Llama 4 Scout vs GPT-5.1-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.
Quick verdict: which should you choose?
Choose Llama 4 Scout if you need
- ✓Extremely large context windows up to 10M tokens for long text and image sequences
- ✓Very low output cost at $0.3 per 1M tokens
- ✓Open-weight model from Meta for full control and customization
- ✓Strong reasoning over extended multimodal inputs without losing coherence
Choose GPT-5.1-Codex if you need
- ✓Highest intelligence index (43.1) for complex tasks
- ✓Faster output speed at 180.03 t/s
- ✓Specialized performance on extended coding workflows with visual context
- ✓Reliable handling of very large inputs in software development scenarios
Verdict
GPT-5.1-Codex leads decisively on intelligence (43.1 vs 13.5) and output speed (180 vs 112 t/s) while remaining specialized for coding workflows. Llama 4 Scout dominates on context length (10M vs 400k tokens) and price ($0.3 vs $10 per 1M tokens) with open-weight availability. The choice hinges on whether raw capability or extreme-scale, low-cost long-context reasoning is prioritized.
Llama 4 Scout vs GPT-5.1-Codex: side by side
| Spec | Llama 4 Scout | GPT-5.1-Codex | Winner |
|---|---|---|---|
| Intelligence | 13.5 | 43.1 | GPT-5.1-Codex |
| Output speed | 112 t/s | 180 t/s | GPT-5.1-Codex |
| Output price | $0.30/1M | $10.00/1M | Llama 4 Scout |
| Context | 10000K | 400K | Llama 4 Scout |
| Params | — | — | Tie |
| Type | Open-weight | Proprietary | Tie |
| Provider | Meta | OpenAI | Tie |
Detailed analysis
Intelligence
Winner: GPT-5.1-CodexGPT-5.1-Codex scores 43.1 on the intelligence index compared to Llama 4 Scout's 13.5. This gap indicates stronger overall capability on the provided benchmarks. Both models support text and image inputs.
Speed
Winner: GPT-5.1-CodexGPT-5.1-Codex delivers 180.03 tokens per second versus Llama 4 Scout's 112.48 t/s. The faster model may reduce latency in interactive use. Neither lists additional modality support beyond text and image.
Pricing
Winner: Llama 4 ScoutLlama 4 Scout costs $0.3 per million output tokens while GPT-5.1-Codex costs $10 per million. The 33x price difference favors Llama 4 Scout for high-volume workloads. Both carry high compute costs at maximum context lengths.
Context Window
Winner: Llama 4 ScoutLlama 4 Scout provides a 10,000,000-token context versus GPT-5.1-Codex's 400,000 tokens. This gives Llama 4 Scout a 25x advantage for long multimodal sequences. Both are limited to text and image modalities.
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-5.1-Codex
Pros
- +Strong performance on extended coding workflows
- +Effective integration of visual context with code
- +Handles very large inputs without losing coherence
- +Specialized for software development tasks
Cons
- –Limited to text and image inputs only
- –High computational cost for maximum context
- –May require careful prompt engineering for complex tasks
Summary: Llama 4 Scout vs GPT-5.1-Codex
Select Llama 4 Scout when maximum context length and minimal cost are essential. Choose GPT-5.1-Codex when higher intelligence, faster generation, and coding specialization matter most. The models serve different priorities within the multimodal category.
Frequently asked questions
GPT-5.1-Codex scores higher on intelligence and speed while Llama 4 Scout offers far larger context and lower price; neither is universally better.