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DeepSeek V3 0324 vs Llama 3.1 405B

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 DeepSeek V3 0324 and Llama 3.1 405B.

DeepSeek

DeepSeek V3 0324

DeepSeek V3, a 685B-parameter, mixture-of-experts model, is the latest iteration of the flagship chat model family from the DeepSeek team. It succeeds the [DeepSeek V3](/deepseek/deepseek-chat-v3) model and performs really well...

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Meta

Llama 3.1 405B

Llama 3.1 405B is Meta's largest open-weight language model and one of the most capable openly available models in the world. With 405 billion parameters, it achieves performance competitive with GPT-4 and Claude Opus across benchmarks spanning general knowledge, mathematics, coding, and multilingual tasks. Llama 3.1 405B is released under Meta's custom commercial license, supporting broad use cases including deployment via major cloud providers (AWS, GCP, Azure) and self-hosted inference with multi-GPU configurations.

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Technical Specifications

SpecificationDeepSeek V3 0324Llama 3.1 405B
ProviderDeepSeekMeta
Context Window163,840 tokens131,072 tokens
Agent SuitabilityN/A90/100
Time to First Token (TTFT)N/A550 ms
Deployment Modelself hostableself hostable
Production Stabilitystablestable
API AvailableYesYes
Released Date2025-03-242024-07-23

API Pricing Comparison

Input Price per Million Tokens

DeepSeek V3 0324

$0.24

Llama 3.1 405B

$0.80

Output Price per Million Tokens

DeepSeek V3 0324

$0.90

Llama 3.1 405B

$0.80

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.

DeepSeek V3 0324 Quirks & Gotchas

No developer gotchas reported.

Llama 3.1 405B Quirks & Gotchas

  • โ–ธMassive model โ€” requires 8ร— A100 80GB for FP16 inference
  • โ–ธAvailable via Together AI, Fireworks, and Bedrock as managed API