Seed 2.0 Lite is ByteDance Seed’s balanced general-purpose model in the Seed2.0 series. The manufacturer places Lite between the family’s more demanding Pro direction and the smaller Mini option, emphasising a practical balance of capability and responsiveness. It accepts text and visual material and returns text, making it useful for document assistance, image-grounded questions, coding help and multi-step information processing.
The Seed2.0 family develops visual reasoning and structured extraction from screenshots, charts, scanned documents and mixed-media content. For a document task, label the pages and preserve headings, table labels and units. Ask for the fields or conclusions you need, with a source reference for each. A practical brief is: “Extract the product specifications from these pages, preserve the original units and identify any disagreement between the table and the explanatory paragraph.”
For an image, make the relevant lettering readable and state which object or region the question concerns. For video understanding, identify the event, sequence or segment to analyse and request a textual description tied to its time or source. The result is an interpretation or extraction from supplied media; creating a new image or rendered video is a separate task.
The producer’s Lite workflows support deep thinking and direct-response use. Thinking output and final answer content have distinct roles: the reasoning works through the task, while the answer should match the requested deliverable. State the desired fields, answer language and treatment of missing information. For routine extraction, ask for the defined record; for a comparison, provide the criteria and require evidence for each conclusion.
Lite can participate in a tool-guided task when the surrounding application supplies the tool definitions and observations. Describe what each operation does and the actions permitted for the assignment. A selected operation becomes evidence only after the application returns its actual result. Keep those observations associated with the task instead of expecting the model’s text alone to retrieve a file or execute code.
The published Lite profile uses a 256K context and lists up to 128K tokens of combined output including reasoning. Input ceilings differ between the manufacturer’s text and visual workflow tables and between named revisions, so the context figure should be used together with the chosen request specification. Reasoning, history and tool results share the available budget with the source and final answer. A focused collection of relevant materials is more useful than an unstructured set of files.
A support assistant can turn a user’s question and supplied product material into an evidence-backed explanation. A feedback-analysis task can identify recurring issues and return a structured summary; a coding brief can ask for a focused change tied to the supplied implementation. Give each task an output form and a completion criterion.
The manufacturer treats complex context integration and end-to-end real-world work as areas that still need improvement. Review extracted numbers against the source, inspect code in its intended application and provide current evidence for knowledge-sensitive claims. Lite retains its own edition and workload role; it does not silently acquire the capabilities or defaults of a later Seed release.
", "aboutText": "Seed 2.0 Lite is ByteDance Seed’s balanced general-purpose model in the Seed2.0 series. The manufacturer places Lite between the family’s more demanding Pro direction and the smaller Mini option, emphasising a practical balance of capability and responsiveness. It accepts text and visual material and returns text, making it useful for document assistance, image-grounded questions, coding help and multi-step information processing.\n\n### Understanding mixed source material\n\nThe Seed2.0 family develops visual reasoning and structured extraction from screenshots, charts, scanned documents and mixed-media content. For a document task, label the pages and preserve headings, table labels and units. Ask for the fields or conclusions you need, with a source reference for each. A practical brief is: “Extract the product specifications from these pages, preserve the original units and identify any disagreement between the table and the explanatory paragraph.”\n\nFor an image, make the relevant lettering readable and state which object or region the question concerns. For video understanding, identify the event, sequence or segment to analyse and request a textual description tied to its time or source. The result is an interpretation or extraction from supplied media; creating a new image or rendered video is a separate task.\n\n### Reasoning and instruction following\n\nThe producer’s Lite workflows support deep thinking and direct-response use. Thinking output and final answer content have distinct roles: the reasoning works through the task, while the answer should match the requested deliverable. State the desired fields, answer language and treatment of missing information. For routine extraction, ask for the defined record; for a comparison, provide the criteria and require evidence for each conclusion.\n\nLite can participate in a tool-guided task when the surrounding application supplies the tool definitions and observations. Describe what each operation does and the actions permitted for the assignment. A selected operation becomes evidence only after the application returns its actual result. Keep those observations associated with the task instead of expecting the model’s text alone to retrieve a file or execute code.