DeepSeekDeepSeek V4 FlashVSMetaLlama-3.1 405B
Our Take
We recommend DeepSeek V4 Flash 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 DeepSeek V4 Flash 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 DeepSeek V4 Flash is a 284B parameter model.
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Benchmarks & Scores
Coding (swe-bench-pro)
49.1%complex codebases, multi-file repositories, and architectural planning
Reasoning (gpqa-diamond)Winner (+29.3%)
80%graduate-level science QA
Cost & Context
Cost (per 1M tokens)
$0.17Input: $0.14 | Output: $0.28Context WindowLarger
1.05M tokensBenchmarks & 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 tokensFrequently Asked Questions about DeepSeek V4 Flash vs Llama-3.1 405B
For coding tasks, DeepSeek V4 Flash scores 49.1% on swe-bench-pro (complex codebases, multi-file repositories, and architectural planning), while Llama-3.1 405B scores 89% on human-eval (basic standalone code completion and simple functions).
Related Matchups
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