Seed 2.1 Turbo is ByteDance Seed’s efficient, high-throughput edition of the Seed2.1 family. The manufacturer positions Turbo for product scenarios that need useful everyday assistance and dependable progress through longer tasks, while Pro targets the family’s most demanding reasoning and professional workflows. Turbo works with instructions and source material to produce answers, code, analysis and tool-call content. The family also understands visual and video material, so the task can combine what a user writes with information visible in supplied media.
Seed2.1’s general-agent direction centres on planning, processing files, using tools, maintaining task state and delivering a finished work product. For a document assignment, give the relevant materials meaningful names and identify the decision or artifact they should support. A practical brief is: “Compare these project requirements with the attached proposal, identify unresolved dependencies and write a decision memo with evidence for each finding.” Specify the audience, mandatory facts and the criteria for a usable result.
For spreadsheet or report work, explain the meaning of the fields, the units and how missing values should be handled. Ask for the calculation or conclusion together with its source location. When an application provides tools, the model can choose an operation and use its returned observation to continue the task. The application controls the actual file access and execution; retain that result rather than treating a proposed action as a completed one.
The maker develops the family for requirement analysis, feature implementation, debugging and result validation. Supply the relevant repository context, existing interfaces, the behaviour you need and a reproducible example of the problem. For a change spanning several files, explain the dependencies and which contracts must remain stable. A useful result contains the implementation, an explanation of the change and the observations that establish whether the required behaviour was achieved.
A focused debugging prompt can ask the model to trace a failing input through the code, identify the first incorrect condition and propose a correction. For an interface task, combine the content requirements with a mockup and describe the visible states and interactions. Generated code is a proposed artifact until it runs in its intended application; feed actual failures or screenshots back into the next revision.
Seed2.1 combines document and chart interpretation, spatial understanding and video comprehension with its agent tasks. Prepare readable source images, preserve surrounding labels and tell the model which detail matters. For video, state the segment and the event you want explained, such as a change in a scene or the order of visible actions. Ask the answer to separate an observed detail from an interpretation, with a source page, image label or time reference where applicable.
These inputs support analysis and code generation from visual evidence. Producing a textual editing plan or a proposed operation is distinct from rendering a new video or performing an interface action; those outcomes use the surrounding tools and their actual results.
The manufacturer identifies open-ended professional and frontier-research problems as areas with room for improvement. Keep a difficult assignment tied to its evidence, intermediate observations and completion criteria. Use relevant source material for knowledge-sensitive claims and require the final output to identify unanswered questions. The manufacturer’s published Turbo reference profile has a 256K context. Reserve room in that budget for the conversation, tool returns, reasoning and final result; reasoning also counts towards the completion allowance. Turbo’s edition remains distinct from Pro and earlier Seed releases; prepare the task around its own documented modes and request format.
", "aboutText": "Seed 2.1 Turbo is ByteDance Seed’s efficient, high-throughput edition of the Seed2.1 family. The manufacturer positions Turbo for product scenarios that need useful everyday assistance and dependable progress through longer tasks, while Pro targets the family’s most demanding reasoning and professional workflows. Turbo works with instructions and source material to produce answers, code, analysis and tool-call content. The family also understands visual and video material, so the task can combine what a user writes with information visible in supplied media.\n\n### General agents and useful deliverables\n\nSeed2.1’s general-agent direction centres on planning, processing files, using tools, maintaining task state and delivering a finished work product. For a document assignment, give the relevant materials meaningful names and identify the decision or artifact they should support. A practical brief is: “Compare these project requirements with the attached proposal, identify unresolved dependencies and write a decision memo with evidence for each finding.” Specify the audience, mandatory facts and the criteria for a usable result.\n\nFor spreadsheet or report work, explain the meaning of the fields, the units and how missing values should be handled. Ask for the calculation or conclusion together with its source location. When an application provides tools, the model can choose an operation and use its returned observation to continue the task. The application controls the actual file access and execution; retain that result rather than treating a proposed action as a completed one.\n\n### Coding from requirements through validation\n\nThe maker develops the family for requirement analysis, feature implementation, debugging and result validation. Supply the relevant repository context, existing interfaces, the behaviour you need and a reproducible example of the problem. For a change spanning several files, explain the dependencies and which contracts must remain stable. A useful result contains the implementation, an explanation of the change and the observations that establish whether the required behaviour was achieved.