Source: https://ailoot.ai/models/en/unbiased-pareto-26-10/ Canonical: https://ailoot.ai/models/en/unbiased-pareto-26-10/ Language: en # Pareto 26.10 [Markdown](https://ailoot.ai/models/en/unbiased-pareto-26-10/index.md) · [JSON](https://ailoot.ai/models/en/unbiased-pareto-26-10/facts.json) ## [Pareto 26.10](https://ailoot.ai/models/en/unbiased-pareto-26-10/) `unbiased/pareto-26.10` UnbiasedTextInput $0.0016 / 1KOutput $0.0064 / 1K Unbiased’s composite text model with a 1,048,576-token context for analyzing large collections of material and preparing detailed responses. ### How Pareto 26.10 works Pareto 26.10 is a stable release of Unbiased’s composite model. It uses an approach in which several frontier and open models are selected for each request and their results are combined into one response. This differs from simply forwarding a question to a single selected model: the user interacts with one Pareto model and receives a combined result. The participating models can change, so the release name does not define a fixed set of underlying models. ### Working with text in Ailoot API In Ailoot API, Pareto 26.10 accepts text and returns text. This connection has a context window of 1,048,576 tokens, containing instructions, conversation history, supplied material and the response being generated. The model’s metadata lists output of up to 131,072 tokens; it is an upper boundary rather than the required length of every generation. For substantive work, define the result’s length and section order in advance. The long context supports tasks that compare several text sources in one request: a technical brief and its implementation, requirements and meeting minutes, or several versions of a document. Provide source passages with clear names and ask the model to tie conclusions to them. For example, it can assemble a differences table, identify contradictory requirements or prepare a change plan based on the whole collection. These are ways to frame a text task; your application determines access to files or external systems. ### How to build a practical request For code analysis, include the relevant functions, expected behavior and observed error. Ask first for an explanation of their relationship, then for a localized change and verification methods. For research based on supplied texts, state the question, list the sources within the request and specify which claims should reference the relevant passage. For document preparation, define the audience, purpose, mandatory sections and acceptance criteria. The text connection is available through Messages, Responses, Chat Completions and the text generateContent interface. Choosing a format helps integrate the model into an existing client. It does not itself add image understanding, web search or command execution. Generated code, plans and recommendations remain text output until your application takes further action. ### A version for explicit selection The identifier `unbiased/pareto-26.10` selects release 26.10 specifically. This is useful when you need to pin the model explicitly in application settings or compare one version’s results on your own task set. Release 26.9 exists separately and has its own context window. ### Available through Ailoot API Input types `text` Output types `text` Context Not specified in the catalogue Max. output Not specified in the catalogue Input $0.0016 */ 1K* Output $0.0064 */ 1K* Cache read ≈$0.00006 */ 1K* Input types `text` Output types `text` Context Not specified in the catalogue Max. output Not specified in the catalogue Input $0.0016 */ 1K* Output $0.0064 */ 1K* Cache read ≈$0.00006 */ 1K* Input types `text` Output types `text` Context Not specified in the catalogue Max. output Not specified in the catalogue Input $0.0016 */ 1K* Output $0.0064 */ 1K* Cache read ≈$0.00006 */ 1K* Input types `text` Output types `text` Context Not specified in the catalogue Max. output Not specified in the catalogue Input $0.0016 */ 1K* Output $0.0064 */ 1K* Cache read ≈$0.00006 */ 1K* `unbiased/pareto-26.10` · JSON ``` {"about":"### How Pareto 26.10 works\n\nPareto 26.10 is a stable release of Unbiased’s composite model. It uses an approach in which several frontier and open models are selected for each request and their results are combined into one response. This differs from simply forwarding a question to a single selected model: the user interacts with one Pareto model and receives a combined result. The participating models can change, so the release name does not define a fixed set of underlying models.\n\n### Working with text in Ailoot API\n\nIn Ailoot API, Pareto 26.10 accepts text and returns text. This connection has a context window of 1,048,576 tokens, containing instructions, conversation history, supplied material and the response being generated. The model’s metadata lists output of up to 131,072 tokens; it is an upper boundary rather than the required length of every generation. For substantive work, define the result’s length and section order in advance.\n\nThe long context supports tasks that compare several text sources in one request: a technical brief and its implementation, requirements and meeting minutes, or several versions of a document. Provide source passages with clear names and ask the model to tie conclusions to them. For example, it can assemble a differences table, identify contradictory requirements or prepare a change plan based on the whole collection. These are ways to frame a text task; your application determines access to files or external systems.