\n\n### Context and response budgets\n\nThe published Lite profile uses a 256K context and lists up to 128K tokens of combined output including reasoning. Input ceilings differ between the manufacturer’s text and visual workflow tables and between named revisions, so the context figure should be used together with the chosen request specification. Reasoning, history and tool results share the available budget with the source and final answer. A focused collection of relevant materials is more useful than an unstructured set of files.\n\n### Practical results and limitations\n\nA support assistant can turn a user’s question and supplied product material into an evidence-backed explanation. A feedback-analysis task can identify recurring issues and return a structured summary; a coding brief can ask for a focused change tied to the supplied implementation. Give each task an output form and a completion criterion.\n\nThe manufacturer treats complex context integration and end-to-end real-world work as areas that still need improvement. Review extracted numbers against the source, inspect code in its intended application and provide current evidence for knowledge-sensitive claims. Lite retains its own edition and workload role; it does not silently acquire the capabilities or defaults of a later Seed release.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Reconcile product specifications\n\nCompare the supplied product text. Table T: battery 4,000 mAh; weight 180 g; USB-C charging. Paragraph P: weighs 180 g, charges by USB-C, contains a 4,500 mAh battery. Produce a specification table with source labels, mark conflicts and draft one question to the product owner. Do not choose a battery value without evidence.A field-by-field comparison table and one clarification question about the conflicting specification.Verify that both battery values and their T/P source labels survive in the result.bytedance-seed/seed-2.0-liteSeed 2.0 Lite balances the Seed2.0 family’s reasoning and multimodal understanding for general-purpose applications. It handles written instructions, images and video to produce text, structured findings and proposed tool actions, with a 256K producer context.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Seed 2.0 Lite is ByteDance Seed’s balanced general-purpose model in the Seed2.0 series. The manufacturer places Lite between the family’s more demanding Pro direction and the smaller Mini option, emphasising a practical balance of capability and responsiveness. It accepts text and visual material and returns text, making it useful for document assistance, image-grounded questions, coding help and multi-step information processing.
The Seed2.0 family develops visual reasoning and structured extraction from screenshots, charts, scanned documents and mixed-media content. For a document task, label the pages and preserve headings, table labels and units. Ask for the fields or conclusions you need, with a source reference for each. A practical brief is: “Extract the product specifications from these pages, preserve the original units and identify any disagreement between the table and the explanatory paragraph.”
For an image, make the relevant lettering readable and state which object or region the question concerns. For video understanding, identify the event, sequence or segment to analyse and request a textual description tied to its time or source. The result is an interpretation or extraction from supplied media; creating a new image or rendered video is a separate task.
The producer’s Lite workflows support deep thinking and direct-response use. Thinking output and final answer content have distinct roles: the reasoning works through the task, while the answer should match the requested deliverable. State the desired fields, answer language and treatment of missing information. For routine extraction, ask for the defined record; for a comparison, provide the criteria and require evidence for each conclusion.
Lite can participate in a tool-guided task when the surrounding application supplies the tool definitions and observations. Describe what each operation does and the actions permitted for the assignment. A selected operation becomes evidence only after the application returns its actual result. Keep those observations associated with the task instead of expecting the model’s text alone to retrieve a file or execute code.
The published Lite profile uses a 256K context and lists up to 128K tokens of combined output including reasoning. Input ceilings differ between the manufacturer’s text and visual workflow tables and between named revisions, so the context figure should be used together with the chosen request specification. Reasoning, history and tool results share the available budget with the source and final answer. A focused collection of relevant materials is more useful than an unstructured set of files.
A support assistant can turn a user’s question and supplied product material into an evidence-backed explanation. A feedback-analysis task can identify recurring issues and return a structured summary; a coding brief can ask for a focused change tied to the supplied implementation. Give each task an output form and a completion criterion.
