Reka AibalancedVisionTool use

Reka Edge

Reka Edge is an extremely efficient 7B multimodal vision-language model that accepts image/video+text inputs and generates text outputs. This model is optimized specifically to deliver industry-leading performance in image understanding,...

16K context·~12K words·16K max output

View context details Compare with Amazon Titan Text Express

Context window16Ktokens

Max output16Ktokens

Context window

This model accepts 16K tokens in one request (~12K words of text).

Context window size16K tokens

4K32K128K1M10M

What fits in one request

Short document
About 1,500 words of text
Fits
Long document
About 37K words of text
Won't fit
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: 16,384 tokens (16K)
Max output tokens: 16,384 tokens (16K)
Speed tier: balanced
Provider: Reka Ai
Release date: Mar 2026

Capabilities

See which features this model supports, such as vision, tools, and streaming.

Supported (4)

Vision: Accepts image inputs alongside text; Supported
Tool use: Can call external tools and APIs; Supported
Function calling: Structured function call interface; Supported
Streaming: Returns tokens as they are generated; Supported

Not supported (4)

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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AI agents Multimodal

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Frequently asked questions

Short answers about context size and how this model behaves.

Reka Edge has a context window of 16K tokens (16,384 tokens). This is sufficient for most chat, summarization, and moderate document tasks.

More from Reka Ai

Other models by Reka Ai in our catalog.

Reka Flash 3

66Kfast

Use a smaller model.
Get better results.

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.

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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