Reka Edge is Reka AI’s compact vision-language model for physical and visual applications. The published 2603 edition accepts images or video with a written prompt and generates text; it also supports text-only questions. Its main tasks are scene understanding, video analysis, object detection and tool-oriented interaction informed by visual input.
The model combines a 657-million-parameter vision encoder with a 6.4-billion-parameter language backbone. Its convolutional visual encoder produces 64 tokens per image tile. The published text configuration sets a maximum position count of 16,384, with length scaling from an 8192-position base. Those are properties of the producer’s checkpoint configuration; visual inputs and the conversation still need to fit the context used for a task.
For visual question answering, supply the image and ask about a concrete object, relationship or event. A useful inspection prompt identifies what must be distinguished: the item’s position, a visible condition or a change between frames. For video, include the sequence and describe the event to find. The manufacturer’s application examples span media captions, logo detection, highlights and metadata, as well as robotics, vehicle cameras and visual assistance.
Object detection has a specific producer prompt: Detect: followed by the object description. The description can name one target or several targets. The documented result associates object labels with bounding-box coordinates, using ref and bbox markers. In an application, retain the relationship between each label and its coordinates so that the detected region can be inspected or displayed on the original image.
A practical media-review task can ask for the visible event, the objects involved and a concise caption for an archive. For a scene inspection, define the target object and the relevant condition before requesting a description. Keep what the model observed separate from the next action that an application chooses to take. The model supplies the visual interpretation or proposed tool interaction; the surrounding application executes external actions.
Reka publishes local deployment instructions for the checkpoint, including image, video, detection and text-only examples. The card uses the model’s own conversation template and turn-end marker. It explicitly disables a separate reasoning mode in its local example: visual interpretation is a capability, while an exposed thinking stage is a different feature. The model is designed for edge, on-premise and cloud use; quantized deployment is a separate configuration, not an identity implied by the model name.
For local evaluation, the producer recommends leaving memory headroom for generation: its card gives a 24 GB memory minimum for a Mac, and at least 24 GB of GPU memory plus 24 GB of system memory for a PC setup. It recommends 32 GB or more in the respective memory categories, with approximately 14 GB for the float16 model on a Mac. These deployment figures describe the published local setup. They help distinguish the model’s compact size from the resources needed by an application handling visual inputs.
", "aboutText": "Reka Edge is Reka AI’s compact vision-language model for physical and visual applications. The published 2603 edition accepts images or video with a written prompt and generates text; it also supports text-only questions. Its main tasks are scene understanding, video analysis, object detection and tool-oriented interaction informed by visual input.\n\nThe model combines a 657-million-parameter vision encoder with a 6.4-billion-parameter language backbone. Its convolutional visual encoder produces 64 tokens per image tile. The published text configuration sets a maximum position count of 16,384, with length scaling from an 8192-position base. Those are properties of the producer’s checkpoint configuration; visual inputs and the conversation still need to fit the context used for a task.\n\nFor visual question answering, supply the image and ask about a concrete object, relationship or event. A useful inspection prompt identifies what must be distinguished: the item’s position, a visible condition or a change between frames. For video, include the sequence and describe the event to find. The manufacturer’s application examples span media captions, logo detection, highlights and metadata, as well as robotics, vehicle cameras and visual assistance.\n\nObject detection has a specific producer prompt: Detect: followed by the object description. The description can name one target or several targets. The documented result associates object labels with bounding-box coordinates, using ref and bbox markers. In an application, retain the relationship between each label and its coordinates so that the detected region can be inspected or displayed on the original image.\n\nA practical media-review task can ask for the visible event, the objects involved and a concise caption for an archive. For a scene inspection, define the target object and the relevant condition before requesting a description. Keep what the model observed separate from the next action that an application chooses to take. The model supplies the visual interpretation or proposed tool interaction; the surrounding application executes external actions.