Gemini 2.5 Pro vs Llama 4 Scout
How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for Gemini 2.5 Pro and Llama 4 Scout.
Gemini 2.5 Pro
Gemini 2.5 Pro is Google’s state-of-the-art AI model designed for advanced reasoning, coding, mathematics, and scientific tasks. It employs “thinking” capabilities, enabling it to reason through responses with enhanced accuracy...
Llama 4 Scout
Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimodal input...
Technical Specifications
| Specification | Gemini 2.5 Pro | Llama 4 Scout |
|---|---|---|
| Provider | Meta | |
| Context Window | 1,048,576 tokens | 10,000,000 tokens |
| Agent Suitability | 90/100 | 82/100 |
| Time to First Token (TTFT) | 450 ms | 350 ms |
| Deployment Model | managed api | self hostable |
| Production Stability | stable | beta |
| API Available | Yes | Yes |
| Released Date | 2025-06-17 | 2025-04-05 |
API Pricing Comparison
Input Price per Million Tokens
Gemini 2.5 Pro
$1.25
Llama 4 Scout
$0.10
Output Price per Million Tokens
Gemini 2.5 Pro
$10.00
Llama 4 Scout
$0.30
Want to test both models live?
Run side-by-side prompt prompts in our dynamic Sandbox. Check execution speeds, latency metrics, and compute actual costs in real-time.
Benchmark Performance Metrics
Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.
Gemini 2.5 Pro Quirks & Gotchas
- ▸Legacy model — migrate to Gemini 3.1 Pro for better tool calling and lower latency
Llama 4 Scout Quirks & Gotchas
- ▸10M context causes significant VRAM pressure — recommend 4-bit quantization
- ▸Primarily designed for RAG, not agentic tool calling