dots3-note Preview is Dots Studio’s lightweight member of the dots3 multimodal family. The producer lists a mixture-of-experts model with 280B total parameters, 16B active parameters and support for up to 512K tokens of context. It takes text, images, video and audio and produces text, with general knowledge, instructions, reasoning, coding and agent-oriented work among its stated tasks.
The language backbone has one dense layer and 45 expert layers, with eight routed experts selected from 256 alongside a shared expert. Its attention combines 13 sparse-attention layers and 33 sliding-window layers. The producer lists separate vision and audio encoders and a shared prediction layer. These components support multimodal understanding; the model’s output remains text rather than generated images, speech or video.
For visual analysis, supply the image, page or chart and ask about the feature that affects the decision. A useful document task can compare a visible table with a written requirement and return the relevant row, the interpretation and any missing information. For video, identify the event or sequence to examine; the producer’s input example includes the audio track when the clip has one.
For audio, state whether the task needs a transcript or an explanation of the material, and provide any names or domain terms needed for interpretation. A combined assignment can use the recording together with written background and ask for findings organized by the question they answer. The result is a textual artifact for the application to present or process.
The producer’s conversation template distinguishes thinking from a direct response. Enabling thinking adds a reasoning stage; disabling it returns the answer directly. The documented sample uses temperature 1.0 and top-p 0.95. Choose the task and desired result explicitly, and leave enough generation room for the answer rather than treating a sample token allocation as the model’s hard output limit.
For a coding task, provide the relevant source, the expected behavior and an observed failure or requested change. Ask for the proposed artifact and the checks that would assess it. In a connected agent workflow, the model proposes tool interactions and the application executes them and returns results. The maker publishes a dedicated tool-call parser; the surrounding application supplies the actual tools and their access.
Interactive work can require exploration, adaptation and updates to the task’s working memory. Keep a compact record of the objective, confirmed findings and unresolved condition. Ask the next turn to identify what new evidence changes and what should remain in that record. Persistence of the record and execution of actions belong to the application around the model.
The producer publishes distinct BF16 and FP8 artifacts and local serving recipes with different configured context lengths. A configured serving length is separate from the 512K model specification. Preserve the Preview edition and precision distinctions when identifying the model, and request a final result that states the finding, the proposed change and the evidence the workflow actually gathered.
", "aboutText": "dots3-note Preview is Dots Studio’s lightweight member of the dots3 multimodal family. The producer lists a mixture-of-experts model with 280B total parameters, 16B active parameters and support for up to 512K tokens of context. It takes text, images, video and audio and produces text, with general knowledge, instructions, reasoning, coding and agent-oriented work among its stated tasks.\n\nThe language backbone has one dense layer and 45 expert layers, with eight routed experts selected from 256 alongside a shared expert. Its attention combines 13 sparse-attention layers and 33 sliding-window layers. The producer lists separate vision and audio encoders and a shared prediction layer. These components support multimodal understanding; the model’s output remains text rather than generated images, speech or video.\n\nFor visual analysis, supply the image, page or chart and ask about the feature that affects the decision. A useful document task can compare a visible table with a written requirement and return the relevant row, the interpretation and any missing information. For video, identify the event or sequence to examine; the producer’s input example includes the audio track when the clip has one.\n\nFor audio, state whether the task needs a transcript or an explanation of the material, and provide any names or domain terms needed for interpretation. A combined assignment can use the recording together with written background and ask for findings organized by the question they answer. The result is a textual artifact for the application to present or process.\n\nThe producer’s conversation template distinguishes thinking from a direct response. Enabling thinking adds a reasoning stage; disabling it returns the answer directly. The documented sample uses temperature 1.0 and top-p 0.95. Choose the task and desired result explicitly, and leave enough generation room for the answer rather than treating a sample token allocation as the model’s hard output limit.\n\nFor a coding task, provide the relevant source, the expected behavior and an observed failure or requested change. Ask for the proposed artifact and the checks that would assess it. In a connected agent workflow, the model proposes tool interactions and the application executes them and returns results. The maker publishes a dedicated tool-call parser; the surrounding application supplies the actual tools and their access.\n\nInteractive work can require exploration, adaptation and updates to the task’s working memory. Keep a compact record of the objective, confirmed findings and unresolved condition. Ask the next turn to identify what new evidence changes and what should remain in that record. Persistence of the record and execution of actions belong to the application around the model.\n\nThe producer publishes distinct BF16 and FP8 artifacts and local serving recipes with different configured context lengths. A configured serving length is separate from the 512K model specification. Preserve the Preview edition and precision distinctions when identifying the model, and request a final result that states the finding, the proposed change and the evidence the workflow actually gathered.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "8a1d5c5a5fb6983826e281b8e6f561a74c450250c61cc91432cd2dadf6a3434b", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "en", "methods": [ { "html": "dots-studio/dots-3-note-preview:freeDots Studio’s dots3-note Preview is a multimodal MoE model for reasoning, coding and agent workflows. The producer lists 280B total and 16B active parameters, a 512K context, text/image/video/audio input and text output.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
dots3-note Preview is Dots Studio’s lightweight member of the dots3 multimodal family. The producer lists a mixture-of-experts model with 280B total parameters, 16B active parameters and support for up to 512K tokens of context. It takes text, images, video and audio and produces text, with general knowledge, instructions, reasoning, coding and agent-oriented work among its stated tasks.
