Versus Engine
DeepSeek R1 vs Gemma 3 (27B)
Recorded capabilities and trade-offs for DeepSeek R1 and Gemma 3 (27B), 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
Gemma 3 (27B)
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
Gemma 3 (27B)
Google's 27B open-weights model, tuned to fit machines with 32GB of unified memory.
Editorial notes on record
Recorded strengths
- Fits comfortably in 32GB of unified memory
- Matches or beats the previous generation of 70B models
- Native multimodal capability at this size
Recorded limitations
- Google's licence carries commercial restrictions worth legal review
- Still needs mid-tier hardware at minimum
- Weaker fine-tuning ecosystem than Llama