ModelRefs / Text Embedding 3 Large vs Llama 4 Scout — Benchmarks, Prici…
Text Embedding 3 Large vs Llama 4 Scout — Benchmarks, Prici…
Text Embedding 3 Large vs Llama 4 Scout: side-by-side benchmarks, pricing, context windows, coding ability and deployment. Pick the right model for your stac…
Overview
Llama 4 Scout: Llama 4 Scout is Meta's efficiency-focused open-weights model with an exceptionally large 10M-token context window and vision support. It is optimized for long-document retrieval and low-cost inference at scale.
Context window: Llama 4 Scout accepts up to 10,000,000 tokens, Text Embedding 3 Large up to 8,191. Confirm the limit for your specific deployment channel before relying on it.
Llama 4 Scout vs Text Embedding 3 Large at a glance
| Attribute | Llama 4 Scout | Text Embedding 3 Large |
|---|---|---|
| Provider | Meta | OpenAI |
| Released | 2025-04-01 | 2024-01-25 |
| Context window | 10,000,000 tokens | 8,191 tokens |
| Input price | $0.11/M tokens | $0.13/M tokens |
| Output price | $0.34/M tokens | Free / self-hosted |
| Licence | Llama 4 Community | Proprietary |
| Self-hostable | Yes, open weights | No, hosted only |
| Modalities | text, vision | text, Embedding |
Where they differ most
- Cost Efficiency: Llama 4 Scout 94%, Text Embedding 3 Large 99%. Text Embedding 3 Large leads on this dimension.
Capability scores are ModelRefs' own derived signals, not vendor claims or benchmark results. Validate against your own workload before relying on them.
Continue your research
Use these connected ModelRefs sections to compare alternatives, inspect implementation paths, and review the evidence and governance boundaries relevant to Text Embedding 3 Large vs Llama 4 Scout — Benchmarks, Prici….
Frequently asked questions
Which is better, Text Embedding 3 Large or Llama 4 Scout?
Text Embedding 3 Large and Llama 4 Scout target different workloads — see the benchmark and pricing tables for a side-by-side answer.
Is Text Embedding 3 Large cheaper than Llama 4 Scout?
Compare $0.00013 vs $0.00011 on this page.