DeepSeek V3.2 is DeepSeek’s text model for reasoning and agent work with an emphasis on efficient long-context processing. The producer organises its main changes around DeepSeek Sparse Attention, a scaled reinforcement-learning framework and a pipeline for training agentic tasks. It accepts written questions and context and produces text, including an answer, code or tool-call content. It is distinct from the earlier V3.2 Exp release and the separate Speciale reasoning variant.
DeepSeek Sparse Attention adds an indexer and fine-grained token selection to the latent-attention architecture. For a long sequence, the indexer selects the earlier token information used by the main attention calculation. The report says this is the architectural change from V3.1 Terminus and uses the same architecture as V3.2 Exp. Its published context is 128K tokens, which supports substantial source material and a continuing task, while generated reasoning and tool history also consume the available context.
Post-training covers both thinking and non-thinking responses across mathematics, programming, logical reasoning, general agent tasks, agentic coding and search. Domain specialists supply data that is consolidated into the final model, followed by mixed reinforcement learning. For an ordinary question, a direct answer may be appropriate; for a difficult problem, the thinking workflow can develop a solution before presenting the final result. Specify the required answer or artifact independently of the intermediate reasoning.
A substantial edition change is thinking with tools. The updated chat format allows reasoning to continue through a tool-calling task rather than treating analysis and action as unrelated stages. A model-selected operation is executed by the application, which returns its result for the next step. The producer’s tool-context rule retains historical reasoning while only tool-related messages are added; when a new user message arrives, it discards that reasoning while preserving tool calls and results. Correctly distinguished conversation roles matter to that behaviour.
For coding, provide the source fragment or relevant files, the observed problem, the expected result and the conditions the change must preserve. An illustrative brief is: “Use the supplied code and tool results to locate the cause, propose the smallest correction and return the final patch with its supporting validation result.” For a search task, describe the question, required evidence and what counts as a completed answer. Search and execution tools are supplied by the surrounding application rather than by the text alone.
The producer recommends temperature 1.0 and top-p 0.95 for local use. V3.2 introduces a revised tool-call representation and a dedicated message encoder. Its developer role is reserved for the producer’s search-agent template, and the official API does not accept that role. The reference parser expects well-formed output and does not repair malformed results; when an application uses generated fields or tool arguments, it should check their structure before acting on them.
The technical report identifies long reasoning trajectories and redundant self-verification as practical limitations. An agent task can reach the 128K boundary even when the original source material fits, because tool outputs and repeated reasoning accumulate. Keep a concise record of relevant observations and make the final artifact explicit. For knowledge-sensitive work, attach the authoritative material or provide a suitable search tool rather than expecting training knowledge to define every fact.
DeepSeek V3.2 Speciale is a separate high-compute deep-reasoning variant and does not support tool calling in the model card. Its features should not be used as the standard V3.2 tool profile. Likewise, the later V4 generation and the experimental predecessor have separate identities and configuration choices. Retain the named V3.2 edition when comparing tasks whose model and prompting format matter.
For a practical source-based analysis, label the materials and ask for a conclusion supported by the relevant passages, with assumptions and missing information kept distinct. For an agent, state the allowed functions, the expected final result and the completion criterion. This prepares the model’s reasoning-and-tool workflow around a reviewable text, code or analytical artifact tied to the supplied evidence.