The manufacturer treats complex context integration and end-to-end real-world work as areas that still need improvement. Review extracted numbers against the source, inspect code in its intended application and provide current evidence for knowledge-sensitive claims. Lite retains its own edition and workload role; it does not silently acquire the capabilities or defaults of a later Seed release.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits bytedance-seed/seed-2.0-lite | 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 $0.0005 / 1K tokens Output · Output tokens $0.004 / 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 $0.0005 / 1K tokens Output · Output tokens $0.004 / 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 $0.001 / 1K tokens Output · Output tokens $0.008 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${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-2.0-lite", "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-2.0-lite',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-2.0-lite',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-2.0-lite
Use the method paths shown above.
bytedance-seed/seed-2.0-liteSeed 2.0 Lite balances the Seed2.0 family’s reasoning and multimodal understanding for general-purpose applications. It handles written instructions, images and video to produce text, structured findings and proposed tool actions, with a 256K producer context.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Seed 2.0 Lite is ByteDance Seed’s balanced general-purpose model in the Seed2.0 series. The manufacturer places Lite between the family’s more demanding Pro direction and the smaller Mini option, emphasising a practical balance of capability and responsiveness. It accepts text and visual material and returns text, making it useful for document assistance, image-grounded questions, coding help and multi-step information processing.
The Seed2.0 family develops visual reasoning and structured extraction from screenshots, charts, scanned documents and mixed-media content. For a document task, label the pages and preserve headings, table labels and units. Ask for the fields or conclusions you need, with a source reference for each. A practical brief is: “Extract the product specifications from these pages, preserve the original units and identify any disagreement between the table and the explanatory paragraph.”
For an image, make the relevant lettering readable and state which object or region the question concerns. For video understanding, identify the event, sequence or segment to analyse and request a textual description tied to its time or source. The result is an interpretation or extraction from supplied media; creating a new image or rendered video is a separate task.
The producer’s Lite workflows support deep thinking and direct-response use. Thinking output and final answer content have distinct roles: the reasoning works through the task, while the answer should match the requested deliverable. State the desired fields, answer language and treatment of missing information. For routine extraction, ask for the defined record; for a comparison, provide the criteria and require evidence for each conclusion.
Lite can participate in a tool-guided task when the surrounding application supplies the tool definitions and observations. Describe what each operation does and the actions permitted for the assignment. A selected operation becomes evidence only after the application returns its actual result. Keep those observations associated with the task instead of expecting the model’s text alone to retrieve a file or execute code.
The published Lite profile uses a 256K context and lists up to 128K tokens of combined output including reasoning. Input ceilings differ between the manufacturer’s text and visual workflow tables and between named revisions, so the context figure should be used together with the chosen request specification. Reasoning, history and tool results share the available budget with the source and final answer. A focused collection of relevant materials is more useful than an unstructured set of files.
A support assistant can turn a user’s question and supplied product material into an evidence-backed explanation. A feedback-analysis task can identify recurring issues and return a structured summary; a coding brief can ask for a focused change tied to the supplied implementation. Give each task an output form and a completion criterion.
The manufacturer treats complex context integration and end-to-end real-world work as areas that still need improvement. Review extracted numbers against the source, inspect code in its intended application and provide current evidence for knowledge-sensitive claims. Lite retains its own edition and workload role; it does not silently acquire the capabilities or defaults of a later Seed release.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits bytedance-seed/seed-2.0-lite | 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 $0.0005 / 1K tokens Output · Output tokens $0.004 / 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 $0.0005 / 1K tokens Output · Output tokens $0.004 / 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 $0.001 / 1K tokens Output · Output tokens $0.008 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "bytedance-seed/seed-2.0-lite",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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-2.0-lite', '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-2.0-lite', '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-2.0-lite
Use the method paths shown above.
Technical example in English
Reconcile product specifications\n\nCompare the supplied product text. Table T: battery 4,000 mAh; weight 180 g; USB-C charging. Paragraph P: weighs 180 g, charges by USB-C, contains a 4,500 mAh battery. Produce a specification table with source labels, mark conflicts and draft one question to the product owner. Do not choose a battery value without evidence.A field-by-field comparison table and one clarification question about the conflicting specification.Verify that both battery values and their T/P source labels survive in the result.