DeepSeek V3.2 Exp is DeepSeek’s experimental text-model edition built on V3.1 Terminus. Its defining change is DeepSeek Sparse Attention, introduced to study more efficient training and inference for extended text sequences. The task remains text-based: written instructions and context produce a text answer, code or agent-related output. This is the sparse-attention research edition, separate from the final V3.2 model and its later thinking-with-tools format.
DSA uses a lightweight indexer and fine-grained token selection so the main attention calculation focuses on selected earlier token information. DeepSeek deliberately aligned the experimental training configuration with V3.1 Terminus to isolate the architectural change. The V3.2 report confirms that the final V3.2 and V3.2 Exp share this architecture and describes continued training from the 128K-context Terminus base. The published experimental profile uses that 128K long-context setting.
The producer’s experimental card keeps reasoning and agentic work in scope while preserving the prior model’s task profile. Its evidence covers reasoning without tools and agentic tool use as distinct workflows. Prepare a clear question, relevant source context and the desired final form. If the task uses a tool, the application supplies its definition and returns the result; the model’s proposed action is not the same as an operation that has already executed.
The final V3.2 card introduces a revised tool-call format and explicitly describes thinking with tools. Those updates belong to the final release and should not be treated as the experimental edition’s entire interaction contract. For V3.2 Exp, retain the edition’s own message formatting and the inherited Terminus task context rather than assuming that a similar name makes every later control or role interchangeable.
For a long document comparison, label each source and identify the sections relevant to the question. An illustrative brief is: “Compare these requirements with the proposed implementation, identify each mismatch and cite the passage that establishes it. End with a prioritised correction list.” For code, provide the failing behaviour, relevant fragment and expected result, then ask for a focused change with a clear explanation. The source layout and completion rule help direct a long-context task toward a usable artifact.
The producer’s reference implementation includes the sparse-attention indexer, and its card records an important implementation correction: indexer RoPE uses a non-interleaved layout, whereas the latent-attention module expects an interleaved layout. This is a producer-reference detail relevant to faithful model execution, rather than a different user prompt or a new capability. It also means a model name alone does not describe every implementation’s handling of the experimental architecture.
A continuing agent task must budget its source text, conversation, generated reasoning and tool observations together. Keep the useful observations organised and ask for the final answer to distinguish evidence, assumptions and proposed actions. For a source-dependent factual result, supply the authoritative material or a suitable tool rather than relying on broad training knowledge. The output remains a text, code or analytical record that can be checked against the task and its inputs.
", "aboutText": "DeepSeek V3.2 Exp is DeepSeek’s experimental text-model edition built on V3.1 Terminus. Its defining change is DeepSeek Sparse Attention, introduced to study more efficient training and inference for extended text sequences. The task remains text-based: written instructions and context produce a text answer, code or agent-related output. This is the sparse-attention research edition, separate from the final V3.2 model and its later thinking-with-tools format.\n\nDSA uses a lightweight indexer and fine-grained token selection so the main attention calculation focuses on selected earlier token information. DeepSeek deliberately aligned the experimental training configuration with V3.1 Terminus to isolate the architectural change. The V3.2 report confirms that the final V3.2 and V3.2 Exp share this architecture and describes continued training from the 128K-context Terminus base. The published experimental profile uses that 128K long-context setting.\n\nThe producer’s experimental card keeps reasoning and agentic work in scope while preserving the prior model’s task profile. Its evidence covers reasoning without tools and agentic tool use as distinct workflows. Prepare a clear question, relevant source context and the desired final form. If the task uses a tool, the application supplies its definition and returns the result; the model’s proposed action is not the same as an operation that has already executed.\n\nThe final V3.2 card introduces a revised tool-call format and explicitly describes thinking with tools. Those updates belong to the final release and should not be treated as the experimental edition’s entire interaction contract. For V3.2 Exp, retain the edition’s own message formatting and the inherited Terminus task context rather than assuming that a similar name makes every later control or role interchangeable.