Source: https://ailoot.ai/models/en/recursive-flint-v1-0-free/ Canonical: https://ailoot.ai/models/en/recursive-flint-v1-0-free/ Language: en # Recursive: Flint (free) [Markdown](https://ailoot.ai/models/en/recursive-flint-v1-0-free/index.md) · [JSON](https://ailoot.ai/models/en/recursive-flint-v1-0-free/facts.json) ## [Recursive: Flint (free)](https://ailoot.ai/models/en/recursive-flint-v1-0-free/) `recursive/flint-v1.0:free` RecursiveTextInput $0.00 / 1KOutput $0.00 / 1K Recursive’s text MoE model for exploring codebases, fixing bugs and preparing features and tests. ### A model for working with code Flint is a Recursive text model built for coding agents: exploring codebases, fixing bugs, implementing features and writing tests. It is an open-weight MoE model: approximately 2.5 billion of its 39.8 billion parameters are active when processing a token. The expert structure is part of its architecture; it does not mean there are several separate assistants in the chat. Eureka Machine, Recursive’s research system, participated in Flint’s development. It helped explore architectural choices, select data and training methods, and optimize computation. Flint itself underwent pre-training, mid-training, instruction-example training and reinforcement learning. Its training tasks included text, code, instruction following and agentic workflows. ### Understanding a project and preparing changes Start a working task with a specific part of the project: provide the necessary files as text, explain the functions’ purpose and describe the expected result. To investigate a bug, include the observed behavior and a log excerpt. Then ask for a cause explanation, a minimal fix and a test that distinguishes correct behavior from the bug. This sequence makes the result easier to verify later in your environment. For a new feature, specify the input data, constraints and example outputs. For refactoring, list the properties that must be preserved and ask for interface changes to be described separately. The developer recommends breaking complex work into small subtasks and giving explicit instructions. A sequential conversation with intermediate results therefore fits Flint’s intended use better than an open-ended instruction to remake an entire project. ### Context and connection formats In Ailoot API, Flint accepts text and returns text. Its context window is 262,144 tokens; source material, conversation history and the response share this space. The stated output limit is 235,929 tokens. These values describe the connection’s boundaries; set the actual length to suit the task, whether a bug explanation, a patch, a test suite or a work plan. Messages, Responses and Chat Completions formats are available, allowing you to use your client application’s familiar structure. The application itself handles reading local files and applying changes: in a text request, the model receives supplied code and returns proposals, explanations or new code. Flint suits iterative technical discussions in which each step builds on the project’s contents and the verification result of the preceding change. Its specialization helps frame the task, while the usable result is concrete text: an explanation, an implementation or a test that can be run and checked. ### Available through Ailoot API Input types `text` Output types `text` Context 262,144 *tokens* Max. output 235,929 *tokens* Input $0.00 */ 1K* Output $0.00 */ 1K* Input types `text` Output types `text` Context 262,144 *tokens* Max. output 235,929 *tokens* Input $0.00 */ 1K* Output $0.00 */ 1K* Input types `text` Output types `text` Context 262,144 *tokens* Max. output 235,929 *tokens* Input $0.00 */ 1K* Output $0.00 */ 1K* `recursive/flint-v1.0:free` · JSON ``` {"about":"### A model for working with code\n\nFlint is a Recursive text model built for coding agents: exploring codebases, fixing bugs, implementing features and writing tests. It is an open-weight MoE model: approximately 2.5 billion of its 39.8 billion parameters are active when processing a token. The expert structure is part of its architecture; it does not mean there are several separate assistants in the chat.\n\nEureka Machine, Recursive’s research system, participated in Flint’s development. It helped explore architectural choices, select data and training methods, and optimize computation. Flint itself underwent pre-training, mid-training, instruction-example training and reinforcement learning. Its training tasks included text, code, instruction following and agentic workflows.