Seed 1.6 Flash is ByteDance Seed’s small multimodal model in the Seed1.6 generation. It processes written instructions and supported visual material and returns text: a direct answer, an explanation, extracted information or tool-call content. The Flash producer profile retains image and video understanding alongside language tasks. Its named identity is separate from the larger adaptive-thinking Seed 1.6 model and from subsequent Seed2.0 editions.
The maker documents a thinking switch for the Flash edition, including support for disabling thinking in the original June reference. A direct-response task can ask for a short explanation or a specified set of fields. A more involved question can use the producer’s thinking workflow while keeping the final answer focused on the requested result. The adaptive FullCoT/NoCoT/AdaCoT description of the larger Seed 1.6 model should not be treated as Flash’s complete control specification.
For a support interaction, provide the user’s question and the relevant product information, then ask for the answer and the source detail it uses. For extraction, define the fields and how to handle an unreadable or absent value. A practical brief is: “Read the supplied page, extract the model name and specification values with their units, and mark any field that is not shown.”
Flash’s documented profile covers image and video understanding. Give a screenshot or page image a label, preserve surrounding context and make the important text readable. Tell the model whether it should explain a chart, identify a visible element or compare two images. Request the visible evidence behind each finding. For video, specify the event or segment and ask for a textual description of the order of actions or changes.
The output is an account of the supplied material. If the task needs an interface action or an edited media artifact, the surrounding application performs the relevant operation and returns the resulting state. Use that observation to continue rather than treating a proposed action as execution.
The producer catalogue documents structured output and tool use for Flash. Supply meaningful field definitions and function descriptions, and state the actions allowed for the task. The model can produce a selected operation or a structured answer; the application checks its arguments and returns the actual observation. Keep source-derived values associated with their page, image or operation so the result can be reviewed.
The original Flash reference profile lists a 256K context, up to 224K input and up to 32K combined output including reasoning. Those are producer profile limits; the available completion also depends on how much of the context is already occupied. Reserve room for the answer and keep a longer conversation focused on observations that matter.
For a useful result, define the output form and the review criterion before generating it. Check numbers against the visual source, keep extracted wording separate from interpretation and supply authoritative context for knowledge-sensitive questions. Later Flash revisions and the larger Seed 1.6 edition retain their own differences; no parameter count or adaptive mode is inferred from the Flash name alone.
", "aboutText": "Seed 1.6 Flash is ByteDance Seed’s small multimodal model in the Seed1.6 generation. It processes written instructions and supported visual material and returns text: a direct answer, an explanation, extracted information or tool-call content. The Flash producer profile retains image and video understanding alongside language tasks. Its named identity is separate from the larger adaptive-thinking Seed 1.6 model and from subsequent Seed2.0 editions.\n\n### Direct and thinking responses\n\nThe maker documents a thinking switch for the Flash edition, including support for disabling thinking in the original June reference. A direct-response task can ask for a short explanation or a specified set of fields. A more involved question can use the producer’s thinking workflow while keeping the final answer focused on the requested result. The adaptive FullCoT/NoCoT/AdaCoT description of the larger Seed 1.6 model should not be treated as Flash’s complete control specification.\n\nFor a support interaction, provide the user’s question and the relevant product information, then ask for the answer and the source detail it uses. For extraction, define the fields and how to handle an unreadable or absent value. A practical brief is: “Read the supplied page, extract the model name and specification values with their units, and mark any field that is not shown.”\n\n### Visual understanding\n\nFlash’s documented profile covers image and video understanding. Give a screenshot or page image a label, preserve surrounding context and make the important text readable. Tell the model whether it should explain a chart, identify a visible element or compare two images. Request the visible evidence behind each finding. For video, specify the event or segment and ask for a textual description of the order of actions or changes.