Versus Engine
DeepSeek R1 vs Llama 3.3 (70B)
Recorded capabilities and trade-offs for DeepSeek R1 and Llama 3.3 (70B), side by side. Missing information is not treated as a tie.
Capabilities on record
DeepSeek R1
No structured feature record is available.
Capabilities on record
Llama 3.3 (70B)
No structured feature record is available.
DeepSeek R1
A Mixture-of-Experts reasoning model that reset price expectations for frontier-class performance.
Editorial notes on record
Recorded strengths
- Exceptional mathematical and code reasoning
- MoE architecture cuts inference cost against dense equivalents
- Open weights with no dependency on US-hosted inference
Recorded limitations
- 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.
Editorial notes on record
Recorded strengths
- The reference standard for open-weights models
- Competitive with frontier closed models on practical tasks
- Broad fine-tuning and serving ecosystem
Recorded limitations
- 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