Gemini 3.1 Pro Preview Custom Tools vs Llama 3.1 8B
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 3.1 Pro Preview Custom Tools and Llama 3.1 8B.
Gemini 3.1 Pro Preview Custom Tools
Gemini 3.1 Pro Preview Custom Tools is a variant of Gemini 3.1 Pro that improves tool selection behavior by preventing overuse of a general bash tool when more efficient third-party...
Llama 3.1 8B
Llama 3.1 8B is Meta's lightweight open-weight model from the Llama 3.1 generation, optimized for efficient deployment on consumer hardware and edge devices. Despite its compact 8-billion-parameter size, it delivers strong performance on instruction following, text summarization, and lightweight coding tasks. Lllama 3.1 8B is the most downloaded model in the Llama family and runs efficiently on laptops, single GPUs, and CPU via quantization — making it the default choice for on-device AI applications and local prototyping.
Technical Specifications
| Specification | Gemini 3.1 Pro Preview Custom Tools | Llama 3.1 8B |
|---|---|---|
| Provider | Meta | |
| Context Window | 1,048,756 tokens | 131,072 tokens |
| Agent Suitability | N/A | 74/100 |
| Time to First Token (TTFT) | N/A | 80 ms |
| Deployment Model | managed api | self hostable |
| Production Stability | beta | stable |
| API Available | Yes | Yes |
| Released Date | 2026-02-25 | 2024-07-23 |
API Pricing Comparison
Input Price per Million Tokens
Gemini 3.1 Pro Preview Custom Tools
$2.00
Llama 3.1 8B
$0.04
Output Price per Million Tokens
Gemini 3.1 Pro Preview Custom Tools
$12.00
Llama 3.1 8B
$0.04
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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 3.1 Pro Preview Custom Tools Quirks & Gotchas
No developer gotchas reported.
Llama 3.1 8B Quirks & Gotchas
- ▸Perfect for CPU/edge deployment — runs on Raspberry Pi with quantization
- ▸Limited tool calling vs larger models — best for simple classification and chat