Macaron V1 Tall

LLM
Mindai

Macaron V1 Tall is designed for local deployment. A 50B mixture-of-LoRA-experts model with specialist adapters for chat, agent, coding, and generative UI tasks, offering a 262,144 token context window.

Context tokens

262,144

Output tokens

32,768

Released

Jul 15, 2026

Schema

Each request is automatically routed to one of four specialist adapters — Chat, Agent, Coding, or GenUI — based on your input, with ongoing reasoning staying within the selected specialist.

Schema documentation

Capabilities

Tool use
Structured output

Supported tools

  • Seclai Content Tools

    Inspect source documents connected to your account. Includes tools for loading full content, reading character ranges, searching within documents, viewing stats, and listing available content sources. When a source_connection_content_version_id is provided in agent run metadata it is used as the default. Otherwise the model can discover content via list_content_sources.

  • Seclai Knowledge Base

    Search your knowledge bases using semantic similarity. Includes search_knowledge_base and list_knowledge_bases. When a knowledge_base_id is provided in the prompt or agent run metadata it is used as the default. Otherwise the model can discover available knowledge bases at runtime.

  • Seclai Memory Banks

    Manage persistent memory across agent runs. Includes tools for listing memory banks, writing entries, searching memory via semantic similarity, and loading entries in chronological order. Supports two memory types: 'conversation' (speaker-attributed turns) and 'general' (freeform text). Use key to organize entries by topic, session, or user.

Pricing

TypeCreditsUnits
Input5.985Credits per 1k tokens
Output34.58Credits per 1k tokens
Cache hit1.064Credits per 1k tokens

Variants

No variants available for this model.

Try This Model

Write a prompt and experiment with Macaron V1 Tall in the model experiments page. You can compare it with other models side by side.