\n\nReka publishes local deployment instructions for the checkpoint, including image, video, detection and text-only examples. The card uses the model’s own conversation template and turn-end marker. It explicitly disables a separate reasoning mode in its local example: visual interpretation is a capability, while an exposed thinking stage is a different feature. The model is designed for edge, on-premise and cloud use; quantized deployment is a separate configuration, not an identity implied by the model name.\n\nFor local evaluation, the producer recommends leaving memory headroom for generation: its card gives a 24 GB memory minimum for a Mac, and at least 24 GB of GPU memory plus 24 GB of system memory for a PC setup. It recommends 32 GB or more in the respective memory categories, with approximately 14 GB for the float16 model on a Mac. These deployment figures describe the published local setup. They help distinguish the model’s compact size from the resources needed by an application handling visual inputs.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Caption a described loading scene\n\nWrite a concise archive caption from this textual scene description: a worker stands beside a parked van; two sealed boxes are on a trolley; no loading movement is described. Include the objects and their relationship, and add a short uncertainty note about whether loading has begun. Do not infer motion from proximity.A neutral scene caption and a separate uncertainty note.Check that the caption distinguishes observed positions from an unobserved loading action.rekaai/reka-edgeReka Edge is a 7B vision-language model for image and video understanding, object detection and visually guided tool workflows. It takes visual material and text, produces text, and provides a dedicated detection prompt format.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Reka Edge is Reka AI’s compact vision-language model for physical and visual applications. The published 2603 edition accepts images or video with a written prompt and generates text; it also supports text-only questions. Its main tasks are scene understanding, video analysis, object detection and tool-oriented interaction informed by visual input.
The model combines a 657-million-parameter vision encoder with a 6.4-billion-parameter language backbone. Its convolutional visual encoder produces 64 tokens per image tile. The published text configuration sets a maximum position count of 16,384, with length scaling from an 8192-position base. Those are properties of the producer’s checkpoint configuration; visual inputs and the conversation still need to fit the context used for a task.
For visual question answering, supply the image and ask about a concrete object, relationship or event. A useful inspection prompt identifies what must be distinguished: the item’s position, a visible condition or a change between frames. For video, include the sequence and describe the event to find. The manufacturer’s application examples span media captions, logo detection, highlights and metadata, as well as robotics, vehicle cameras and visual assistance.
Object detection has a specific producer prompt: Detect: followed by the object description. The description can name one target or several targets. The documented result associates object labels with bounding-box coordinates, using ref and bbox markers. In an application, retain the relationship between each label and its coordinates so that the detected region can be inspected or displayed on the original image.
A practical media-review task can ask for the visible event, the objects involved and a concise caption for an archive. For a scene inspection, define the target object and the relevant condition before requesting a description. Keep what the model observed separate from the next action that an application chooses to take. The model supplies the visual interpretation or proposed tool interaction; the surrounding application executes external actions.
Reka publishes local deployment instructions for the checkpoint, including image, video, detection and text-only examples. The card uses the model’s own conversation template and turn-end marker. It explicitly disables a separate reasoning mode in its local example: visual interpretation is a capability, while an exposed thinking stage is a different feature. The model is designed for edge, on-premise and cloud use; quantized deployment is a separate configuration, not an identity implied by the model name.
For local evaluation, the producer recommends leaving memory headroom for generation: its card gives a 24 GB memory minimum for a Mac, and at least 24 GB of GPU memory plus 24 GB of system memory for a PC setup. It recommends 32 GB or more in the respective memory categories, with approximately 14 GB for the float16 model on a Mac. These deployment figures describe the published local setup. They help distinguish the model’s compact size from the resources needed by an application handling visual inputs.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits rekaai/reka-edge | 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.
