DeepSeek V3 vs Gemma 3
Pricing, benchmarks, and use case comparison
Quick take
- •DeepSeek V3 is meaningfully stronger at coding (80 vs 62 on our capability index).
- •Gemma 3 is meaningfully stronger at multimodal (70 vs 10).
Specs comparison
| DeepSeek V3 | Gemma 3 | |
|---|---|---|
| Provider | DeepSeek | Google DeepMind |
| Type | Open source | Open source |
| Context window | 128K tokens | 128K tokens (32K for the 1B variant) |
| Input / 1M tokens | Free (self-host) | Free (self-host) |
| Output / 1M tokens | Free (self-host) | Free (self-host) |
| Release date | 2024-12 | 2025-03 |
Benchmarks
| Benchmark | DeepSeek V3 | Gemma 3 |
|---|---|---|
| Pre-training scale | ~15T tokens | - |
| MATH (27B) | - | 89% |
| MMMU (27B, multimodal) | - | 64.9% |
Scores sourced from official provider release posts and independent benchmark aggregators.
Which should you choose?
Choose DeepSeek V3 if...
- →You want a proven, stable open model with broad ecosystem support
- →You need to self-host or fine-tune without licensing friction
- →Cost is critical and you don't need V4's 1M context or top scores
- →You want reproducible open-weight behavior pinned to a known version
Choose Gemma 3 if...
- →You need an open, self-hostable model with a size to match your hardware
- →Multilingual or multimodal tasks on-prem
- →Privacy-sensitive or offline deployments
- →Fine-tuning on your own data