Versus Engine
DeepSeek R1 vs Gemma 3 (27B)
Specs, price and the one trade-off that actually decides it — DeepSeek R1 against Gemma 3 (27B), 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
Gemma 3 (27B)
Google's 27B open-weights model, tuned to fit machines with 32GB of unified memory.
Where it wins, where it doesn't
Pros
- Fits comfortably in 32GB of unified memory
- Matches or beats the previous generation of 70B models
- Native multimodal capability at this size
Cons
- Google's licence carries commercial restrictions worth legal review
- Still needs mid-tier hardware at minimum
- Weaker fine-tuning ecosystem than Llama