Arcee Aideep

Maestro Reasoning

Maestro Reasoning is Arcee's flagship analysis model: a 32 B‑parameter derivative of Qwen 2.5‑32 B tuned with DPO and chain‑of‑thought RL for step‑by‑step logic. Compared to the earlier 7 B...

131K context·~98K words·32K max output

View context details Compare with Amazon Titan Text Express

Context window131Ktokens

Max output32Ktokens

Context window

This model accepts 131K tokens in one request (~98K words of text).

Context window size131K tokens

4K32K128K1M10M

What fits in one request

Short document
About 1,500 words of text
Fits
Long document
About 37K words of text
Fits
Small codebase
About 150K words of text
Won't fit
Full novel
About 375K words of text
Won't fit

Specifications

Context size, pricing, and release info in one place.

Context window: 131,072 tokens (131K)
Max output tokens: 32,000 tokens (32K)
Speed tier: deep
Provider: Arcee Ai
Release date: May 2025

Capabilities

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Supported (1)

Streaming: Returns tokens as they are generated; Supported

Not supported (7)

Vision: Accepts image inputs alongside text; Not supported
Tool use: Can call external tools and APIs; Not supported
Function calling: Structured function call interface; Not supported
Extended thinking: Shows its chain-of-thought reasoning; Not supported
Web search: Can browse the web during a request; Not supported
Batch API: Process many requests asynchronously; Not supported
Prompt caching: Reuse repeated prompt prefixes cheaply; Not supported

Best for

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Reasoning Long documents RAG

Compare Maestro Reasoning

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Frequently asked questions

Short answers about context size and how this model behaves.

Maestro Reasoning has a context window of 131K tokens (131,072 tokens). This covers most professional use cases including large code files, lengthy reports, and long conversation histories.

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Mem0 gives your AI long-term memory so you stop re-sending context on every call. That means you can use a smaller, faster, cheaper model — and still get better answers.

Calculate your savings Learn about Mem0

Example: a multi-turn chat session

Without Mem0~128K tokens sent

Full history

Repeated info

Old context

With Mem0~20K tokens sent

Key memories

Current turn

80% less to send — works with any model