Llama 4 Scout vs GPT-5 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 10M-token context for long text and image sequences
- ✓Very low cost at $0.3 per 1M tokens
- ✓Open-weight access from Meta for customization
- ✓Strong reasoning over long multimodal inputs
Choose GPT-5 Codex if you need
- ✓Much higher intelligence index of 44.6 for complex tasks
- ✓Faster output at 152.4 tokens per second
- ✓Strong coding specialization with unified text-image reasoning
- ✓Effective handling of large inputs despite smaller context
Verdict
GPT-5 Codex leads decisively on intelligence (44.6 vs 13.5) and output speed (152.4 t/s vs 112.48 t/s) while Llama 4 Scout dominates on context length (10M vs 400k tokens) and price ($0.3 vs $10 per 1M). Llama 4 Scout's open-weight nature and extreme context suit long-sequence multimodal reasoning, whereas GPT-5 Codex excels at high-performance coding and unified reasoning tasks. The choice hinges on whether scale and cost or raw capability matter most.
Llama 4 Scout vs GPT-5 Codex: side by side
| Spec | Llama 4 Scout | GPT-5 Codex | Winner |
|---|---|---|---|
| Intelligence | 13.5 | 44.6 | GPT-5 Codex |
| Output speed | 112 t/s | 152 t/s | GPT-5 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 CodexGPT-5 Codex scores 44.6 on the intelligence index compared to Llama 4 Scout's 13.5. This gap indicates superior performance on complex multimodal reasoning and coding. Llama 4 Scout remains competitive only on long-context subsets.
Speed
Winner: GPT-5 CodexGPT-5 Codex delivers 152.4 tokens per second versus Llama 4 Scout's 112.48 t/s. The faster rate benefits high-volume or interactive multimodal workloads. Both models note potential latency issues at maximum context lengths.
Pricing
Winner: Llama 4 ScoutLlama 4 Scout costs $0.3 per million tokens while GPT-5 Codex costs $10 per million. The 33x price advantage favors Llama 4 Scout for budget-sensitive or high-volume usage. Both carry high compute costs when using full context.
Context Window
Winner: Llama 4 ScoutLlama 4 Scout supports a 10-million-token context versus GPT-5 Codex's 400,000 tokens. This enables native handling of far longer text and image sequences. GPT-5 Codex still claims effective large-input handling within its smaller window.
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 Codex
Pros
- +Handles extremely large inputs effectively
- +Strong coding specialization
- +Unified text and image reasoning
Cons
- –High resource demands with maximum context
- –Limited to text and static images
- –Potential coherence loss in very long outputs
Summary: Llama 4 Scout vs GPT-5 Codex
Select Llama 4 Scout when maximum context length, low cost, and open weights are priorities. Choose GPT-5 Codex when highest intelligence, speed, and coding performance are required. The models serve different trade-off points rather than direct substitutes.
Frequently asked questions
GPT-5 Codex is better on intelligence and speed; Llama 4 Scout is better on context size and price. No single winner exists across all metrics.