\n\nThe output is an account of the supplied material. If the task needs an interface action or an edited media artifact, the surrounding application performs the relevant operation and returns the resulting state. Use that observation to continue rather than treating a proposed action as execution.\n\n### Structured results and tools\n\nThe producer catalogue documents structured output and tool use for Flash. Supply meaningful field definitions and function descriptions, and state the actions allowed for the task. The model can produce a selected operation or a structured answer; the application checks its arguments and returns the actual observation. Keep source-derived values associated with their page, image or operation so the result can be reviewed.\n\n### Context and output\n\nThe original Flash reference profile lists a 256K context, up to 224K input and up to 32K combined output including reasoning. Those are producer profile limits; the available completion also depends on how much of the context is already occupied. Reserve room for the answer and keep a longer conversation focused on observations that matter.\n\nFor a useful result, define the output form and the review criterion before generating it. Check numbers against the visual source, keep extracted wording separate from interpretation and supply authoritative context for knowledge-sensitive questions. Later Flash revisions and the larger Seed 1.6 edition retain their own differences; no parameter count or adaptive mode is inferred from the Flash name alone.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
A product-policy support answer\n\nUse this product note to answer the customer in two sentences: 'The travel mug holds 450 ml, is hand-wash only, and its lid is replaceable.' Customer: 'Can I put the mug in a dishwasher, and must I buy a whole new mug if the lid breaks?' Add the supporting phrase after the answer.A two-sentence customer reply followed by the relevant source wording.The answer says hand-wash only and replaceable lid without adding a price or replacement procedure.bytedance-seed/seed-1.6-flashSeed 1.6 Flash is the small multimodal Seed1.6 edition for text, image and video understanding. Its producer workflows combine direct or thinking responses with structured text and tool use, suited to focused extraction, visual questions and interactive assistance.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Seed 1.6 Flash is ByteDance Seed’s small multimodal model in the Seed1.6 generation. It processes written instructions and supported visual material and returns text: a direct answer, an explanation, extracted information or tool-call content. The Flash producer profile retains image and video understanding alongside language tasks. Its named identity is separate from the larger adaptive-thinking Seed 1.6 model and from subsequent Seed2.0 editions.
The maker documents a thinking switch for the Flash edition, including support for disabling thinking in the original June reference. A direct-response task can ask for a short explanation or a specified set of fields. A more involved question can use the producer’s thinking workflow while keeping the final answer focused on the requested result. The adaptive FullCoT/NoCoT/AdaCoT description of the larger Seed 1.6 model should not be treated as Flash’s complete control specification.
For a support interaction, provide the user’s question and the relevant product information, then ask for the answer and the source detail it uses. For extraction, define the fields and how to handle an unreadable or absent value. A practical brief is: “Read the supplied page, extract the model name and specification values with their units, and mark any field that is not shown.”
Flash’s documented profile covers image and video understanding. Give a screenshot or page image a label, preserve surrounding context and make the important text readable. Tell the model whether it should explain a chart, identify a visible element or compare two images. Request the visible evidence behind each finding. For video, specify the event or segment and ask for a textual description of the order of actions or changes.
The output is an account of the supplied material. If the task needs an interface action or an edited media artifact, the surrounding application performs the relevant operation and returns the resulting state. Use that observation to continue rather than treating a proposed action as execution.
The producer catalogue documents structured output and tool use for Flash. Supply meaningful field definitions and function descriptions, and state the actions allowed for the task. The model can produce a selected operation or a structured answer; the application checks its arguments and returns the actual observation. Keep source-derived values associated with their page, image or operation so the result can be reviewed.
The original Flash reference profile lists a 256K context, up to 224K input and up to 32K combined output including reasoning. Those are producer profile limits; the available completion also depends on how much of the context is already occupied. Reserve room for the answer and keep a longer conversation focused on observations that matter.
