Versus Engine
DeepSeek R1 vs Llama 3.3 (70B)
Specs, price and the one trade-off that actually decides it — DeepSeek R1 against Llama 3.3 (70B), side by side.
Cheaper to start
Tie
Both start at a similar price.
Best ecosystem
Tie
Neither lists native integrations.
Standout
DeepSeek R1
Exceptional mathematical and code reasoning
DeepSeek R1
A Mixture-of-Experts reasoning model that reset price expectations for frontier-class performance.
Where it wins, where it doesn't
Pros
- Exceptional mathematical and code reasoning
- MoE architecture cuts inference cost against dense equivalents
- Open weights with no dependency on US-hosted inference
Cons
- Enterprise procurement often blocks it on provenance grounds
- Large MoE models are harder to deploy locally than the parameter count suggests
- Memory footprint is the full parameter count despite lower compute
Llama 3.3 (70B)
The open-weights reference point at 70B — the strongest local option if the hardware exists.
Where it wins, where it doesn't
Pros
- The reference standard for open-weights models
- Competitive with frontier closed models on practical tasks
- Unmatched fine-tuning and serving ecosystem
Cons
- Needs roughly 64GB of VRAM or unified memory to run well
- Heavy energy cost under sustained inference
- Aggressive quantisation costs the quality you bought it for