GPT-5.5 Pro 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 GPT-5.5 Pro and Llama 3.1 8B.
GPT-5.5 Pro
GPT-5.5 Pro is OpenAI’s high-capability model optimized for deep reasoning and accuracy on complex, high-stakes workloads. It features a 1M+ token context window (922K input, 128K output) with support for...
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 | GPT-5.5 Pro | Llama 3.1 8B |
|---|---|---|
| Provider | OpenAI | Meta |
| Context Window | 1,050,000 tokens | 131,072 tokens |
| Agent Suitability | 98/100 | 74/100 |
| Time to First Token (TTFT) | 450 ms | 80 ms |
| Deployment Model | managed api | self hostable |
| Production Stability | beta | stable |
| API Available | Yes | Yes |
| Released Date | 2026-04-24 | 2024-07-23 |
API Pricing Comparison
Input Price per Million Tokens
GPT-5.5 Pro
$30.00
Llama 3.1 8B
$0.04
Output Price per Million Tokens
GPT-5.5 Pro
$180.00
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.
GPT-5.5 Pro Quirks & Gotchas
- ▸Best-in-class instruction following for complex agentic chains
- ▸Premium pricing — use GPT-5 for cost-sensitive workloads
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