MetaLlama-3.1 70BVSMetaLlama-3.1 8B
Our Take
We recommend Llama-3.1 70B for superior reasoning capability, or Llama-3.1 8B for faster local inference on smaller GPU hardware. While both models run locally for zero API costs, Llama-3.1 70B carries a larger parameter memory footprint. Choose Llama-3.1 70B if you have the VRAM to support it, or Llama-3.1 8B for consumer-grade hardware compatibility.
▶WHY?
Benchmark Calculations & Evidence:
Reasoning Accuracy: Both models were evaluated on the GPQA Diamond benchmark. Llama-3.1 70B scored 46.7%, while Llama-3.1 8B scored 30.4% (+16.3% gap).
Coding Performance: Both models were evaluated on the HumanEval benchmark. Llama-3.1 8B scored 72.6%, while Llama-3.1 70B scored 80.5% (+-7.9% gap).
Hardware Footprint: Llama-3.1 70B is a 70B parameter model requiring more VRAM, while Llama-3.1 8B is a 8B parameter model.
Was this recommendation helpful?
Benchmarks & Scores
Coding (human-eval)Winner (+7.9%)
80.5%basic standalone code completion and simple functions
Reasoning (gpqa-diamond)Winner (+16.3%)
46.7%graduate-level science QA
Cost & Context
Cost (per 1M tokens)
Self HostLocal execution (zero API fees)Context Window
131.07k tokensBenchmarks & Scores
Coding (human-eval)
72.6%basic standalone code completion and simple functions
Reasoning (gpqa-diamond)
30.4%graduate-level science QA
Cost & Context
Cost (per 1M tokens)
Self HostLocal execution (zero API fees)Context Window
131.07k tokensFrequently Asked Questions about Llama-3.1 70B vs Llama-3.1 8B
Llama-3.1 70B is better for coding tasks on this benchmark. It scores 80.5% on human-eval (basic standalone code completion and simple functions) compared to Llama-3.1 8B which scores 72.6%.
Related Matchups
Explore similar comparisons for Llama-3.1 70B and Llama-3.1 8B.
Do you want to find a model for your constraints?
Use our interactive model finder to filter LLMs by reasoning capability, coding performance, cost, and context length.