Pareto Code
Neural Network
Pareto Code is a router that directs requests to strong coding models, providing a unified Pareto Code API for integrations.
Specifications
- Input
- text, images, documents
- Output
- text
- Context size
- 2 000 000 tokens
- Max answer length
- 660 000 tokens
- Reasoning effort
- low, medium, high
- Capabilities
- reasoning, function calling
How it works Pareto Code?
Pareto Code is not a single model, but a router: it maintains a multi-level shortlist of strong programming models, ranked by coding percentiles from the independent Artificial Analysis rating, and sends your request to one of them. The min_coding_score parameter (0 to 1) sets the bar: higher values mean a narrower selection and stricter code quality requirements for candidate models. You can connect to Pareto Code via BotHub from Russia without a VPN or foreign card, paying in rubles with Russian cards based on usage. The unified OpenAI-compatible API allows switching between this router and any other model without rewriting integrations. Data is transmitted encrypted, and for teams, there is a contract, invoice, and admin panel with limits. Scenarios are simple: write and refactor code, debug errors and stack traces, review pull requests, generate tests and utilities, and keep a single endpoint for coding tasks in your product, changing only the quality bar instead of the model list.Frequently asked questions about Pareto Code
You can use generated results for commercial purposes. You own all rights to the content you create. The only restriction: make sure your prompt does not include copyrighted third-party material. You are responsible for respecting the rights to any input data.
pareto-code works with text and documents, and also accepts images as input. A context of up to 200,000 tokens is suitable for long files, code, and documentation analysis: you can upload material in its entirety and ask questions about it. Access to the model is available via BotHub — without a VPN or foreign card.
Our data indicates an output limit of up to 660,000 tokens per response, with a model context of 200,000 tokens. This means long materials—reports, code reviews, voluminous texts—can be received in a single message without splitting the task. It is more convenient to check specific values directly in the interface.
There is no specific note about step-by-step reasoning before answering in our data, so we cannot promise this in advance. However, the model is suitable for tasks where logic is important: working with code, analyzing documents, and parsing long texts. It is easier to check the required mode in practice by setting a multi-step task.
There is no note about function calling or structured JSON output in our data, but this does not mean the capabilities are absent—there is simply no confirmation. If you need a strict response format, describe it in the prompt and verify the result with a test request before building the integration. For multi-model systems, BotHub has a unified OpenAI-compatible API.
The model data shows that it accepts text, images, and documents as input: you can attach an image or file and ask questions about them. There are no specific notes about audio and video, so this should be checked in practice. All of this is available via the BotHub website and Telegram bot.
Our data contains no information about prompt and output languages, so we cannot evaluate the quality of work with Russian text in advance—this is a matter for practical testing. The model accepts text queries and documents, so you can test it with your own materials and phrasing in a regular chat. Payment is based on usage, and tokens do not expire.