\n\nFor a long document comparison, label each source and identify the sections relevant to the question. An illustrative brief is: “Compare these requirements with the proposed implementation, identify each mismatch and cite the passage that establishes it. End with a prioritised correction list.” For code, provide the failing behaviour, relevant fragment and expected result, then ask for a focused change with a clear explanation. The source layout and completion rule help direct a long-context task toward a usable artifact.\n\nThe producer’s reference implementation includes the sparse-attention indexer, and its card records an important implementation correction: indexer RoPE uses a non-interleaved layout, whereas the latent-attention module expects an interleaved layout. This is a producer-reference detail relevant to faithful model execution, rather than a different user prompt or a new capability. It also means a model name alone does not describe every implementation’s handling of the experimental architecture.\n\nA continuing agent task must budget its source text, conversation, generated reasoning and tool observations together. Keep the useful observations organised and ask for the final answer to distinguish evidence, assumptions and proposed actions. For a source-dependent factual result, supply the authoritative material or a suitable tool rather than relying on broad training knowledge. The output remains a text, code or analytical record that can be checked against the task and its inputs.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Compare requirements with a release note\n\nCompare requirement R1: exports must include archived records; R2: date filters use UTC; R3: empty exports still include headers. Implementation note: archived rows are excluded, filters use UTC, and empty exports return an empty file. List mismatches with source labels and propose a correction order without assuming any implementation details beyond these notes.A requirement-by-requirement comparison and a prioritized list of the two documented mismatches.Check that R2 is marked aligned and that each proposed correction points to R1 or R3.deepseek/deepseek-v3.2-expDeepSeek V3.2 Exp is the sparse-attention edition built on V3.1 Terminus. It processes text for reasoning, coding and agent tasks while exploring more efficient long-context attention, with a 128K producer context and a separate identity from the final V3.2 release.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
DeepSeek V3.2 Exp is DeepSeek’s experimental text-model edition built on V3.1 Terminus. Its defining change is DeepSeek Sparse Attention, introduced to study more efficient training and inference for extended text sequences. The task remains text-based: written instructions and context produce a text answer, code or agent-related output. This is the sparse-attention research edition, separate from the final V3.2 model and its later thinking-with-tools format.
DSA uses a lightweight indexer and fine-grained token selection so the main attention calculation focuses on selected earlier token information. DeepSeek deliberately aligned the experimental training configuration with V3.1 Terminus to isolate the architectural change. The V3.2 report confirms that the final V3.2 and V3.2 Exp share this architecture and describes continued training from the 128K-context Terminus base. The published experimental profile uses that 128K long-context setting.
The producer’s experimental card keeps reasoning and agentic work in scope while preserving the prior model’s task profile. Its evidence covers reasoning without tools and agentic tool use as distinct workflows. Prepare a clear question, relevant source context and the desired final form. If the task uses a tool, the application supplies its definition and returns the result; the model’s proposed action is not the same as an operation that has already executed.
The final V3.2 card introduces a revised tool-call format and explicitly describes thinking with tools. Those updates belong to the final release and should not be treated as the experimental edition’s entire interaction contract. For V3.2 Exp, retain the edition’s own message formatting and the inherited Terminus task context rather than assuming that a similar name makes every later control or role interchangeable.
For a long document comparison, label each source and identify the sections relevant to the question. An illustrative brief is: “Compare these requirements with the proposed implementation, identify each mismatch and cite the passage that establishes it. End with a prioritised correction list.” For code, provide the failing behaviour, relevant fragment and expected result, then ask for a focused change with a clear explanation. The source layout and completion rule help direct a long-context task toward a usable artifact.
The producer’s reference implementation includes the sparse-attention indexer, and its card records an important implementation correction: indexer RoPE uses a non-interleaved layout, whereas the latent-attention module expects an interleaved layout. This is a producer-reference detail relevant to faithful model execution, rather than a different user prompt or a new capability. It also means a model name alone does not describe every implementation’s handling of the experimental architecture.
