-2-vision-1212 name. The manufacturer presents Grok 2 as a model for text and visual understanding, with tasks that include document questions and mathematical reasoning from visual material. The later 1212 release announcement describes improvements in instruction following and multilingual behaviour. This card concerns the named vision edition and its text result.
The producer’s model metadata lists text and images as input and text as output. A practical task therefore starts with both the visual evidence and a precise question. For a document page, specify the field, section or relationship you need explained; for a chart, identify the labels and quantities that matter; for a screenshot, describe the user’s goal and the element you want examined. The aim is to relate visible content to the request, rather than ask for an undirected description of everything in the picture.
An illustrative document request is: “Read the headings and labelled fields on this page. Return the visible values, flag entries that cannot be read confidently, and explain which passage supports your answer.” A chart request might ask for the relationship between two series and a distinction between directly visible values and interpretation. For a product photograph, ask for visible features and a comparison with the supplied written specification. These are preparation and output examples based on the manufacturer’s visual task families, not promises that every detail can be recovered from every image.
The named-model example also records support for structured outputs and function calling. For extraction, define the desired result: for instance, a list of fields, their visible text and an uncertainty note. For a tool-assisted task, supply the permitted tool descriptions and the information needed for an operation. The application performs the operation and returns its result; the model can then use that information in the next textual answer. The vision feature itself is the ability to interpret the supplied image, while the external operation belongs to the surrounding application.
This edition is listed without a separate reasoning mode in that producer example. It can still answer questions requiring relationships and explanations, but the explicit extended-thinking modes described for other Grok releases should be kept separate. The same example lists a maximum prompt length of 32,768 tokens. That is the published input field for the named model, not a separately documented maximum answer length. The read primary sources do not establish an exact global image-count, image-resolution or output-token ceiling for Vision 1212, so those values should come from a specific request specification instead of being borrowed from a newer vision model.
The fixed 1212 name also matters when choosing an edition. Grok 2’s text-only release and its image-generation products serve different inputs and outputs. The result described here is a written answer about visual material. Features shown in the Grok application, such as additional search or image-generation experiences, are separate integrations around the family and should be distinguished from the capabilities of this named model entry.
Prepare legible pages and give each image a label when comparing material. State which part of the image is relevant, include any necessary definitions or units in the question, and ask the answer to preserve the original names. For a useful final artifact, choose an explanation, an extraction table or a list of observations linked to the evidence. If the image is ambiguous, retain that uncertainty in the output and check consequential readings against the original material.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits grok-2-vision-1212 | 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 $0.004 / 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": "grok-2-vision-1212", "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='grok-2-vision-1212',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='grok-2-vision-1212',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 grok-2-vision-1212
Use the method paths shown above.
grok-2-vision-1212Grok 2 Vision 1212 is xAI’s image-understanding edition for questions about documents, charts, photographs and screenshots. It combines a written question with visual input and returns a text answer, with structured-output and function-calling features documented for the named edition.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Grok 2 Vision 1212 is the visual-input edition of xAI’s Grok 2 family, identified by the grok-2-vision-1212 name. The manufacturer presents Grok 2 as a model for text and visual understanding, with tasks that include document questions and mathematical reasoning from visual material. The later 1212 release announcement describes improvements in instruction following and multilingual behaviour. This card concerns the named vision edition and its text result.
The producer’s model metadata lists text and images as input and text as output. A practical task therefore starts with both the visual evidence and a precise question. For a document page, specify the field, section or relationship you need explained; for a chart, identify the labels and quantities that matter; for a screenshot, describe the user’s goal and the element you want examined. The aim is to relate visible content to the request, rather than ask for an undirected description of everything in the picture.
An illustrative document request is: “Read the headings and labelled fields on this page. Return the visible values, flag entries that cannot be read confidently, and explain which passage supports your answer.” A chart request might ask for the relationship between two series and a distinction between directly visible values and interpretation. For a product photograph, ask for visible features and a comparison with the supplied written specification. These are preparation and output examples based on the manufacturer’s visual task families, not promises that every detail can be recovered from every image.
The named-model example also records support for structured outputs and function calling. For extraction, define the desired result: for instance, a list of fields, their visible text and an uncertainty note. For a tool-assisted task, supply the permitted tool descriptions and the information needed for an operation. The application performs the operation and returns its result; the model can then use that information in the next textual answer. The vision feature itself is the ability to interpret the supplied image, while the external operation belongs to the surrounding application.
This edition is listed without a separate reasoning mode in that producer example. It can still answer questions requiring relationships and explanations, but the explicit extended-thinking modes described for other Grok releases should be kept separate. The same example lists a maximum prompt length of 32,768 tokens. That is the published input field for the named model, not a separately documented maximum answer length. The read primary sources do not establish an exact global image-count, image-resolution or output-token ceiling for Vision 1212, so those values should come from a specific request specification instead of being borrowed from a newer vision model.
The fixed 1212 name also matters when choosing an edition. Grok 2’s text-only release and its image-generation products serve different inputs and outputs. The result described here is a written answer about visual material. Features shown in the Grok application, such as additional search or image-generation experiences, are separate integrations around the family and should be distinguished from the capabilities of this named model entry.
Prepare legible pages and give each image a label when comparing material. State which part of the image is relevant, include any necessary definitions or units in the question, and ask the answer to preserve the original names. For a useful final artifact, choose an explanation, an extraction table or a list of observations linked to the evidence. If the image is ambiguous, retain that uncertainty in the output and check consequential readings against the original material.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits grok-2-vision-1212 | 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 $0.004 / 1K tokens Output · Output tokens $0.02 / 1K tokens Input · Cache read $0 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "grok-2-vision-1212",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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': 'grok-2-vision-1212', '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': 'grok-2-vision-1212', '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 grok-2-vision-1212
Use the method paths shown above.
Technical example in English
Interpret a transcribed sales chart\n\nUse this chart transcription: January sales 80 units, February 100, March 90. The vertical axis is units, not currency. Write a three-sentence chart caption covering the peak, the change from January to March and one conclusion about revenue that cannot be drawn from these values.A three-sentence caption separating stated sales values, a calculated change and the revenue-information limit.Check that the caption retains units and does not infer revenue or prices from unit counts.