\n\nA focused debugging prompt can ask the model to trace a failing input through the code, identify the first incorrect condition and propose a correction. For an interface task, combine the content requirements with a mockup and describe the visible states and interactions. Generated code is a proposed artifact until it runs in its intended application; feed actual failures or screenshots back into the next revision.\n\n### Multimodal context\n\nSeed2.1 combines document and chart interpretation, spatial understanding and video comprehension with its agent tasks. Prepare readable source images, preserve surrounding labels and tell the model which detail matters. For video, state the segment and the event you want explained, such as a change in a scene or the order of visible actions. Ask the answer to separate an observed detail from an interpretation, with a source page, image label or time reference where applicable.\n\nThese inputs support analysis and code generation from visual evidence. Producing a textual editing plan or a proposed operation is distinct from rendering a new video or performing an interface action; those outcomes use the surrounding tools and their actual results.\n\n### Task boundaries and review\n\nThe manufacturer identifies open-ended professional and frontier-research problems as areas with room for improvement. Keep a difficult assignment tied to its evidence, intermediate observations and completion criteria. Use relevant source material for knowledge-sensitive claims and require the final output to identify unanswered questions. The manufacturer’s published Turbo reference profile has a 256K context. Reserve room in that budget for the conversation, tool returns, reasoning and final result; reasoning also counts towards the completion allowance. Turbo’s edition remains distinct from Pro and earlier Seed releases; prepare the task around its own documented modes and request format.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Prepare a launch dependency memo\n\nWrite a decision memo for a small release. Requirements: password reset and an export button must work before launch. Status notes: password reset passes its check; export displays a button but produces an empty file; documentation is ready. Recommend launch or delay, name the blocking dependency and specify the next evidence needed.A concise recommendation, a dependency finding, and a concrete export acceptance check.Check that a visible export button is not treated as proof of a usable export file.bytedance-seed/seed-2-1-turboSeed 2.1 Turbo is the Seed family’s efficient model for everyday assistance and multi-step productivity work. It connects written and visual source material with coding, document analysis and tool-guided tasks, producing text and proposed actions for the surrounding application.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Seed 2.1 Turbo is ByteDance Seed’s efficient, high-throughput edition of the Seed2.1 family. The manufacturer positions Turbo for product scenarios that need useful everyday assistance and dependable progress through longer tasks, while Pro targets the family’s most demanding reasoning and professional workflows. Turbo works with instructions and source material to produce answers, code, analysis and tool-call content. The family also understands visual and video material, so the task can combine what a user writes with information visible in supplied media.
Seed2.1’s general-agent direction centres on planning, processing files, using tools, maintaining task state and delivering a finished work product. For a document assignment, give the relevant materials meaningful names and identify the decision or artifact they should support. A practical brief is: “Compare these project requirements with the attached proposal, identify unresolved dependencies and write a decision memo with evidence for each finding.” Specify the audience, mandatory facts and the criteria for a usable result.
For spreadsheet or report work, explain the meaning of the fields, the units and how missing values should be handled. Ask for the calculation or conclusion together with its source location. When an application provides tools, the model can choose an operation and use its returned observation to continue the task. The application controls the actual file access and execution; retain that result rather than treating a proposed action as a completed one.
The maker develops the family for requirement analysis, feature implementation, debugging and result validation. Supply the relevant repository context, existing interfaces, the behaviour you need and a reproducible example of the problem. For a change spanning several files, explain the dependencies and which contracts must remain stable. A useful result contains the implementation, an explanation of the change and the observations that establish whether the required behaviour was achieved.
A focused debugging prompt can ask the model to trace a failing input through the code, identify the first incorrect condition and propose a correction. For an interface task, combine the content requirements with a mockup and describe the visible states and interactions. Generated code is a proposed artifact until it runs in its intended application; feed actual failures or screenshots back into the next revision.
Seed2.1 combines document and chart interpretation, spatial understanding and video comprehension with its agent tasks. Prepare readable source images, preserve surrounding labels and tell the model which detail matters. For video, state the segment and the event you want explained, such as a change in a scene or the order of visible actions. Ask the answer to separate an observed detail from an interpretation, with a source page, image label or time reference where applicable.
These inputs support analysis and code generation from visual evidence. Producing a textual editing plan or a proposed operation is distinct from rendering a new video or performing an interface action; those outcomes use the surrounding tools and their actual results.
