GPT-3.5 Turbo Instruct 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-3.5 Turbo Instruct and Llama 3.1 8B.
GPT-3.5 Turbo Instruct
This model is a variant of GPT-3.5 Turbo tuned for instructional prompts and omitting chat-related optimizations. Training data: up to Sep 2021.
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-3.5 Turbo Instruct | Llama 3.1 8B |
|---|---|---|
| Provider | OpenAI | Meta |
| Context Window | 4,095 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 | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2023-09-28 | 2024-07-23 |
API Pricing Comparison
Input Price per Million Tokens
GPT-3.5 Turbo Instruct
$1.50
Llama 3.1 8B
$0.04
Output Price per Million Tokens
GPT-3.5 Turbo Instruct
$2.00
Llama 3.1 8B
$0.04
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.
GPT-3.5 Turbo Instruct 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