", "aboutText": "DeepSeek V3.2 is DeepSeek’s text model for reasoning and agent work with an emphasis on efficient long-context processing. The producer organises its main changes around DeepSeek Sparse Attention, a scaled reinforcement-learning framework and a pipeline for training agentic tasks. It accepts written questions and context and produces text, including an answer, code or tool-call content. It is distinct from the earlier V3.2 Exp release and the separate Speciale reasoning variant.\n\nDeepSeek Sparse Attention adds an indexer and fine-grained token selection to the latent-attention architecture. For a long sequence, the indexer selects the earlier token information used by the main attention calculation. The report says this is the architectural change from V3.1 Terminus and uses the same architecture as V3.2 Exp. Its published context is 128K tokens, which supports substantial source material and a continuing task, while generated reasoning and tool history also consume the available context.\n\nPost-training covers both thinking and non-thinking responses across mathematics, programming, logical reasoning, general agent tasks, agentic coding and search. Domain specialists supply data that is consolidated into the final model, followed by mixed reinforcement learning. For an ordinary question, a direct answer may be appropriate; for a difficult problem, the thinking workflow can develop a solution before presenting the final result. Specify the required answer or artifact independently of the intermediate reasoning.\n\nA substantial edition change is thinking with tools. The updated chat format allows reasoning to continue through a tool-calling task rather than treating analysis and action as unrelated stages. A model-selected operation is executed by the application, which returns its result for the next step. The producer’s tool-context rule retains historical reasoning while only tool-related messages are added; when a new user message arrives, it discards that reasoning while preserving tool calls and results. Correctly distinguished conversation roles matter to that behaviour.\n\nFor coding, provide the source fragment or relevant files, the observed problem, the expected result and the conditions the change must preserve. An illustrative brief is: “Use the supplied code and tool results to locate the cause, propose the smallest correction and return the final patch with its supporting validation result.” For a search task, describe the question, required evidence and what counts as a completed answer. Search and execution tools are supplied by the surrounding application rather than by the text alone.\n\nThe producer recommends temperature 1.0 and top-p 0.95 for local use. V3.2 introduces a revised tool-call representation and a dedicated message encoder. Its developer role is reserved for the producer’s search-agent template, and the official API does not accept that role. The reference parser expects well-formed output and does not repair malformed results; when an application uses generated fields or tool arguments, it should check their structure before acting on them.\n\nThe technical report identifies long reasoning trajectories and redundant self-verification as practical limitations. An agent task can reach the 128K boundary even when the original source material fits, because tool outputs and repeated reasoning accumulate. Keep a concise record of relevant observations and make the final artifact explicit. For knowledge-sensitive work, attach the authoritative material or provide a suitable search tool rather than expecting training knowledge to define every fact.\n\nDeepSeek V3.2 Speciale is a separate high-compute deep-reasoning variant and does not support tool calling in the model card. Its features should not be used as the standard V3.2 tool profile. Likewise, the later V4 generation and the experimental predecessor have separate identities and configuration choices. Retain the named V3.2 edition when comparing tasks whose model and prompting format matter.\n\nFor a practical source-based analysis, label the materials and ask for a conclusion supported by the relevant passages, with assumptions and missing information kept distinct. For an agent, state the allowed functions, the expected final result and the completion criterion. This prepares the model’s reasoning-and-tool workflow around a reviewable text, code or analytical artifact tied to the supplied evidence.", "editorialOverlay": { "copy_fields": { "practical_html": "Technical example in English
Repair an overwritten grouped total\n\nReview this Python fragment: totals = {}\nfor row in rows:\n totals[row['team']] = row['hours']\n For rows with teams A, B, A and hours 2, 3, 4, the last A overwrites the first. Return a minimal accumulation fix, explain the state changes and propose assertions for repeated teams and empty input.Corrected grouping code, a short state trace and two assertions with concrete inputs.Repeated teams accumulate all hours while an empty list returns an empty mapping.deepseek/deepseek-v3.2DeepSeek V3.2 combines text reasoning with tool use and long-context analysis. Its sparse-attention architecture and updated conversation format support coding, search and multi-step agent tasks, with a 128K context and separate thinking and direct-response workflows.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
DeepSeek V3.2 is DeepSeek’s text model for reasoning and agent work with an emphasis on efficient long-context processing. The producer organises its main changes around DeepSeek Sparse Attention, a scaled reinforcement-learning framework and a pipeline for training agentic tasks. It accepts written questions and context and produces text, including an answer, code or tool-call content. It is distinct from the earlier V3.2 Exp release and the separate Speciale reasoning variant.