The language backbone has one dense layer and 45 expert layers, with eight routed experts selected from 256 alongside a shared expert. Its attention combines 13 sparse-attention layers and 33 sliding-window layers. The producer lists separate vision and audio encoders and a shared prediction layer. These components support multimodal understanding; the model’s output remains text rather than generated images, speech or video.
For visual analysis, supply the image, page or chart and ask about the feature that affects the decision. A useful document task can compare a visible table with a written requirement and return the relevant row, the interpretation and any missing information. For video, identify the event or sequence to examine; the producer’s input example includes the audio track when the clip has one.
For audio, state whether the task needs a transcript or an explanation of the material, and provide any names or domain terms needed for interpretation. A combined assignment can use the recording together with written background and ask for findings organized by the question they answer. The result is a textual artifact for the application to present or process.
The producer’s conversation template distinguishes thinking from a direct response. Enabling thinking adds a reasoning stage; disabling it returns the answer directly. The documented sample uses temperature 1.0 and top-p 0.95. Choose the task and desired result explicitly, and leave enough generation room for the answer rather than treating a sample token allocation as the model’s hard output limit.
For a coding task, provide the relevant source, the expected behavior and an observed failure or requested change. Ask for the proposed artifact and the checks that would assess it. In a connected agent workflow, the model proposes tool interactions and the application executes them and returns results. The maker publishes a dedicated tool-call parser; the surrounding application supplies the actual tools and their access.
Interactive work can require exploration, adaptation and updates to the task’s working memory. Keep a compact record of the objective, confirmed findings and unresolved condition. Ask the next turn to identify what new evidence changes and what should remain in that record. Persistence of the record and execution of actions belong to the application around the model.
The producer publishes distinct BF16 and FP8 artifacts and local serving recipes with different configured context lengths. A configured serving length is separate from the 512K model specification. Preserve the Preview edition and precision distinctions when identifying the model, and request a final result that states the finding, the proposed change and the evidence the workflow actually gathered.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits dots-studio/dots-3-note-preview:free | Default— |
Fieldmessages | Typearray | RequirementRequired | Values & limits | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits false | Defaultfalse |
Fieldmax_tokens | Typeinteger | RequirementOptional | Values & limits Minimum: 1 Maximum: 460,800 | Default— |
Fieldmax_completion_tokens | Typeinteger | RequirementOptional | Values & limits Minimum: 1 Maximum: 460,800 | Default— |
Fieldn | Typeinteger | RequirementOptional | Values & limits Minimum: 1 Maximum: 128 | Default— |
Fieldtemperature | Typenumber | RequirementOptional | Values & limits | Default— |
Fieldtop_p | Typenumber | RequirementOptional | Values & limits | Default— |
Fieldseed | Typeinteger | RequirementOptional | Values & limits | Default— |
Fieldstop | Type | RequirementOptional | Values & limits | Default— |
Fieldfrequency_penalty | Typenumber | RequirementOptional | Values & limits | Default— |
Fieldpresence_penalty | Typenumber | RequirementOptional | Values & limits | Default— |
Fielduser | Typestring | RequirementOptional | Values & limits | Default— |
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, messages |
Parameteroptional | Contractstream, max_tokens, max_completion_tokens, n, temperature, top_p, seed, stop, frequency_penalty, presence_penalty, user |
Parametermaterial_types | Contracttext |
Parameterconditions | ContractUse literal text messages only. Cache controls, tools, image/audio/video content and streamed responses are unavailable for this method., The complete encoded JSON request must not exceed 512000 bytes. |
Lifecycle: synchronous_json
Tariff references do not add request options. Supported parameters are listed for the selected method.
| Configuration | Rate schedule | Charge |
|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Charge Input · Input tokens $0 / 1K tokens Output · Output tokens $0 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\nexport AILOOT_API_KEY\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "dots-studio/dots-3-note-preview:free", "messages": [{"role": "user", "content": "Summarize the supplied text in three concise points."}], "stream": false}')\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\n# Submit once. Retain the complete result, original identifiers and every response header.\nJSON
Keep every complete returned response, file and content part.
Retain the complete response, all actual results, original identifiers, usage and response headers.
