Ling 3.0 Flash is a text-to-text model from inclusionAI. Its published architecture is a mixture of experts with 124 billion total parameters and approximately 5.1 billion active parameters per token. Select inclusionai/ling-3.0-flash through POST /v1/chat/completions with model and a messages array. Instructions, source material and prior conversation turns belong in the message text.
Use a focused prompt to turn supplied material into a specific deliverable. For a customer reply, provide the customer’s question and the product or policy text the answer must use, then specify the tone and next step. For a document summary, name the sections you need, such as decisions, unresolved questions and actions. For extraction, define the requested fields and how to represent facts absent from the source. For code work, include the relevant function or file excerpt and ask for an explanation, proposed change or review against an explicit requirement.
The published context window is 262,144 tokens, with up to 32,768 completion tokens listed for the model. Budget the source text, instructions, conversation history and requested completion together. Use max_tokens to set the requested output allowance. A document should be supplied as its actual text when you want the answer to depend on its contents; refer to the relevant sections explicitly when a request combines several passages.
The supported parameter list includes tools and tool_choice for function calling. Define the available functions and their argument descriptions in the request. Your application validates a returned call, executes the selected function and supplies its result to the conversation before requesting a follow-up answer. For example, a support workflow can expose a narrowly defined order lookup and use its returned facts when drafting the response. Keep tool results attached to the correct conversation turn so the model can distinguish them from the user’s original request.
The model supports response_format for JSON output, without JSON-schema enforcement. State the intended fields and validate the returned object in your application before using it. Other published controls include temperature, top_p, top_k, min_p, stop, seed and the listed repetition, frequency and presence penalties. The parameter list also includes reasoning and include_reasoning; choose their supported representation from the request specification rather than assuming values from a different edition.
Chat Completions can return a complete response or streamed text. For a complete reply, read the returned message content and retain the response choices and usage. With streaming, assemble the deltas before treating the answer as complete, and retain any returned tool calls with their arguments. The result is text or a function-call request for the next application step; the surrounding application owns execution and the use of the completed answer.
", "aboutText": "Ling 3.0 Flash is a text-to-text model from inclusionAI. Its published architecture is a mixture of experts with 124 billion total parameters and approximately 5.1 billion active parameters per token. Select inclusionai/ling-3.0-flash through POST /v1/chat/completions with model and a messages array. Instructions, source material and prior conversation turns belong in the message text.\n\nUse a focused prompt to turn supplied material into a specific deliverable. For a customer reply, provide the customer’s question and the product or policy text the answer must use, then specify the tone and next step. For a document summary, name the sections you need, such as decisions, unresolved questions and actions. For extraction, define the requested fields and how to represent facts absent from the source. For code work, include the relevant function or file excerpt and ask for an explanation, proposed change or review against an explicit requirement.\n\nThe published context window is 262,144 tokens, with up to 32,768 completion tokens listed for the model. Budget the source text, instructions, conversation history and requested completion together. Use max_tokens to set the requested output allowance. A document should be supplied as its actual text when you want the answer to depend on its contents; refer to the relevant sections explicitly when a request combines several passages.\n\nThe supported parameter list includes tools and tool_choice for function calling. Define the available functions and their argument descriptions in the request. Your application validates a returned call, executes the selected function and supplies its result to the conversation before requesting a follow-up answer. For example, a support workflow can expose a narrowly defined order lookup and use its returned facts when drafting the response. Keep tool results attached to the correct conversation turn so the model can distinguish them from the user’s original request.\n\nThe model supports response_format for JSON output, without JSON-schema enforcement. State the intended fields and validate the returned object in your application before using it. Other published controls include temperature, top_p, top_k, min_p, stop, seed and the listed repetition, frequency and presence penalties. The parameter list also includes reasoning and include_reasoning; choose their supported representation from the request specification rather than assuming values from a different edition.