\n\n### How to build a practical request\n\nFor code analysis, include the relevant functions, expected behavior and observed error. Ask first for an explanation of their relationship, then for a localized change and verification methods. For research based on supplied texts, state the question, list the sources within the request and specify which claims should reference the relevant passage. For document preparation, define the audience, purpose, mandatory sections and acceptance criteria.\n\nThe text connection is available through Messages, Responses, Chat Completions and the text generateContent interface. Choosing a format helps integrate the model into an existing client. It does not itself add image understanding, web search or command execution. Generated code, plans and recommendations remain text output until your application takes further action.\n\n### A version for explicit selection\n\nThe identifier `unbiased/pareto-26.10` selects release 26.10 specifically. This is useful when you need to pin the model explicitly in application settings or compare one version’s results on your own task set. Release 26.9 exists separately and has its own context window.","alias_of":null,"aliases":[],"author":"Unbiased","canonical_model":"unbiased/pareto-26.10","ctx":null,"formats":["chat","gemini","messages","responses"],"id":"unbiased/pareto-26.10","input":["text"],"kind":"text","max_out":null,"methods":[{"cache_read":0.06,"cache_write":null,"cache_write_1h":null,"cache_write_5m":null,"conditional_meter":null,"ctx":null,"family":"anthropic","fast_multiplier":1,"flex_multiplier":1,"fmt":"messages","http":"POST","img_in":null,"img_out":null,"in":1.6,"input":["text"],"label":"Anthropic Messages","max_out":null,"max_tool_calls":null,"out":6.4,"output":["text"],"path":"/v1/messages","request_model_id":"unbiased/pareto-26.10","route_label":"Ultra","status_reads":[],"tiers":[],"tool_prices":null,"unit":"token","units":[]},{"cache_read":0.06,"cache_write":null,"cache_write_1h":null,"cache_write_5m":null,"conditional_meter":null,"ctx":null,"family":"openai","fast_multiplier":1,"flex_multiplier":1,"fmt":"responses","http":"POST","img_in":null,"img_out":null,"in":1.6,"input":["text"],"label":"OpenAI Responses","max_out":null,"max_tool_calls":null,"out":6.4,"output":["text"],"path":"/v1/responses","request_model_id":"unbiased/pareto-26.10","route_label":"Ultra","status_reads":[],"tiers":[],"tool_prices":null,"unit":"token","units":[]},{"cache_read":0.06,"cache_write":null,"cache_write_1h":null,"cache_write_5m":null,"conditional_meter":null,"ctx":null,"family":"openai","fast_multiplier":1,"flex_multiplier":1,"fmt":"chat","http":"POST","img_in":null,"img_out":null,"in":1.6,"input":["text"],"label":"OpenAI Chat","max_out":null,"max_tool_calls":null,"out":6.4,"output":["text"],"path":"/v1/chat/completions","request_model_id":"unbiased/pareto-26.10","route_label":"Ultra","status_reads":[],"tiers":[],"tool_prices":null,"unit":"token","units":[]},{"cache_read":0.06,"cache_write":null,"cache_write_1h":null,"cache_write_5m":null,"conditional_meter":null,"ctx":null,"family":"gemini","fast_multiplier":1,"flex_multiplier":1,"fmt":"gemini","http":"POST","img_in":null,"img_out":null,"in":1.6,"input":["text"],"label":"Gemini generateContent","max_out":null,"max_tool_calls":null,"out":6.4,"output":["text"],"path":"/v1beta/models/unbiased/pareto-26.10:generateContent","request_model_id":"unbiased/pareto-26.10","route_label":"Ultra","status_reads":[],"tiers":[],"tool_prices":null,"unit":"token","units":[]}],"model_aliases":[{"canonical_model_id":"unbiased/pareto-26.10","request_model_id":"unbiased/pareto-26.10","route_label":"Ultra"}],"name":"Pareto 26.10","price":{"in":1.6,"in_label":null,"out":6.4,"out_label":null,"sort_in":1.6,"sort_out":6.4,"varies":false},"public_id":"unbiased/pareto-26.10","short":"Unbiased’s composite text model with a 1,048,576-token context for analyzing large collections of material and preparing detailed responses."} ``` ## Questions How do I choose a model for my task? First identify the required input and output: text, image, audio or video. Then compare compatible API methods, the exact model ID or current alias target, price and billing unit. GET /v1/models is free and lists models available to your key. Does each model support every API method and media type? Use the API methods and input/output types listed on the model card. Support for text, images, audio or video depends on the selected model and method; check their request example before sending your data. What is the difference between a model ID and an alias? An exact model ID names a specific model. An alias points to a model and its target can change. Check the current alias target in the catalogue; use the exact ID when your integration needs a specific model. How do I compare prices before sending a request? Check the model card for the price and billing unit of the selected API method. Compare the same unit and input/output type; request history shows actual usage and the charged amount. What does a subscription give me? A discount on token purchases for regular use. Choose a plan for your request volume; subscription terms and pricing are shown at checkout. How do I apply a promo code for up to 50% off? Enter the promo code when buying tokens. The total updates according to the promotion terms; you can see the discount before paying. ## Ailoot · The company and its project portfolio Ailoot combines established operating experience, a large client base, stable service and ongoing KPI control. The company and its project portfolio: millions of customers; monthly sales in the billions. Ailoot brings that operating experience to API access for AI features in customer products, agents, applications and automations.