The manufacturer identifies open-ended professional and frontier-research problems as areas with room for improvement. Keep a difficult assignment tied to its evidence, intermediate observations and completion criteria. Use relevant source material for knowledge-sensitive claims and require the final output to identify unanswered questions. The manufacturer’s published Turbo reference profile has a 256K context. Reserve room in that budget for the conversation, tool returns, reasoning and final result; reasoning also counts towards the completion allowance. Turbo’s edition remains distinct from Pro and earlier Seed releases; prepare the task around its own documented modes and request format.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits bytedance-seed/seed-2-1-turbo | 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 $0.001 / 1K tokens Output · Output tokens $0.005 / 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 $0.001 / 1K tokens Output · Output tokens $0.005 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${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-2-1-turbo", "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-2-1-turbo',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-2-1-turbo',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-2-1-turbo
Use the method paths shown above.
bytedance-seed/seed-2-1-turboSeed 2.1 Turbo is the Seed family’s efficient model for everyday assistance and multi-step productivity work. It connects written and visual source material with coding, document analysis and tool-guided tasks, producing text and proposed actions for the surrounding application.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Seed 2.1 Turbo is ByteDance Seed’s efficient, high-throughput edition of the Seed2.1 family. The manufacturer positions Turbo for product scenarios that need useful everyday assistance and dependable progress through longer tasks, while Pro targets the family’s most demanding reasoning and professional workflows. Turbo works with instructions and source material to produce answers, code, analysis and tool-call content. The family also understands visual and video material, so the task can combine what a user writes with information visible in supplied media.
Seed2.1’s general-agent direction centres on planning, processing files, using tools, maintaining task state and delivering a finished work product. For a document assignment, give the relevant materials meaningful names and identify the decision or artifact they should support. A practical brief is: “Compare these project requirements with the attached proposal, identify unresolved dependencies and write a decision memo with evidence for each finding.” Specify the audience, mandatory facts and the criteria for a usable result.
For spreadsheet or report work, explain the meaning of the fields, the units and how missing values should be handled. Ask for the calculation or conclusion together with its source location. When an application provides tools, the model can choose an operation and use its returned observation to continue the task. The application controls the actual file access and execution; retain that result rather than treating a proposed action as a completed one.
The maker develops the family for requirement analysis, feature implementation, debugging and result validation. Supply the relevant repository context, existing interfaces, the behaviour you need and a reproducible example of the problem. For a change spanning several files, explain the dependencies and which contracts must remain stable. A useful result contains the implementation, an explanation of the change and the observations that establish whether the required behaviour was achieved.
A focused debugging prompt can ask the model to trace a failing input through the code, identify the first incorrect condition and propose a correction. For an interface task, combine the content requirements with a mockup and describe the visible states and interactions. Generated code is a proposed artifact until it runs in its intended application; feed actual failures or screenshots back into the next revision.
Seed2.1 combines document and chart interpretation, spatial understanding and video comprehension with its agent tasks. Prepare readable source images, preserve surrounding labels and tell the model which detail matters. For video, state the segment and the event you want explained, such as a change in a scene or the order of visible actions. Ask the answer to separate an observed detail from an interpretation, with a source page, image label or time reference where applicable.
These inputs support analysis and code generation from visual evidence. Producing a textual editing plan or a proposed operation is distinct from rendering a new video or performing an interface action; those outcomes use the surrounding tools and their actual results.
The manufacturer identifies open-ended professional and frontier-research problems as areas with room for improvement. Keep a difficult assignment tied to its evidence, intermediate observations and completion criteria. Use relevant source material for knowledge-sensitive claims and require the final output to identify unanswered questions. The manufacturer’s published Turbo reference profile has a 256K context. Reserve room in that budget for the conversation, tool returns, reasoning and final result; reasoning also counts towards the completion allowance. Turbo’s edition remains distinct from Pro and earlier Seed releases; prepare the task around its own documented modes and request format.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits bytedance-seed/seed-2-1-turbo | 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 $0.001 / 1K tokens Output · Output tokens $0.005 / 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 $0.001 / 1K tokens Output · Output tokens $0.005 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "bytedance-seed/seed-2-1-turbo",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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-2-1-turbo', '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-2-1-turbo', '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-2-1-turbo
Use the method paths shown above.
Technical example in English
Prepare a launch dependency memo\n\nWrite a decision memo for a small release. Requirements: password reset and an export button must work before launch. Status notes: password reset passes its check; export displays a button but produces an empty file; documentation is ready. Recommend launch or delay, name the blocking dependency and specify the next evidence needed.A concise recommendation, a dependency finding, and a concrete export acceptance check.Check that a visible export button is not treated as proof of a usable export file.