**Vistral** - это наша новая флагманская унимодальная LLM (Large Language Model) представляющая из себя улучшенную версию mistralai/Mistral-Small-3.2-24B-Instruct-2506 командой **VikhrModels**, адаптированную преимущественно для русского и английского языков. Удалён визуальный энкодер, убрана мультимодальность. Сохранена стандартная архитектура "MistralForCausalLM" без изменений в базовой структуре модели. Весь использованный код для обучения доступен в нашем репозитории effective_llm_alignment на GitHub, а основные датасеты доступны в нашем профиле на HF. - GGUF Vikhrmodels/Vistral-24B-Instruct-GGUF - MLX - 4 bit Vikhrmodels/Vistral-24B-Instruct-MLX_4bit - 8 bit Vikhrmodels/Vistral-24B-Instruct-MLX_8bit

Text generation

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Vistral-24B-Instruct-GGUF

Vikhrmodels

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COST EXPLORER

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Cost analysis

Adjust monthly token usage to compare estimated API costs.

Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
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MODEL HANDBOOK

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Selection guide & FAQ

How should API prices be compared?+

Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.

Do open weights mean free commercial use?+

Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.

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Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.

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Vikhr-Qwen-2.5-1.5B-Instruct

Vikhr-Qwen-2.5-1.5B-Instruct-GGUF

Vikhr-YandexGPT-5-Lite-8B-it

Vikhr-YandexGPT-5-Lite-8B-it_GGUF

Vistral-24B-Instruct

Vistral-24B-Instruct-GGUF

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