For a useful result, define the output form and the review criterion before generating it. Check numbers against the visual source, keep extracted wording separate from interpretation and supply authoritative context for knowledge-sensitive questions. Later Flash revisions and the larger Seed 1.6 edition retain their own differences; no parameter count or adaptive mode is inferred from the Flash name alone.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits bytedance-seed/seed-1.6-flash | 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 $0.00015 / 1K tokens Output · Output tokens $0.0006 / 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 including128,000 | Charge Input · Input tokens $0.00015 / 1K tokens Output · Output tokens $0.0006 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
| ConfigurationRequest-length tier 2 | Rate schedule— | Context tokens, more than128,000 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.0002 / 1K tokens Output · Output tokens $0.0016 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "bytedance-seed/seed-1.6-flash", "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='bytedance-seed/seed-1.6-flash',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='bytedance-seed/seed-1.6-flash',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 bytedance-seed/seed-1.6-flash
Use the method paths shown above.
bytedance-seed/seed-1.6-flashSeed 1.6 Flash is the small multimodal Seed1.6 edition for text, image and video understanding. Its producer workflows combine direct or thinking responses with structured text and tool use, suited to focused extraction, visual questions and interactive assistance.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Seed 1.6 Flash is ByteDance Seed’s small multimodal model in the Seed1.6 generation. It processes written instructions and supported visual material and returns text: a direct answer, an explanation, extracted information or tool-call content. The Flash producer profile retains image and video understanding alongside language tasks. Its named identity is separate from the larger adaptive-thinking Seed 1.6 model and from subsequent Seed2.0 editions.
The maker documents a thinking switch for the Flash edition, including support for disabling thinking in the original June reference. A direct-response task can ask for a short explanation or a specified set of fields. A more involved question can use the producer’s thinking workflow while keeping the final answer focused on the requested result. The adaptive FullCoT/NoCoT/AdaCoT description of the larger Seed 1.6 model should not be treated as Flash’s complete control specification.
For a support interaction, provide the user’s question and the relevant product information, then ask for the answer and the source detail it uses. For extraction, define the fields and how to handle an unreadable or absent value. A practical brief is: “Read the supplied page, extract the model name and specification values with their units, and mark any field that is not shown.”
Flash’s documented profile covers image and video understanding. Give a screenshot or page image a label, preserve surrounding context and make the important text readable. Tell the model whether it should explain a chart, identify a visible element or compare two images. Request the visible evidence behind each finding. For video, specify the event or segment and ask for a textual description of the order of actions or changes.
The output is an account of the supplied material. If the task needs an interface action or an edited media artifact, the surrounding application performs the relevant operation and returns the resulting state. Use that observation to continue rather than treating a proposed action as execution.
The producer catalogue documents structured output and tool use for Flash. Supply meaningful field definitions and function descriptions, and state the actions allowed for the task. The model can produce a selected operation or a structured answer; the application checks its arguments and returns the actual observation. Keep source-derived values associated with their page, image or operation so the result can be reviewed.
The original Flash reference profile lists a 256K context, up to 224K input and up to 32K combined output including reasoning. Those are producer profile limits; the available completion also depends on how much of the context is already occupied. Reserve room for the answer and keep a longer conversation focused on observations that matter.
For a useful result, define the output form and the review criterion before generating it. Check numbers against the visual source, keep extracted wording separate from interpretation and supply authoritative context for knowledge-sensitive questions. Later Flash revisions and the larger Seed 1.6 edition retain their own differences; no parameter count or adaptive mode is inferred from the Flash name alone.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits bytedance-seed/seed-1.6-flash | 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 $0.00015 / 1K tokens Output · Output tokens $0.0006 / 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 including128,000 | Charge Input · Input tokens $0.00015 / 1K tokens Output · Output tokens $0.0006 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
| ConfigurationRequest-length tier 2 | Rate schedule— | Context tokens, more than128,000 | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.0002 / 1K tokens Output · Output tokens $0.0016 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "bytedance-seed/seed-1.6-flash",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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': 'bytedance-seed/seed-1.6-flash', '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': 'bytedance-seed/seed-1.6-flash', '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 bytedance-seed/seed-1.6-flash
Use the method paths shown above.
Technical example in English
A product-policy support answer\n\nUse this product note to answer the customer in two sentences: 'The travel mug holds 450 ml, is hand-wash only, and its lid is replaceable.' Customer: 'Can I put the mug in a dishwasher, and must I buy a whole new mug if the lid breaks?' Add the supporting phrase after the answer.A two-sentence customer reply followed by the relevant source wording.The answer says hand-wash only and replaceable lid without adding a price or replacement procedure.