Devstral 2512
Neural Network
Devstral 2512 is an open Mistral AI model for agentic programming: 123B parameters and a 256K token context window.
Max answer length
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Context size
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Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
How it works Devstral 2512?
Devstral 2512 is an open Mistral AI model designed for agentic programming: a dense 123B parameter transformer with a 256K token context window, sufficient to keep an entire large repository in view rather than just individual files. The model is focused on working with codebases—finding necessary fragments, making edits, and verifying results—while the open license removes concerns about where and how to apply it. In BotHub, Devstral is available from Russia without a VPN or foreign card: you pay in rubles only for the tokens you use, and unused tokens do not expire. Alongside it in one window are over 250 other models, making it easy to switch between them and compare answers to the same prompt; a unified OpenAI-compatible API allows you to change models in production without rewriting the integration. Chats are encrypted via AES-GCM and not saved, and for teams, there is a contract, invoice, electronic document management, and an admin panel with limits. Simple scenarios: understand an unfamiliar project, migrate a legacy module to a new stack, find the cause of a floating bug, assemble autotests, and prepare a review before release.Frequently asked questions about Devstral 2512
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.
Devstral-2512 by Mistral AI is a text model focused on development: code generation and refactoring, debugging, reviews, and explaining unfamiliar modules. It accepts documents as input, supports function calling, and its 262,144 token context allows you to keep an entire codebase in a single dialogue.
The single response limit is 209,715 tokens, which is very high for text models: enough for a large module with tests, extensive documentation, or a detailed architectural breakdown. It is still more convenient to request individual parts step-by-step, as the result is easier to read and verify.
A separate reasoning mode is not noted in our data for Devstral-2512, so we will not promise a hidden chain of thought. In practice, a simple technique helps: ask it to break down the task step-by-step, show a plan, and provide intermediate conclusions—this significantly helps with complex logic.
Yes, function calling and structured JSON output are supported—the model is suitable for agents that read files, call your tools, and return responses in a strict schema. Connection is via the unified OpenAI-compatible BotHub API, so you won't have to rewrite the integration for another model.
In addition to text, the model accepts documents—it is convenient to upload a specification, log, or technical task and work with them in one dialogue. Images, audio, and video are not noted in our data; for such tasks, switch to a specialized model in the same BotHub window.
The language of prompts and responses is not fixed in our data, so we will not invent an assessment—it is more reliable to test it on your own task with a couple of queries. The service itself is Russian-speaking: intuitive interface, payment with Russian cards, and access without VPN or foreign cards.