Llama 4 Scout
- Context
- 10,000,000
- Max output
- Not published
- Input / MTok
- Not published
- Cached / MTok
- —
- Output / MTok
- Not published
- Input types
- text + image
OPEN-WEIGHT ARCHITECTURE / VERIFIED 2026-10-04
Compare Meta's Llama 4 models on verified context capacity, modalities and published specification differences without inventing direct API pricing.
LIVE VERIFIED FACTS
DECISION FACTORS
Llama 4 Scout publishes the larger context window: 10,000,000 vs 1,000,000 tokens (10.0×).
Both list image, text as input types.
Direct Standard token-cost comparison is unavailable because Llama 4 Scout, Llama 4 Maverick does not have a calculator-eligible provider-published paid rate in the SXF catalog.
HOW TO DECIDE
Context, modalities and direct token economics are comparable from official sources. Coding quality, latency, reliability and agent success should be measured on your own acceptance tests before production routing.
Use representative prompts, files, tools and expected outputs from the workload you plan to ship.
Include retries, cached tokens, long-context rules and tool charges—not only headline input price.
Record hallucinations, tool errors, timeout behavior and human corrections alongside pass rate.
A portfolio can outperform a one-model policy when different task classes have different cost and capability needs.
INDEPENDENT EVALUATIONS
SXF does not infer quality from specifications or compare benchmark scores across different evaluator versions/configurations.
WHAT CHANGED
No post-baseline factual changes recorded for Llama 4 Scout, Llama 4 Maverick since 2026-10-03.
QUICK ANSWERS
Llama 4 Scout publishes the larger context window: 10,000,000 vs 1,000,000 tokens (10.0×).
Direct Standard token-cost comparison is unavailable because Llama 4 Scout, Llama 4 Maverick does not have a calculator-eligible provider-published paid rate in the SXF catalog.
No. This page compares source-backed specifications and economics. Quality, latency and task success require workload-specific evaluation evidence.