Seed 2.0 Mini is ByteDance Seed’s small model option in the Seed2.0 series. The manufacturer develops Mini for high-throughput, latency-sensitive applications while retaining the family’s language and visual capabilities. The input is a written task with relevant source material, including supported images or video; the output is text such as an answer, classification, extracted record or proposed tool operation.
Mini is useful when an application asks the model to perform a clear operation repeatedly: identify an issue in customer feedback, extract fields from a document, explain a product detail or rewrite a passage for a specific audience. Supply the field definitions and a small example that demonstrates the intended meaning. Explain how to represent an absent value and which original names or units must remain unchanged.
For feedback processing, an illustrative brief is: “Identify the user’s main problem, assign one of these issue categories and quote the sentence that supports the choice. If none applies, explain the missing category.” The expected result is a short, reviewable record that connects a decision to the actual input. This is a better task specification than a request to analyse everything without identifying the downstream use.
The maker evaluates Mini within the Seed2.0 visual and video-understanding family. Mixed-media tasks can join the written question with a chart, page image or visible sequence. Keep table headings and axis labels readable, number multiple inputs and state what should be compared. For a video, describe the segment and the change or action to explain, then ask for a textual account grounded in that segment.
The family’s structured extraction direction helps prepare information for an application, but a well-formed answer still needs to agree with the source. Ask the model to separate a visible value from an inferred interpretation. When a detail cannot be read, preserve that uncertainty in the result rather than replacing it with an expected value.
The documented Mini profile supports deep reasoning, text generation, structured output and tool use. A producer workflow can distinguish reasoning content from the final response; the final answer should follow the requested fields or prose format. When tools are involved, the application supplies their definitions, performs the selected operation and returns the observation. A proposed search or file action is distinct from a completed result.
For a small multi-step task, identify the allowed actions and the point at which it is complete. Keep the relevant observations concise so the next turn retains the evidence needed for its decision. Use actual returned data to revise the answer, and request the final artifact explicitly.
The manufacturer’s published Mini profile has a 256K context and up to 128K tokens of combined output including reasoning. The exact input allowance depends on the documented workflow and revision; the source, history, tool returns and generated response must fit together. The smaller edition is a workload choice rather than a guarantee that every open-ended task can be completed in one pass. Test the result against the fields, evidence and behaviour specified for the task, retaining Mini separately from Lite, Pro and subsequent Seed versions.
", "aboutText": "Seed 2.0 Mini is ByteDance Seed’s small model option in the Seed2.0 series. The manufacturer develops Mini for high-throughput, latency-sensitive applications while retaining the family’s language and visual capabilities. The input is a written task with relevant source material, including supported images or video; the output is text such as an answer, classification, extracted record or proposed tool operation.\n\n### Repeated tasks with a defined result\n\nMini is useful when an application asks the model to perform a clear operation repeatedly: identify an issue in customer feedback, extract fields from a document, explain a product detail or rewrite a passage for a specific audience. Supply the field definitions and a small example that demonstrates the intended meaning. Explain how to represent an absent value and which original names or units must remain unchanged.\n\nFor feedback processing, an illustrative brief is: “Identify the user’s main problem, assign one of these issue categories and quote the sentence that supports the choice. If none applies, explain the missing category.” The expected result is a short, reviewable record that connects a decision to the actual input. This is a better task specification than a request to analyse everything without identifying the downstream use.\n\n### Images and video as evidence\n\nThe maker evaluates Mini within the Seed2.0 visual and video-understanding family. Mixed-media tasks can join the written question with a chart, page image or visible sequence. Keep table headings and axis labels readable, number multiple inputs and state what should be compared. For a video, describe the segment and the change or action to explain, then ask for a textual account grounded in that segment.