Source: https://ailoot.ai/models/en/gpt-6-1-sol-ultrafast/ Canonical: https://ailoot.ai/models/en/gpt-6-1-sol-ultrafast/ Language: en # gpt-6.1-sol-ultrafast [Markdown](https://ailoot.ai/models/en/gpt-6-1-sol-ultrafast/index.md) · [JSON](https://ailoot.ai/models/en/gpt-6-1-sol-ultrafast/facts.json) ## [gpt-6.1-sol-ultrafast](https://ailoot.ai/models/en/gpt-6-1-sol-ultrafast/) `gpt-6.1-sol-ultrafast` OpenAITextInput $0.004 / 1KOutput $0.02 / 1K GPT-6.1 Sol Ultrafast for code analysis, complex text tasks and professional content preparation with a large context. ### Code and complex professional tasks GPT-6.1 Sol Ultrafast is a listing for analyzing code, technical requirements and professional material. OpenAI classifies the underlying GPT-6.1 Sol as a reasoning model and highlights complex programming and professional work as its main areas of focus. Such models use internal reasoning tokens when preparing a response; the visible result can be requested as an explanation, program code or a finished document. ### From the initial task to a solution Useful programming workflows include analyzing a function’s behavior, preparing a change from requirements and drawing up a refactoring plan. Provide the source code, interface constraints and example inputs, then request a specific result. For debugging, add the expected and actual behavior. The model produces text: proposed code, commands and checks can be used in your working environment. For professional tasks, Sol can form part of a workflow for analyzing extensive specifications and preparing material from several sources. For example, consolidate requirements into a single description, compare implementation options, explain a technical decision for another audience or prepare a draft document. The comparison criteria and source information establish the basis of the answer; specifying the format helps produce a result that is easy to verify and use further. ### A large text context OpenAI specifies a 1,050,000-token window for the underlying GPT-6.1 Sol. The Ailoot API listing provides an input limit of 922,000 tokens and an output limit of 128,000 tokens. These values let you plan the volume of source material and the result separately. Reasoning tokens also count toward the generation budget, so the maximum output does not promise a visible response of that length. For a large project, include file and section names alongside their contents. If a question concerns a particular component or document, identify its boundaries. This keeps the task tied to the relevant material and makes proposed changes easier to compare with the source data. ### Input and output in Ailoot API The listing accepts text and returns text. Chat Completions, Responses and Gemini-compatible generateContent are available. For documents and images, this workflow uses previously extracted text or a text description. Choose your client’s interface and specify the end result, mandatory constraints and response structure. ### Available through Ailoot API Input types `text` Output types `text` Context 922,000 *tokens* Max. output 128,000 *tokens* Input $0.004 */ 1K* Output $0.02 */ 1K* Cache read $0.0002 */ 1K* Cache write $0.005 */ 1K* Cache write, 5 min $0.005 */ 1K tokens* Cache write, 1 hour $0.005 */ 1K tokens* #### Prices by request length · OpenAI Chat | Request size, tokens | Input | Output | Cache read | Cache write | 5 min cache | 1 hour cache | | --- | --- | --- | --- | --- | --- | --- | | Request size, tokens(0; 272,000\] | Input$0.004 | Output$0.02 | Cache read$0.0002 | Cache write$0.005 | 5 min cache$0.005 | 1 hour cache$0.005 | | Request size, tokens(272,000; ∞) | Input$0.008 | Output$0.03 | Cache read$0.0004 | Cache write$0.01 | 5 min cache$0.01 | 1 hour cache$0.01 | Rates in USD per 1K tokens. If no rate is set, the base rate for this method is inherited; «—» means no base rate is set. Input types `text` Output types `text` Context 922,000 *tokens* Max. output 128,000 *tokens* Input $0.004 */ 1K* Output $0.02 */ 1K* Cache read $0.0002 */ 1K* Cache write $0.005 */ 1K* Cache write, 5 min $0.005 */ 1K tokens* Cache write, 1 hour $0.005 */ 1K tokens* #### Prices by request length · OpenAI Responses | Request size, tokens | Input | Output | Cache read | Cache write | 5 min cache | 1 hour cache | | --- | --- | --- | --- | --- | --- | --- | | Request size, tokens(0; 272,000\] | Input$0.004 | Output$0.02 | Cache read$0.0002 | Cache write$0.005 | 5 min cache$0.005 | 1 hour cache$0.005 | | Request size, tokens(272,000; ∞) | Input$0.008 | Output$0.03 | Cache read$0.0004 | Cache write$0.01 | 5 min cache$0.01 | 1 hour cache$0.01 | Rates in USD per 1K tokens. If no rate is set, the base rate for this method is inherited; «—» means no base rate is set. Input types `text` Output types `text` Context 922,000 *tokens* Max. output 128,000 *tokens* Input $0.004 */ 1K* Output $0.02 */ 1K* Cache read $0.0002 */ 1K* Cache write $0.005 */ 1K* Cache write, 5 min $0.005 */ 1K tokens* Cache write, 1 hour $0.005 */ 1K tokens* #### Prices by request length · Gemini generateContent | Request size, tokens | Input | Output | Cache read | Cache write | 5 min cache | 1 hour cache | | --- | --- | --- | --- | --- | --- | --- | | Request size, tokens(0; 272,000\] | Input$0.004 | Output$0.02 | Cache read$0.0002 | Cache write$0.005 | 5 min cache$0.005 | 1 hour cache$0.005 | | Request size, tokens(272,000; ∞) | Input$0.008 | Output$0.03 | Cache read$0.0004 | Cache write$0.01 | 5 min cache$0.01 | 1 hour cache$0.01 | Rates in USD per 1K tokens. If no rate is set, the base rate for this method is inherited; «—» means no base rate is set. `gpt-6.1-sol-ultrafast` · JSON ``` {"about":"### Code and complex professional tasks\n\nGPT-6.1 Sol Ultrafast is a listing for analyzing code, technical requirements and professional material. OpenAI classifies the underlying GPT-6.1 Sol as a reasoning model and highlights complex programming and professional work as its main areas of focus. Such models use internal reasoning tokens when preparing a response; the visible result can be requested as an explanation, program code or a finished document.\n\n### From the initial task to a solution\n\nUseful programming workflows include analyzing a function’s behavior, preparing a change from requirements and drawing up a refactoring plan. Provide the source code, interface constraints and example inputs, then request a specific result. For debugging, add the expected and actual behavior. The model produces text: proposed code, commands and checks can be used in your working environment.\n\nFor professional tasks, Sol can form part of a workflow for analyzing extensive specifications and preparing material from several sources. For example, consolidate requirements into a single description, compare implementation options, explain a technical decision for another audience or prepare a draft document. The comparison criteria and source information establish the basis of the answer; specifying the format helps produce a result that is easy to verify and use further.\n\n### A large text context\n\nOpenAI specifies a 1,050,000-token window for the underlying GPT-6.1 Sol. The Ailoot API listing provides an input limit of 922,000 tokens and an output limit of 128,000 tokens. These values let you plan the volume of source material and the result separately. Reasoning tokens also count toward the generation budget, so the maximum output does not promise a visible response of that length.\n\nFor a large project, include file and section names alongside their contents. If a question concerns a particular component or document, identify its boundaries. This keeps the task tied to the relevant material and makes proposed changes easier to compare with the source data.