DeepSeek Sparse Attention adds an indexer and fine-grained token selection to the latent-attention architecture. For a long sequence, the indexer selects the earlier token information used by the main attention calculation. The report says this is the architectural change from V3.1 Terminus and uses the same architecture as V3.2 Exp. Its published context is 128K tokens, which supports substantial source material and a continuing task, while generated reasoning and tool history also consume the available context.
Post-training covers both thinking and non-thinking responses across mathematics, programming, logical reasoning, general agent tasks, agentic coding and search. Domain specialists supply data that is consolidated into the final model, followed by mixed reinforcement learning. For an ordinary question, a direct answer may be appropriate; for a difficult problem, the thinking workflow can develop a solution before presenting the final result. Specify the required answer or artifact independently of the intermediate reasoning.
A substantial edition change is thinking with tools. The updated chat format allows reasoning to continue through a tool-calling task rather than treating analysis and action as unrelated stages. A model-selected operation is executed by the application, which returns its result for the next step. The producer’s tool-context rule retains historical reasoning while only tool-related messages are added; when a new user message arrives, it discards that reasoning while preserving tool calls and results. Correctly distinguished conversation roles matter to that behaviour.
For coding, provide the source fragment or relevant files, the observed problem, the expected result and the conditions the change must preserve. An illustrative brief is: “Use the supplied code and tool results to locate the cause, propose the smallest correction and return the final patch with its supporting validation result.” For a search task, describe the question, required evidence and what counts as a completed answer. Search and execution tools are supplied by the surrounding application rather than by the text alone.
The producer recommends temperature 1.0 and top-p 0.95 for local use. V3.2 introduces a revised tool-call representation and a dedicated message encoder. Its developer role is reserved for the producer’s search-agent template, and the official API does not accept that role. The reference parser expects well-formed output and does not repair malformed results; when an application uses generated fields or tool arguments, it should check their structure before acting on them.
The technical report identifies long reasoning trajectories and redundant self-verification as practical limitations. An agent task can reach the 128K boundary even when the original source material fits, because tool outputs and repeated reasoning accumulate. Keep a concise record of relevant observations and make the final artifact explicit. For knowledge-sensitive work, attach the authoritative material or provide a suitable search tool rather than expecting training knowledge to define every fact.
DeepSeek V3.2 Speciale is a separate high-compute deep-reasoning variant and does not support tool calling in the model card. Its features should not be used as the standard V3.2 tool profile. Likewise, the later V4 generation and the experimental predecessor have separate identities and configuration choices. Retain the named V3.2 edition when comparing tasks whose model and prompting format matter.
For a practical source-based analysis, label the materials and ask for a conclusion supported by the relevant passages, with assumptions and missing information kept distinct. For an agent, state the allowed functions, the expected final result and the completion criterion. This prepares the model’s reasoning-and-tool workflow around a reviewable text, code or analytical artifact tied to the supplied evidence.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits deepseek/deepseek-v3.2 | 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 $0.0016 / 1K tokens Input · Cache read $0.00028 / 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 $0.0016 / 1K tokens Input · Cache read $0.00028 / 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", "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',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',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
Use the method paths shown above.
deepseek/deepseek-v3.2DeepSeek V3.2 combines text reasoning with tool use and long-context analysis. Its sparse-attention architecture and updated conversation format support coding, search and multi-step agent tasks, with a 128K context and separate thinking and direct-response workflows.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
DeepSeek V3.2 is DeepSeek’s text model for reasoning and agent work with an emphasis on efficient long-context processing. The producer organises its main changes around DeepSeek Sparse Attention, a scaled reinforcement-learning framework and a pipeline for training agentic tasks. It accepts written questions and context and produces text, including an answer, code or tool-call content. It is distinct from the earlier V3.2 Exp release and the separate Speciale reasoning variant.
