Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimodal input (text and image) and multilingual output (text and…
Model details →Meta: Llama 4 Scout vs Reka Edge
Meta: Llama 4 Scout wins on 2 of 6 axes — pricing and capability skew in its favour for most workloads.
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, vi…
Model details →Side-by-side comparison
| Capability | Meta: Llama 4 Scout | Reka Edge | Winner |
|---|---|---|---|
| Context window Maximum number of input tokens the model can attend to in a single request. | 328K | 16K | 🏆 Meta: Llama 4 Scout |
| Input price (per 1M) Cost per million input tokens billed by the provider. | $0.08 | $0.10 | 🏆 Meta: Llama 4 Scout |
| Output price (per 1M) Cost per million output tokens billed by the provider. | $0.30 | $0.10 | 🏆 Reka Edge |
| Tool / function calling First-class support for emitting structured tool calls. | Yes | Yes | Tie |
| Vision input Accepts image inputs alongside text. | Yes | Yes | Tie |
| Reasoning mode Internal chain-of-thought / extended-thinking support. | — | — | Tie |
Frequently asked questions
Is Meta: Llama 4 Scout better than Reka Edge?
Meta: Llama 4 Scout wins on 2 of 6 axes — pricing and capability skew in its favour for most workloads.
What's the price difference between Meta: Llama 4 Scout and Reka Edge?
Input: $0.08 vs $0.10 per 1M tokens. Output: $0.30 vs $0.10 per 1M tokens.
What context windows do Meta: Llama 4 Scout and Reka Edge support?
Meta: Llama 4 Scout supports up to 328K tokens. Reka Edge supports up to 16K tokens.
Do both Meta: Llama 4 Scout and Reka Edge support tool calling?
Meta: Llama 4 Scout: yes. Reka Edge: yes.