\n\nChat Completions can return a complete response or streamed text. For a complete reply, read the returned message content and retain the response choices and usage. With streaming, assemble the deltas before treating the answer as complete, and retain any returned tool calls with their arguments. The result is text or a function-call request for the next application step; the surrounding application owns execution and the use of the completed answer.", "editorialOverlay": { "copy_fields": null, "copy_sha256": null, "copy_source": null, "html_sha256": "2b595e1f23f9bc7cc808456747b8d218761de4cc98f93ecb0a57ba4d21de897c", "module_sha256": "ee9dc1c50b1e6a8fb7fa062030fcb4646b9aaaeb08be513b723a5a92a7290f06", "raw_model_unchanged": true, "schema": "ailoot-native-model-editorial-v1" }, "locale": "en", "methods": [ { "html": "inclusionai/ling-3.0-flashLing 3.0 Flash is inclusionAI’s text model for conversations, document work and application-controlled agent workflows. It accepts text through Chat Completions, supports function-call declarations and JSON output formatting, and has a published 262,144-token context window. Use the exact inclusionai/ling-3.0-flash ID when selecting this edition.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Ling 3.0 Flash is a text-to-text model from inclusionAI. Its published architecture is a mixture of experts with 124 billion total parameters and approximately 5.1 billion active parameters per token. Select inclusionai/ling-3.0-flash through POST /v1/chat/completions with model and a messages array. Instructions, source material and prior conversation turns belong in the message text.
Use a focused prompt to turn supplied material into a specific deliverable. For a customer reply, provide the customer’s question and the product or policy text the answer must use, then specify the tone and next step. For a document summary, name the sections you need, such as decisions, unresolved questions and actions. For extraction, define the requested fields and how to represent facts absent from the source. For code work, include the relevant function or file excerpt and ask for an explanation, proposed change or review against an explicit requirement.
The published context window is 262,144 tokens, with up to 32,768 completion tokens listed for the model. Budget the source text, instructions, conversation history and requested completion together. Use max_tokens to set the requested output allowance. A document should be supplied as its actual text when you want the answer to depend on its contents; refer to the relevant sections explicitly when a request combines several passages.
The supported parameter list includes tools and tool_choice for function calling. Define the available functions and their argument descriptions in the request. Your application validates a returned call, executes the selected function and supplies its result to the conversation before requesting a follow-up answer. For example, a support workflow can expose a narrowly defined order lookup and use its returned facts when drafting the response. Keep tool results attached to the correct conversation turn so the model can distinguish them from the user’s original request.
The model supports response_format for JSON output, without JSON-schema enforcement. State the intended fields and validate the returned object in your application before using it. Other published controls include temperature, top_p, top_k, min_p, stop, seed and the listed repetition, frequency and presence penalties. The parameter list also includes reasoning and include_reasoning; choose their supported representation from the request specification rather than assuming values from a different edition.
Chat Completions can return a complete response or streamed text. For a complete reply, read the returned message content and retain the response choices and usage. With streaming, assemble the deltas before treating the answer as complete, and retain any returned tool calls with their arguments. The result is text or a function-call request for the next application step; the surrounding application owns execution and the use of the completed answer.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits inclusionai/ling-3.0-flash | Default— |
Fieldmessages | Typearray | RequirementRequired | Values & limits | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
Fieldmax_tokens | Typeinteger | RequirementOptional | Values & limits Minimum: 1 | Default— |
Fieldmax_completion_tokens | Typeinteger | RequirementOptional | Values & limits Minimum: 1 | Default— |
Fieldn | Typeinteger | RequirementOptional | Values & limits Minimum: 1 | 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 | Contract |
Lifecycle: synchronous_json_or_sse
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 $0.00012 / 1K tokens Output · Output tokens $0.00036 / 1K tokens Input · Cache read $0.000024 / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${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": "inclusionai/ling-3.0-flash", "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 or supported SSE
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 inclusionai/ling-3.0-flash
Use the method paths shown above.