Edge
Reka Edge is a 7-billion-parameter multimodal model from Reka AI, designed specifically for real-time, physical AI applications such as robotics, automotive systems, and edge devices.
Publisher review
Reka Edge is a 7-billion-parameter multimodal model from Reka AI, designed specifically for real-time, physical AI applications such as robotics, automotive systems, and edge devices. Unlike most large language models that treat vision and audio as add-ons, Edge natively processes text, images, video, and audio, with a particular emphasis on visual reasoning and physical grounding. The model is optimized to produce only 64 tokens per image tile, enabling efficient handling of high-definition visual data without excessive computational overhead. Edge is engineered for developers and enterprises that need low-latency, on-device intelligence—it runs on cloud, VPC, on-premise, or fully air-gapped environments, and is also available via the Puter.js API at no cost. The model targets use cases like video understanding, object detection, and agentic tool use, where it demonstrates frontier-level tool-calling abilities despite its small size.
Reka Edge achieves state-of-the-art performance for its size class, outperforming larger closed models such as Gemini 3 Pro on benchmarks including MLVU (video understanding), MMVU (multimodal video understanding), and RefCOCO (referring expression comprehension). It excels at visual reasoning tasks, such as interpreting complex scenes from video frames and answering temporal questions over long sequences. The model is built on Reka's own research foundation, which includes proprietary training data and inference infrastructure (infer engine). Edge is part of a broader lineup that includes Spark (1B), Flash (21B), and Core (67B), allowing users to scale up or down depending on latency and accuracy requirements. The model is also open-sourced on Hugging Face and GitHub, with active community contributions.
In the competitive landscape, Reka Edge competes directly with models from Anthropic (Claude), Google (Gemini), OpenAI (GPT-4o), DeepSeek, MoonshotAI, Tencent, and Bytedance Seed. Its primary differentiator is its focus on edge deployment and physical AI—whereas most competitors optimize for cloud-based chat or general-purpose reasoning, Edge is tuned for real-time, latency-sensitive tasks like robotics control and automotive perception. It also undercuts larger models on cost: $0.10 per million input tokens and $0.10 per million output tokens, making it one of the cheapest multimodal models available. However, it lacks the broad ecosystem and brand recognition of OpenAI or Google, and its benchmark victories are often against models with far more parameters, not against the latest frontier systems.
The honest trade-offs: Reka Edge is not a turnkey product for non-technical users. There is no polished end-user UI—most interactions happen via API or a basic playground. Documentation can be incomplete for newer features, and there is no public pricing page; enterprise rates are only available on request. The model's 7B parameter count means it cannot match the raw reasoning depth of 100B+ models on complex text-only tasks. Additionally, while Edge is free through Puter.js, the primary commercial API lacks transparent, self-serve pricing tiers, which can frustrate small teams or individual developers. For organizations that need a lightweight, fast, and cost-effective multimodal model for real-world physical AI, Edge is a strong contender—but it requires technical expertise to deploy effectively.
How it works
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Multimodal processing
Natively processes text, images, video, and audio simultaneously, not as separate add-ons.
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Visual reasoning
Optimized for interpreting complex visual scenes, object detection, and physical grounding tasks.
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Efficient token usage
Produces only 64 tokens per image tile, reducing computational cost for high-definition visuals.
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Real-time edge deployment
Designed for low-latency applications like robotics and automotive, running on cloud or air-gapped environments.
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Agentic tool use
Demonstrates frontier-level tool-calling abilities, enabling autonomous actions based on visual input.
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Benchmark leadership
Outperforms larger models on MLVU, MMVU, and RefCOCO, despite having only 7B parameters.
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Open-source availability
Model weights and tools are published on Hugging Face and GitHub for community use.
Strengths and trade-offs
Strengths
- Outperforms Gemini 3 Pro on video understanding benchmarks (MLVU, MMVU) despite having 7B parameters versus Gemini's much larger size.
- Efficient token generation at 64 tokens per image tile enables cost-effective processing of high-definition visual data.
- Priced at $0.10 per million input tokens and $0.10 per million output tokens, making it one of the cheapest multimodal models available.
- Free to use through the Puter.js API, lowering the barrier for developers to experiment with edge AI.