\n\nThe family’s structured extraction direction helps prepare information for an application, but a well-formed answer still needs to agree with the source. Ask the model to separate a visible value from an inferred interpretation. When a detail cannot be read, preserve that uncertainty in the result rather than replacing it with an expected value.\n\n### Reasoning and tool-guided tasks\n\nThe documented Mini profile supports deep reasoning, text generation, structured output and tool use. A producer workflow can distinguish reasoning content from the final response; the final answer should follow the requested fields or prose format. When tools are involved, the application supplies their definitions, performs the selected operation and returns the observation. A proposed search or file action is distinct from a completed result.\n\nFor a small multi-step task, identify the allowed actions and the point at which it is complete. Keep the relevant observations concise so the next turn retains the evidence needed for its decision. Use actual returned data to revise the answer, and request the final artifact explicitly.\n\n### Limits and edition scope\n\nThe manufacturer’s published Mini profile has a 256K context and up to 128K tokens of combined output including reasoning. The exact input allowance depends on the documented workflow and revision; the source, history, tool returns and generated response must fit together. The smaller edition is a workload choice rather than a guarantee that every open-ended task can be completed in one pass. Test the result against the fields, evidence and behaviour specified for the task, retaining Mini separately from Lite, Pro and subsequent Seed versions.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Extract a delivery complaint\n\nExtract a compact record from this message: Order K42 arrived on Monday, but one of the two bottles was leaking. I have not contacted the courier. Return order_id, issue, affected_quantity and supporting_quote. Use null for any missing field. Preserve the order identifier exactly and do not infer a refund request.A four-field record: order_id, issue, affected_quantity, supporting_quote. Missing values use null.Check that affected_quantity is one, K42 is preserved and the quoted evidence comes from the message.bytedance-seed/seed-2.0-miniSeed 2.0 Mini is the small Seed2.0 edition for responsive assistance and repeated information-processing tasks. It combines text and visual understanding with reasoning and tool-use workflows, returning answers or structured text within 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 Mini is ByteDance Seed’s small model option in the Seed2.0 series. The manufacturer develops Mini for high-throughput, latency-sensitive applications while retaining the family’s language and visual capabilities. The input is a written task with relevant source material, including supported images or video; the output is text such as an answer, classification, extracted record or proposed tool operation.
Mini is useful when an application asks the model to perform a clear operation repeatedly: identify an issue in customer feedback, extract fields from a document, explain a product detail or rewrite a passage for a specific audience. Supply the field definitions and a small example that demonstrates the intended meaning. Explain how to represent an absent value and which original names or units must remain unchanged.
For feedback processing, an illustrative brief is: “Identify the user’s main problem, assign one of these issue categories and quote the sentence that supports the choice. If none applies, explain the missing category.” The expected result is a short, reviewable record that connects a decision to the actual input. This is a better task specification than a request to analyse everything without identifying the downstream use.
The maker evaluates Mini within the Seed2.0 visual and video-understanding family. Mixed-media tasks can join the written question with a chart, page image or visible sequence. Keep table headings and axis labels readable, number multiple inputs and state what should be compared. For a video, describe the segment and the change or action to explain, then ask for a textual account grounded in that segment.
The family’s structured extraction direction helps prepare information for an application, but a well-formed answer still needs to agree with the source. Ask the model to separate a visible value from an inferred interpretation. When a detail cannot be read, preserve that uncertainty in the result rather than replacing it with an expected value.
The documented Mini profile supports deep reasoning, text generation, structured output and tool use. A producer workflow can distinguish reasoning content from the final response; the final answer should follow the requested fields or prose format. When tools are involved, the application supplies their definitions, performs the selected operation and returns the observation. A proposed search or file action is distinct from a completed result.
For a small multi-step task, identify the allowed actions and the point at which it is complete. Keep the relevant observations concise so the next turn retains the evidence needed for its decision. Use actual returned data to revise the answer, and request the final artifact explicitly.