\n\n### Input and output in Ailoot API\n\nThe listing accepts text and returns text. Chat Completions, Responses and Gemini-compatible generateContent are available. For documents and images, this workflow uses previously extracted text or a text description. Choose your client’s interface and specify the end result, mandatory constraints and response structure.","alias_of":null,"aliases":[],"author":"OpenAI","canonical_model":"gpt-6.1-sol-ultrafast","ctx":922000,"formats":["chat","gemini","responses"],"id":"gpt-6.1-sol-ultrafast","input":["text"],"kind":"text","max_out":128000,"methods":[{"cache_read":0.2,"cache_write":5.0,"cache_write_1h":5.0,"cache_write_5m":5.0,"conditional_meter":null,"ctx":922000,"family":"openai","fast_multiplier":1,"flex_multiplier":1,"fmt":"chat","http":"POST","img_in":null,"img_out":null,"in":4.0,"input":["text"],"label":"OpenAI Chat","max_out":128000,"max_tool_calls":null,"out":20.0,"output":["text"],"path":"/v1/chat/completions","request_model_id":"gpt-6.1-sol-ultrafast","route_label":"Ultra","status_reads":[],"tiers":[{"cache_read":0.2,"cache_write":5.0,"cache_write_1h":5.0,"cache_write_5m":5.0,"in":4.0,"interval_boundary":"min_exclusive_max_inclusive","label":"","max":272000,"min":0,"null_rate_policy":"inherit_base_rate","out":20.0},{"cache_read":0.4,"cache_write":10.0,"cache_write_1h":10.0,"cache_write_5m":10.0,"in":8.0,"interval_boundary":"min_exclusive_max_inclusive","label":"","max":null,"min":272000,"null_rate_policy":"inherit_base_rate","out":30.0}],"tool_prices":null,"unit":"token","units":[]},{"cache_read":0.2,"cache_write":5.0,"cache_write_1h":5.0,"cache_write_5m":5.0,"conditional_meter":null,"ctx":922000,"family":"openai","fast_multiplier":1,"flex_multiplier":1,"fmt":"responses","http":"POST","img_in":null,"img_out":null,"in":4.0,"input":["text"],"label":"OpenAI Responses","max_out":128000,"max_tool_calls":null,"out":20.0,"output":["text"],"path":"/v1/responses","request_model_id":"gpt-6.1-sol-ultrafast","route_label":"Ultra","status_reads":[],"tiers":[{"cache_read":0.2,"cache_write":5.0,"cache_write_1h":5.0,"cache_write_5m":5.0,"in":4.0,"interval_boundary":"min_exclusive_max_inclusive","label":"","max":272000,"min":0,"null_rate_policy":"inherit_base_rate","out":20.0},{"cache_read":0.4,"cache_write":10.0,"cache_write_1h":10.0,"cache_write_5m":10.0,"in":8.0,"interval_boundary":"min_exclusive_max_inclusive","label":"","max":null,"min":272000,"null_rate_policy":"inherit_base_rate","out":30.0}],"tool_prices":null,"unit":"token","units":[]},{"cache_read":0.2,"cache_write":5.0,"cache_write_1h":5.0,"cache_write_5m":5.0,"conditional_meter":null,"ctx":922000,"family":"gemini","fast_multiplier":1,"flex_multiplier":1,"fmt":"gemini","http":"POST","img_in":null,"img_out":null,"in":4.0,"input":["text"],"label":"Gemini generateContent","max_out":128000,"max_tool_calls":null,"out":20.0,"output":["text"],"path":"/v1beta/models/gpt-6.1-sol-ultrafast:generateContent","request_model_id":"gpt-6.1-sol-ultrafast","route_label":"Ultra","status_reads":[],"tiers":[{"cache_read":0.2,"cache_write":5.0,"cache_write_1h":5.0,"cache_write_5m":5.0,"in":4.0,"interval_boundary":"min_exclusive_max_inclusive","label":"","max":272000,"min":0,"null_rate_policy":"inherit_base_rate","out":20.0},{"cache_read":0.4,"cache_write":10.0,"cache_write_1h":10.0,"cache_write_5m":10.0,"in":8.0,"interval_boundary":"min_exclusive_max_inclusive","label":"","max":null,"min":272000,"null_rate_policy":"inherit_base_rate","out":30.0}],"tool_prices":null,"unit":"token","units":[]}],"model_aliases":[{"canonical_model_id":"gpt-6.1-sol-ultrafast","request_model_id":"gpt-6.1-sol-ultrafast","route_label":"Ultra"}],"name":"gpt-6.1-sol-ultrafast","price":{"in":4.0,"in_label":null,"out":20.0,"out_label":null,"sort_in":4.0,"sort_out":20.0,"varies":false},"public_id":"gpt-6.1-sol-ultrafast","short":"GPT-6.1 Sol Ultrafast for code analysis, complex text tasks and professional content preparation with a large context."} ``` ## 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.