A continuing agent task must budget its source text, conversation, generated reasoning and tool observations together. Keep the useful observations organised and ask for the final answer to distinguish evidence, assumptions and proposed actions. For a source-dependent factual result, supply the authoritative material or a suitable tool rather than relying on broad training knowledge. The output remains a text, code or analytical record that can be checked against the task and its inputs.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits deepseek/deepseek-v3.2-exp | 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 $0.000572 / 1K tokens Output · Output tokens $0.00086 / 1K tokens Input · Cache read $0 / 1K tokens Input · Cache write $0 / 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 $0.000572 / 1K tokens Output · Output tokens $0.00086 / 1K tokens Input · Cache read $0 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "deepseek/deepseek-v3.2-exp", "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='deepseek/deepseek-v3.2-exp',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='deepseek/deepseek-v3.2-exp',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 deepseek/deepseek-v3.2-exp
Use the method paths shown above.
deepseek/deepseek-v3.2-expDeepSeek V3.2 Exp is the sparse-attention edition built on V3.1 Terminus. It processes text for reasoning, coding and agent tasks while exploring more efficient long-context attention, with a 128K producer context and a separate identity from the final V3.2 release.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
DeepSeek V3.2 Exp is DeepSeek’s experimental text-model edition built on V3.1 Terminus. Its defining change is DeepSeek Sparse Attention, introduced to study more efficient training and inference for extended text sequences. The task remains text-based: written instructions and context produce a text answer, code or agent-related output. This is the sparse-attention research edition, separate from the final V3.2 model and its later thinking-with-tools format.
DSA uses a lightweight indexer and fine-grained token selection so the main attention calculation focuses on selected earlier token information. DeepSeek deliberately aligned the experimental training configuration with V3.1 Terminus to isolate the architectural change. The V3.2 report confirms that the final V3.2 and V3.2 Exp share this architecture and describes continued training from the 128K-context Terminus base. The published experimental profile uses that 128K long-context setting.
The producer’s experimental card keeps reasoning and agentic work in scope while preserving the prior model’s task profile. Its evidence covers reasoning without tools and agentic tool use as distinct workflows. Prepare a clear question, relevant source context and the desired final form. If the task uses a tool, the application supplies its definition and returns the result; the model’s proposed action is not the same as an operation that has already executed.
The final V3.2 card introduces a revised tool-call format and explicitly describes thinking with tools. Those updates belong to the final release and should not be treated as the experimental edition’s entire interaction contract. For V3.2 Exp, retain the edition’s own message formatting and the inherited Terminus task context rather than assuming that a similar name makes every later control or role interchangeable.
For a long document comparison, label each source and identify the sections relevant to the question. An illustrative brief is: “Compare these requirements with the proposed implementation, identify each mismatch and cite the passage that establishes it. End with a prioritised correction list.” For code, provide the failing behaviour, relevant fragment and expected result, then ask for a focused change with a clear explanation. The source layout and completion rule help direct a long-context task toward a usable artifact.
The producer’s reference implementation includes the sparse-attention indexer, and its card records an important implementation correction: indexer RoPE uses a non-interleaved layout, whereas the latent-attention module expects an interleaved layout. This is a producer-reference detail relevant to faithful model execution, rather than a different user prompt or a new capability. It also means a model name alone does not describe every implementation’s handling of the experimental architecture.
A continuing agent task must budget its source text, conversation, generated reasoning and tool observations together. Keep the useful observations organised and ask for the final answer to distinguish evidence, assumptions and proposed actions. For a source-dependent factual result, supply the authoritative material or a suitable tool rather than relying on broad training knowledge. The output remains a text, code or analytical record that can be checked against the task and its inputs.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits deepseek/deepseek-v3.2-exp | 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 $0.000572 / 1K tokens Output · Output tokens $0.00086 / 1K tokens Input · Cache read $0 / 1K tokens Input · Cache write $0 / 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 $0.000572 / 1K tokens Output · Output tokens $0.00086 / 1K tokens Input · Cache read $0 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "deepseek/deepseek-v3.2-exp",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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': 'deepseek/deepseek-v3.2-exp', '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': 'deepseek/deepseek-v3.2-exp', '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 deepseek/deepseek-v3.2-exp
Use the method paths shown above.
Technical example in English
Compare requirements with a release note\n\nCompare requirement R1: exports must include archived records; R2: date filters use UTC; R3: empty exports still include headers. Implementation note: archived rows are excluded, filters use UTC, and empty exports return an empty file. List mismatches with source labels and propose a correction order without assuming any implementation details beyond these notes.A requirement-by-requirement comparison and a prioritized list of the two documented mismatches.Check that R2 is marked aligned and that each proposed correction points to R1 or R3.