The manufacturer’s published Mini profile has a 256K context and up to 128K tokens of combined output including reasoning. The exact input allowance depends on the documented workflow and revision; the source, history, tool returns and generated response must fit together. The smaller edition is a workload choice rather than a guarantee that every open-ended task can be completed in one pass. Test the result against the fields, evidence and behaviour specified for the task, retaining Mini separately from Lite, Pro and subsequent Seed versions.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits bytedance-seed/seed-2.0-mini | Default— |
Fieldmessages | Typemessage[] | RequirementRequired | Values & limits Minimum items: 1 | Default— |
Fieldmessages[].role | Typestring | RequirementRequired | Values & limits Examples: system, user, assistant, tool, function Conditions: These roles are explicitly represented by Source compatibility types; the exact model role set remains model-specific. Model-specific | Default— |
Fieldmessages[].content | Typestring|content_part[]|null | RequirementOptional | Values & limits | Default— |
Fieldmessages[].tool_calls | Typetool_call[] | RequirementOptional | Values & limits | Default— |
Fieldmessages[].tool_call_id | Typestring | RequirementOptional | Values & limits Applies when: role=tool; use actual returned ID | Default— |
Fieldmessages[].name | Typestring | RequirementOptional | Values & limits | Default— |
Fieldmessages[].reasoning_content | Typestring | RequirementOptional | Values & limits Applies when: Round-trip actual returned reasoning where this model uses it | Default— |
Fieldmessages[].reasoning_details | Typeobject|array | RequirementOptional | Values & limits Model-specific | Default— |
Fieldmax_tokens | Typeinteger | RequirementOptional | Values & limits Minimum: 1 Model-specific | Default— |
Fieldmax_completion_tokens | Typeinteger | RequirementOptional | Values & limits Minimum: 1 Model-specific | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
Fieldstream_options.include_usage | Typeboolean | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtemperature | Typenumber | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtop_p | Typenumber | RequirementOptional | Values & limits Model-specific | Default— |
Fieldstop | Typestring|string[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtools | Typefunction_tool[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtools[].type | Typestring | RequirementOptional | Values & limits function Model-specific | Default— |
Fieldtools[].function.name | Typestring | RequirementRequired | Values & limits Applies when: function tool supplied | Default— |
Fieldtools[].function.description | Typestring | RequirementOptional | Values & limits | Default— |
Fieldtools[].function.parameters | TypeJSON_schema | RequirementOptional | Values & limits | Default— |
Fieldtool_choice | Typestring|object | RequirementOptional | Values & limits Model-specific | Default— |
Fieldparallel_tool_calls | Typeboolean | RequirementOptional | Values & limits Model-specific | Default— |
Fieldresponse_format | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldreasoning_effort | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldreasoning | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldservice_tier | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldinstructions | Typestring | RequirementOptional | Values & limits Compatibility field | Default— |
Fieldfunctions | Typefunction[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldfunction_call | Typestring|object | RequirementOptional | Values & limits Model-specific | Default— |
Fieldprompt_cache_options | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldmodalities | Typestring[] | RequirementOptional | Values & limits text | Default— |
Fieldprovider | Typeobject | RequirementOptional | Values & limits Conditions: provider.options is rejected on this Ultra text route | Default— |
Fieldcache_control.ttl | Typestring | RequirementOptional | Values & limits Conditions: 1h is rejected in actual prompt-cache locations | Default— |
Text · UTF8 text
messages[].content
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, messages |
Parametermaterial_types | Contracttext |
Parametermessages | Contractmessage array with role/content shape |
Parameterfile_material | Contractactual UTF-8 document inserted as literal text; no file ID inferred |
Parameteroptional_controls | Contractstream, temperature, top_p, stop, max_tokens, max_completion_tokens, reasoning, response_format, tools, tool_choice |
Parametercontrol_scope | Contractmodel-specific; user function tools differ from hosted tools |
Parameteroutput_modalities | Contracttext |
Parameterhosted_operations | Contractfalse |
Parametermultimodal_input_binding | Contractnull |
Lifecycle: synchronous_or_streamed
Send exact model ID with messages.
Context length is not an output cap.
A text file may supply literal text; image/audio/file-object inputs need exact binding.
Tariff references do not add request options. Supported parameters are listed for the selected method.
Match each charge to the conditions shown. Use one rate for each set of alternative configurations.
| Configuration | Rate schedule | Context tokens, more than | Context tokens, up to and including | Charge |
|---|---|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Context tokens, more than— | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.0002 / 1K tokens Output · Output tokens $0.0008 / 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 $0.0002 / 1K tokens Output · Output tokens $0.0008 / 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 $0.0004 / 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-2.0-mini", "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-mini',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-mini',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-mini
Use the method paths shown above.
bytedance-seed/seed-2.0-miniSeed 2.0 Mini is the small Seed2.0 edition for responsive assistance and repeated information-processing tasks. It combines text and visual understanding with reasoning and tool-use workflows, returning answers or structured text within 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 Mini is ByteDance Seed’s small model option in the Seed2.0 series. The manufacturer develops Mini for high-throughput, latency-sensitive applications while retaining the family’s language and visual capabilities. The input is a written task with relevant source material, including supported images or video; the output is text such as an answer, classification, extracted record or proposed tool operation.