DeepSeek Sparse Attention adds an indexer and fine-grained token selection to the latent-attention architecture. For a long sequence, the indexer selects the earlier token information used by the main attention calculation. The report says this is the architectural change from V3.1 Terminus and uses the same architecture as V3.2 Exp. Its published context is 128K tokens, which supports substantial source material and a continuing task, while generated reasoning and tool history also consume the available context.
Post-training covers both thinking and non-thinking responses across mathematics, programming, logical reasoning, general agent tasks, agentic coding and search. Domain specialists supply data that is consolidated into the final model, followed by mixed reinforcement learning. For an ordinary question, a direct answer may be appropriate; for a difficult problem, the thinking workflow can develop a solution before presenting the final result. Specify the required answer or artifact independently of the intermediate reasoning.
A substantial edition change is thinking with tools. The updated chat format allows reasoning to continue through a tool-calling task rather than treating analysis and action as unrelated stages. A model-selected operation is executed by the application, which returns its result for the next step. The producer’s tool-context rule retains historical reasoning while only tool-related messages are added; when a new user message arrives, it discards that reasoning while preserving tool calls and results. Correctly distinguished conversation roles matter to that behaviour.
For coding, provide the source fragment or relevant files, the observed problem, the expected result and the conditions the change must preserve. An illustrative brief is: “Use the supplied code and tool results to locate the cause, propose the smallest correction and return the final patch with its supporting validation result.” For a search task, describe the question, required evidence and what counts as a completed answer. Search and execution tools are supplied by the surrounding application rather than by the text alone.
The producer recommends temperature 1.0 and top-p 0.95 for local use. V3.2 introduces a revised tool-call representation and a dedicated message encoder. Its developer role is reserved for the producer’s search-agent template, and the official API does not accept that role. The reference parser expects well-formed output and does not repair malformed results; when an application uses generated fields or tool arguments, it should check their structure before acting on them.
The technical report identifies long reasoning trajectories and redundant self-verification as practical limitations. An agent task can reach the 128K boundary even when the original source material fits, because tool outputs and repeated reasoning accumulate. Keep a concise record of relevant observations and make the final artifact explicit. For knowledge-sensitive work, attach the authoritative material or provide a suitable search tool rather than expecting training knowledge to define every fact.
DeepSeek V3.2 Speciale is a separate high-compute deep-reasoning variant and does not support tool calling in the model card. Its features should not be used as the standard V3.2 tool profile. Likewise, the later V4 generation and the experimental predecessor have separate identities and configuration choices. Retain the named V3.2 edition when comparing tasks whose model and prompting format matter.
For a practical source-based analysis, label the materials and ask for a conclusion supported by the relevant passages, with assumptions and missing information kept distinct. For an agent, state the allowed functions, the expected final result and the completion criterion. This prepares the model’s reasoning-and-tool workflow around a reviewable text, code or analytical artifact tied to the supplied evidence.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits deepseek/deepseek-v3.2 | 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 $0.0016 / 1K tokens Input · Cache read $0.00028 / 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 $0.0016 / 1K tokens Input · Cache read $0.00028 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
{\n "model": "deepseek/deepseek-v3.2",\n "max_tokens": 128,\n "messages": [\n {\n "role": "user",\n "content": "Summarize the incident report."\n }\n ],\n "stream": false\n}set -eu\n: "${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', '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', '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
Use the method paths shown above.
Technical example in English
Repair an overwritten grouped total\n\nReview this Python fragment: totals = {}\nfor row in rows:\n totals[row['team']] = row['hours']\n For rows with teams A, B, A and hours 2, 3, 4, the last A overwrites the first. Return a minimal accumulation fix, explain the state changes and propose assertions for repeated teams and empty input.Corrected grouping code, a short state trace and two assertions with concrete inputs.Repeated teams accumulate all hours while an empty list returns an empty mapping.