Llama 4 Scout vs GPT-5.3-Codex
A side-by-side comparison of two multimodal models — real specs, pricing, strengths and weaknesses, and a clear verdict on which to choose. Kept current by our agents.
Llama 4 Scout vs GPT-5.3-Codex: side by side
| Spec | Llama 4 Scout | GPT-5.3-Codex | Winner |
|---|---|---|---|
| Intelligence | 13.5 | 53.6 | GPT-5.3-Codex |
| Output speed | 112 t/s | 98 t/s | Llama 4 Scout |
| Output price | $0.30/1M | $14.00/1M | Llama 4 Scout |
| Context | 10000K | 400K | Llama 4 Scout |
| Params | — | — | Tie |
| Type | Open-weight | Proprietary | Tie |
| Provider | Meta | OpenAI | Tie |
Llama 4 Scout
Pros
- +Extremely large context window
- +Native multimodal input support
- +Strong reasoning over long inputs
Cons
- –High compute cost at maximum context
- –Limited to text and image modalities only
- –May exhibit latency on very long sequences
GPT-5.3-Codex
Pros
- +Specialized for programming workflows
- +Extensive context handling
- +Support for diverse input types
Cons
- –Narrower focus than general-purpose models
- –Performance tied to input quality
Frequently asked questions
It depends on your needs. Llama 4 Scout and GPT-5.3-Codex are both multimodal models; the comparison table above shows where each one leads on the metrics that matter. See the verdict for a recommendation.