Preserve the original response, headers and X-Client-Request-ID. An uncertain outcome does not authorize another creation.
Ailoot Ultra dots-studio/dots-3-note-preview:free
Use the method paths shown above.
dots-studio/dots-3-note-preview:freeDots Studio’s dots3-note Preview is a multimodal MoE model for reasoning, coding and agent workflows. The producer lists 280B total and 16B active parameters, a 512K context, text/image/video/audio input and text output.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
dots3-note Preview is Dots Studio’s lightweight member of the dots3 multimodal family. The producer lists a mixture-of-experts model with 280B total parameters, 16B active parameters and support for up to 512K tokens of context. It takes text, images, video and audio and produces text, with general knowledge, instructions, reasoning, coding and agent-oriented work among its stated tasks.
The language backbone has one dense layer and 45 expert layers, with eight routed experts selected from 256 alongside a shared expert. Its attention combines 13 sparse-attention layers and 33 sliding-window layers. The producer lists separate vision and audio encoders and a shared prediction layer. These components support multimodal understanding; the model’s output remains text rather than generated images, speech or video.
For visual analysis, supply the image, page or chart and ask about the feature that affects the decision. A useful document task can compare a visible table with a written requirement and return the relevant row, the interpretation and any missing information. For video, identify the event or sequence to examine; the producer’s input example includes the audio track when the clip has one.
For audio, state whether the task needs a transcript or an explanation of the material, and provide any names or domain terms needed for interpretation. A combined assignment can use the recording together with written background and ask for findings organized by the question they answer. The result is a textual artifact for the application to present or process.
The producer’s conversation template distinguishes thinking from a direct response. Enabling thinking adds a reasoning stage; disabling it returns the answer directly. The documented sample uses temperature 1.0 and top-p 0.95. Choose the task and desired result explicitly, and leave enough generation room for the answer rather than treating a sample token allocation as the model’s hard output limit.
For a coding task, provide the relevant source, the expected behavior and an observed failure or requested change. Ask for the proposed artifact and the checks that would assess it. In a connected agent workflow, the model proposes tool interactions and the application executes them and returns results. The maker publishes a dedicated tool-call parser; the surrounding application supplies the actual tools and their access.
Interactive work can require exploration, adaptation and updates to the task’s working memory. Keep a compact record of the objective, confirmed findings and unresolved condition. Ask the next turn to identify what new evidence changes and what should remain in that record. Persistence of the record and execution of actions belong to the application around the model.
The producer publishes distinct BF16 and FP8 artifacts and local serving recipes with different configured context lengths. A configured serving length is separate from the 512K model specification. Preserve the Preview edition and precision distinctions when identifying the model, and request a final result that states the finding, the proposed change and the evidence the workflow actually gathered.
POST /v1/messagestexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits dots-studio/dots-3-note-preview:free | Default— |
Fieldmessages | Typearray | RequirementRequired | Values & limits | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits false | Defaultfalse |
Fieldsystem | Typestring | RequirementOptional | Values & limits | Default— |
Fieldmax_tokens | Typeinteger | RequirementRequired | Values & limits Minimum: 1 Maximum: 460,800 | Default— |
Fieldtemperature | Typenumber | RequirementOptional | Values & limits | Default— |
Fieldtop_p | Typenumber | RequirementOptional | Values & limits | Default— |
Fieldtop_k | Typeinteger | RequirementOptional | Values & limits | Default— |
Fieldstop_sequences | Typearray | RequirementOptional | Values & limits | Default— |
Fieldmetadata | Typeobject | RequirementOptional | Values & limits | Default— |
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, messages, max_tokens |
Parameteroptional | Contractstream, system, temperature, top_p, top_k, stop_sequences, metadata |
Parametermaterial_types | Contracttext |
Parameterconditions | ContractUse literal text messages only. Cache controls, tools, image/audio/video content and streamed responses are unavailable for this method., The complete encoded JSON request must not exceed 512000 bytes. |
Lifecycle: synchronous_json
Tariff references do not add request options. Supported parameters are listed for the selected method.
| Configuration | Rate schedule | Charge |
|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Charge Input · Input tokens $0 / 1K tokens Output · Output tokens $0 / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\nexport AILOOT_API_KEY\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "dots-studio/dots-3-note-preview:free", "messages": [{"role": "user", "content": "Summarize the supplied text in three concise points."}], "stream": false, "max_tokens": 256}')\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/messages \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o result.json\n# Submit once. Retain the complete result, original identifiers and every response header.\nJSON
Keep every complete returned response, file and content part.
Retain the complete response, all actual results, original identifiers, usage and response headers.
Preserve the original response, headers and X-Client-Request-ID. An uncertain outcome does not authorize another creation.
Ailoot Ultra dots-studio/dots-3-note-preview:free
Use the method paths shown above.