Mini is useful when an application asks the model to perform a clear operation repeatedly: identify an issue in customer feedback, extract fields from a document, explain a product detail or rewrite a passage for a specific audience. Supply the field definitions and a small example that demonstrates the intended meaning. Explain how to represent an absent value and which original names or units must remain unchanged.
For feedback processing, an illustrative brief is: “Identify the user’s main problem, assign one of these issue categories and quote the sentence that supports the choice. If none applies, explain the missing category.” The expected result is a short, reviewable record that connects a decision to the actual input. This is a better task specification than a request to analyse everything without identifying the downstream use.
The maker evaluates Mini within the Seed2.0 visual and video-understanding family. Mixed-media tasks can join the written question with a chart, page image or visible sequence. Keep table headings and axis labels readable, number multiple inputs and state what should be compared. For a video, describe the segment and the change or action to explain, then ask for a textual account grounded in that segment.
The family’s structured extraction direction helps prepare information for an application, but a well-formed answer still needs to agree with the source. Ask the model to separate a visible value from an inferred interpretation. When a detail cannot be read, preserve that uncertainty in the result rather than replacing it with an expected value.
The documented Mini profile supports deep reasoning, text generation, structured output and tool use. A producer workflow can distinguish reasoning content from the final response; the final answer should follow the requested fields or prose format. When tools are involved, the application supplies their definitions, performs the selected operation and returns the observation. A proposed search or file action is distinct from a completed result.
For a small multi-step task, identify the allowed actions and the point at which it is complete. Keep the relevant observations concise so the next turn retains the evidence needed for its decision. Use actual returned data to revise the answer, and request the final artifact explicitly.
The manufacturer’s published Mini profile has a 256K context and up to 128K tokens of combined output including reasoning. The exact input allowance depends on the documented workflow and revision; the source, history, tool returns and generated response must fit together. The smaller edition is a workload choice rather than a guarantee that every open-ended task can be completed in one pass. Test the result against the fields, evidence and behaviour specified for the task, retaining Mini separately from Lite, Pro and subsequent Seed versions.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits bytedance-seed/seed-2.0-mini | Default— |
Fieldmessages | Typemessage[] | RequirementRequired | Values & limits | Default— |
Fieldmessages[].role | Typestring | RequirementRequired | Values & limits user, assistant | Default— |
Fieldmessages[].content | Typestring | RequirementRequired | Values & limits | Default— |
Fieldmax_tokens | Typeinteger | RequirementRequired | Values & limits | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
Text · UTF-8 text
messages[].content string
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, messages, max_tokens |
Parameteroptional_controls | Contractstream |
Parametermaterial_types | Contracttext |
Parametertext_file_material | ContractRead the actual UTF-8 document and put its text in the indicated input or message content string. |
Lifecycle: synchronous_or_streamed
Tariff references do not add request options. Supported parameters are listed for the selected method.
Match each charge to the conditions shown. Use one rate for each set of alternative configurations.
| Configuration | Rate schedule | Context tokens, more than | Context tokens, up to and including | Charge |
|---|---|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Context tokens, more than— | Context tokens, up to and includingNo upper bound | Charge Input · Input tokens $0.0002 / 1K tokens Output · Output tokens $0.0008 / 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 $0.0002 / 1K tokens Output · Output tokens $0.0008 / 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 $0.0004 / 1K tokens Output · Output tokens $0.0016 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "bytedance-seed/seed-2.0-mini",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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-mini', '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-mini', '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-mini
Use the method paths shown above.
Technical example in English
Extract a delivery complaint\n\nExtract a compact record from this message: Order K42 arrived on Monday, but one of the two bottles was leaking. I have not contacted the courier. Return order_id, issue, affected_quantity and supporting_quote. Use null for any missing field. Preserve the order identifier exactly and do not infer a refund request.A four-field record: order_id, issue, affected_quantity, supporting_quote. Missing values use null.Check that affected_quantity is one, K42 is preserved and the quoted evidence comes from the message.