Trade-offs
- No public pricing page exists; enterprise rates are only available on request, creating uncertainty for small teams.
- Documentation can be incomplete for newer product features, leading to a steep learning curve for non-technical users.
- Limited end-user UI—most interactions require API calls or the basic playground, not a turnkey application.
- 7B parameter count limits raw reasoning depth compared to 100B+ models on complex, text-only tasks.
Pricing context
$0.10 per million input tokens and $0.10 per million output tokens via API; free through Puter.js API; enterprise pricing available on request.
Getting started with Edge
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Sign up for API access
Go to the Reka AI website and create an account. After verifying your email, request API credentials from the dashboard. Enterprise users can contact sales for dedicated access and pricing.
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Install the client library
Open your terminal and run pip install reka-api to install the official Python client. Alternatively, clone the GitHub repository to access the model weights for local deployment.
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Configure the Edge model
Set your API key as an environment variable using export REKA_API_KEY='your_key'. In your code, import the client and initialize it with the key to connect to the Edge model endpoint.
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Run a visual reasoning task
Load an image or video file into your script. Call the model with the media and a prompt like 'Describe the objects in this scene'. The model returns a text response with detected items and their relationships.
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Deploy on an edge device
Download the model weights from Hugging Face. Use the provided inference code to run the model locally on a device like a Raspberry Pi or NVIDIA Jetson, ensuring low-latency processing without cloud dependency.
Frequently Asked Questions
What is Reka Edge and what is it used for?
Reka Edge is a 7-billion-parameter multimodal model from Reka AI designed for real-time physical AI applications like robotics, automotive systems, and edge devices. It natively processes text, images, video, and audio, with a focus on visual reasoning and low-latency on-device intelligence.
How does Reka Edge compare to larger models like Gemini 3 Pro?
Reka Edge outperforms larger models such as Gemini 3 Pro on video understanding benchmarks like MLVU, MMVU, and RefCOCO, despite having only 7 billion parameters. It excels at visual reasoning tasks, interpreting complex scenes from video frames and answering temporal questions efficiently.
What is the pricing for Reka Edge and is there a free option?
Reka Edge costs $0.10 per million input tokens and $0.10 per million output tokens via its API, making it one of the cheapest multimodal models. It is also available for free through the Puter.js API, though enterprise pricing is only available on request.
Can Reka Edge run on edge devices or air-gapped environments?
Yes, Reka Edge is engineered for low-latency, on-device intelligence and runs on cloud, VPC, on-premise, or fully air-gapped environments. This makes it suitable for real-time applications like robotics control and automotive perception where data privacy and speed are critical.
What are the main weaknesses of Reka Edge for developers?
Reka Edge lacks a polished end-user UI, with most interactions via API or basic playground. Documentation can be incomplete for newer features, and there is no public pricing page, creating uncertainty for small teams. Its 7B parameters limit raw reasoning depth on complex text-only tasks.
Is Reka Edge open source and how can I access it?
Yes, Reka Edge is open-sourced on Hugging Face and GitHub with active community contributions. Developers can access model weights and tools there, or use the model via the Puter.js API at no cost for experimentation and real-time edge AI applications.
Alternatives
How Edge compares
Direct head-to-head against 2 competitors. Picked by 7wData.
Edge
- Pricing
- $0.10 per million input tokens and $0.10 per million output tokens via API; free through Puter.js API; enterprise pricing available on request.
- Target
- Reka Edge is a 7-billion-parameter multimodal model from Reka AI, designed specifically for real-time, physical AI applications such as robotics, automotive systems, and edge devices.
- Strength
- Outperforms Gemini 3 Pro on video understanding benchmarks (MLVU, MMVU) despite having 7B parameters versus Gemini's much larger size.
- Watch for
- No public pricing page exists; enterprise rates are only available on request, creating uncertainty for small teams.
Google Chrome
- Pricing
- Free
- Target
- General browsing, web apps
- Deployment
- Desktop, mobile
- Strength
- Extensive extension ecosystem
- Watch for
- High memory usage
Mozilla Firefox
- Pricing
- Free
- Target
- Privacy-focused users
- Deployment
- Desktop, mobile
- Strength
- Strong privacy features
- Watch for
- Limited enterprise support
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Sources
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