Gemini 3.1 Pro Preview 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 and Llama 3.1 8B.
Gemini 3.1 Pro Preview
Gemini 3.1 Pro Preview is Google’s frontier reasoning model, delivering enhanced software engineering performance, improved agentic reliability, and more efficient token usage across complex workflows. Building on the multimodal foundation...
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 | Llama 3.1 8B |
|---|---|---|
| Provider | Meta | |
| Context Window | 1,048,576 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 | 2026-02-19 | 2024-07-23 |
API Pricing Comparison
Input Price per Million Tokens
Gemini 3.1 Pro Preview
$2.00
Llama 3.1 8B
$0.04
Output Price per Million Tokens
Gemini 3.1 Pro Preview
$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 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