MetaLlama-3.1 405BVSMistral AIMistral Small 4
Our Take
We recommend Mistral Small 4 for practical local execution on standard developer hardware, or Llama-3.1 405B if you need peak reasoning and have the VRAM (or a cloud API) to host it. While Llama-3.1 405B offers frontier capability, its massive parameter size makes it extremely difficult to host locally. Choose Mistral Small 4 for standard local setups, or Llama-3.1 405B for peak reasoning.
▶WHY?
Benchmark Calculations & Evidence:
Model Size: Llama-3.1 405B is a massive 405B parameter model requiring heavy GPU infrastructure, while Mistral Small 4 is a 119B parameter model.
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Benchmarks & Scores
Coding (human-eval)
89%basic standalone code completion and simple functions
Reasoning (gpqa-diamond)
50.7%graduate-level science QA
Cost & Context
Cost (per 1M tokens)
Self HostLocal execution (zero API fees)Context Window
131.07k tokensBenchmarks & Scores
Coding (live-code-bench)
63.6%scripting single-file apps or clearly defined functions
Reasoning (gpqa-diamond)Winner (+20.5%)
71.2%graduate-level science QA
Cost & Context
Cost (per 1M tokens)
$0.26Input: $0.15 | Output: $0.60Context WindowLarger
262.14k tokensFrequently Asked Questions about Llama-3.1 405B vs Mistral Small 4
For coding tasks, Llama-3.1 405B scores 89% on human-eval (basic standalone code completion and simple functions), while Mistral Small 4 scores 63.6% on live-code-bench (scripting single-file apps or clearly defined functions).
Related Matchups
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