\n\n### Understanding a project and preparing changes\n\nStart a working task with a specific part of the project: provide the necessary files as text, explain the functions’ purpose and describe the expected result. To investigate a bug, include the observed behavior and a log excerpt. Then ask for a cause explanation, a minimal fix and a test that distinguishes correct behavior from the bug. This sequence makes the result easier to verify later in your environment.\n\nFor a new feature, specify the input data, constraints and example outputs. For refactoring, list the properties that must be preserved and ask for interface changes to be described separately. The developer recommends breaking complex work into small subtasks and giving explicit instructions. A sequential conversation with intermediate results therefore fits Flint’s intended use better than an open-ended instruction to remake an entire project.\n\n### Context and connection formats\n\nIn Ailoot API, Flint accepts text and returns text. Its context window is 262,144 tokens; source material, conversation history and the response share this space. The stated output limit is 235,929 tokens. These values describe the connection’s boundaries; set the actual length to suit the task, whether a bug explanation, a patch, a test suite or a work plan.\n\nMessages, Responses and Chat Completions formats are available, allowing you to use your client application’s familiar structure. The application itself handles reading local files and applying changes: in a text request, the model receives supplied code and returns proposals, explanations or new code.\n\nFlint suits iterative technical discussions in which each step builds on the project’s contents and the verification result of the preceding change. Its specialization helps frame the task, while the usable result is concrete text: an explanation, an implementation or a test that can be run and checked.","alias_of":null,"aliases":[],"author":"Recursive","canonical_model":"recursive/flint-v1.0:free","ctx":262144,"formats":["chat","messages","responses"],"id":"recursive/flint-v1.0:free","input":["text"],"kind":"text","max_out":235929,"methods":[{"cache_read":null,"cache_write":null,"cache_write_1h":null,"cache_write_5m":null,"conditional_meter":null,"ctx":262144,"family":"anthropic","fast_multiplier":1,"flex_multiplier":1,"fmt":"messages","http":"POST","img_in":null,"img_out":null,"in":0,"input":["text"],"label":"Anthropic Messages","max_out":235929,"max_tool_calls":null,"out":0,"output":["text"],"path":"/v1/messages","request_model_id":"recursive/flint-v1.0:free","route_label":"Ultra","status_reads":[],"tiers":[],"tool_prices":null,"unit":"token","units":[]},{"cache_read":null,"cache_write":null,"cache_write_1h":null,"cache_write_5m":null,"conditional_meter":null,"ctx":262144,"family":"openai","fast_multiplier":1,"flex_multiplier":1,"fmt":"responses","http":"POST","img_in":null,"img_out":null,"in":0,"input":["text"],"label":"OpenAI Responses","max_out":235929,"max_tool_calls":null,"out":0,"output":["text"],"path":"/v1/responses","request_model_id":"recursive/flint-v1.0:free","route_label":"Ultra","status_reads":[],"tiers":[],"tool_prices":null,"unit":"token","units":[]},{"cache_read":null,"cache_write":null,"cache_write_1h":null,"cache_write_5m":null,"conditional_meter":null,"ctx":262144,"family":"openai","fast_multiplier":1,"flex_multiplier":1,"fmt":"chat","http":"POST","img_in":null,"img_out":null,"in":0,"input":["text"],"label":"OpenAI Chat","max_out":235929,"max_tool_calls":null,"out":0,"output":["text"],"path":"/v1/chat/completions","request_model_id":"recursive/flint-v1.0:free","route_label":"Ultra","status_reads":[],"tiers":[],"tool_prices":null,"unit":"token","units":[]}],"model_aliases":[{"canonical_model_id":"recursive/flint-v1.0:free","request_model_id":"recursive/flint-v1.0:free","route_label":"Ultra"}],"name":"Recursive: Flint (free)","price":{"in":0,"in_label":null,"out":0,"out_label":null,"sort_in":0,"sort_out":0,"varies":false},"public_id":"recursive/flint-v1.0:free","short":"Recursive’s text MoE model for exploring codebases, fixing bugs and preparing features and tests."} ``` ## 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.