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Llama 3.1 70B 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 Llama 3.1 70B Instruct and Llama 3.1 8B.

Meta

Llama 3.1 70B Instruct

Meta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 70B instruct-tuned version is optimized for high quality dialogue usecases. It has demonstrated strong...

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Meta

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 โ€” making it the default choice for on-device AI applications and local prototyping.

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Technical Specifications

SpecificationLlama 3.1 70B InstructLlama 3.1 8B
ProviderMetaMeta
Context Window131,072 tokens131,072 tokens
Agent SuitabilityN/A74/100
Time to First Token (TTFT)N/A80 ms
Deployment Modelself hostableself hostable
Production Stabilitystablestable
API AvailableYesYes
Released Date2024-07-232024-07-23

API Pricing Comparison

Input Price per Million Tokens

Llama 3.1 70B Instruct

$0.40

Llama 3.1 8B

$0.04

Output Price per Million Tokens

Llama 3.1 70B Instruct

$0.40

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.

Llama 3.1 70B Instruct Quirks & Gotchas

No developer gotchas reported.

Llama 3.1 8B Quirks & Gotchas

  • โ–ธPerfect for CPU/edge deployment โ€” runs on Raspberry Pi with quantization
  • โ–ธLimited tool calling vs larger models โ€” best for simple classification and chat