Match each charge to the conditions shown. Use one rate for each set of alternative configurations.
| Configuration | Rate schedule | Context tokens, more than | Context tokens, up to and including | Charge |
|---|---|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Context tokens, more than— | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.0002 / 1K tokens Output · Output tokens $0.0002 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
| ConfigurationRequest-length tier 1 | Rate schedule— | Context tokens, more than0 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.0002 / 1K tokens Output · Output tokens $0.0002 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "rekaai/reka-edge", "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='rekaai/reka-edge',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='rekaai/reka-edge',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 rekaai/reka-edge
Use the method paths shown above.
rekaai/reka-edgeReka Edge is a 7B vision-language model for image and video understanding, object detection and visually guided tool workflows. It takes visual material and text, produces text, and provides a dedicated detection prompt format.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Reka Edge is Reka AI’s compact vision-language model for physical and visual applications. The published 2603 edition accepts images or video with a written prompt and generates text; it also supports text-only questions. Its main tasks are scene understanding, video analysis, object detection and tool-oriented interaction informed by visual input.
The model combines a 657-million-parameter vision encoder with a 6.4-billion-parameter language backbone. Its convolutional visual encoder produces 64 tokens per image tile. The published text configuration sets a maximum position count of 16,384, with length scaling from an 8192-position base. Those are properties of the producer’s checkpoint configuration; visual inputs and the conversation still need to fit the context used for a task.
For visual question answering, supply the image and ask about a concrete object, relationship or event. A useful inspection prompt identifies what must be distinguished: the item’s position, a visible condition or a change between frames. For video, include the sequence and describe the event to find. The manufacturer’s application examples span media captions, logo detection, highlights and metadata, as well as robotics, vehicle cameras and visual assistance.
Object detection has a specific producer prompt: Detect: followed by the object description. The description can name one target or several targets. The documented result associates object labels with bounding-box coordinates, using ref and bbox markers. In an application, retain the relationship between each label and its coordinates so that the detected region can be inspected or displayed on the original image.
A practical media-review task can ask for the visible event, the objects involved and a concise caption for an archive. For a scene inspection, define the target object and the relevant condition before requesting a description. Keep what the model observed separate from the next action that an application chooses to take. The model supplies the visual interpretation or proposed tool interaction; the surrounding application executes external actions.
Reka publishes local deployment instructions for the checkpoint, including image, video, detection and text-only examples. The card uses the model’s own conversation template and turn-end marker. It explicitly disables a separate reasoning mode in its local example: visual interpretation is a capability, while an exposed thinking stage is a different feature. The model is designed for edge, on-premise and cloud use; quantized deployment is a separate configuration, not an identity implied by the model name.
For local evaluation, the producer recommends leaving memory headroom for generation: its card gives a 24 GB memory minimum for a Mac, and at least 24 GB of GPU memory plus 24 GB of system memory for a PC setup. It recommends 32 GB or more in the respective memory categories, with approximately 14 GB for the float16 model on a Mac. These deployment figures describe the published local setup. They help distinguish the model’s compact size from the resources needed by an application handling visual inputs.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits rekaai/reka-edge | 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.
Match each charge to the conditions shown. Use one rate for each set of alternative configurations.
| Configuration | Rate schedule | Context tokens, more than | Context tokens, up to and including | Charge |
|---|---|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Context tokens, more than— | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.0002 / 1K tokens Output · Output tokens $0.0002 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
| ConfigurationRequest-length tier 1 | Rate schedule— | Context tokens, more than0 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.0002 / 1K tokens Output · Output tokens $0.0002 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "rekaai/reka-edge",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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': 'rekaai/reka-edge', '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': 'rekaai/reka-edge', '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 rekaai/reka-edge
Use the method paths shown above.
Technical example in English
Caption a described loading scene\n\nWrite a concise archive caption from this textual scene description: a worker stands beside a parked van; two sealed boxes are on a trolley; no loading movement is described. Include the objects and their relationship, and add a short uncertainty note about whether loading has begun. Do not infer motion from proximity.A neutral scene caption and a separate uncertainty note.Check that the caption distinguishes observed positions from an unobserved loading action.