DeepSeek-R1 vs Qwen3
DeepSeek-R1 and Qwen3 side by side: model sizes, the VRAM you need, context window, abilities, licence and which one to pick.
Side by side
| DeepSeek-R1 | Qwen3 | |
|---|---|---|
| Type | Model in the DeepSeek family | Model in the Qwen family |
| Developer | DeepSeek | Alibaba Cloud (Qwen team) |
| Released | Jan 2025 | Apr 2025 |
| Parameters | 671B (+ 1.5B–70B distills) | 0.6B–235B |
| Sizes | 1.5B · 7B · 8B · 14B · 32B · 70B · 671B | 0.6B · 1.7B · 4B · 8B · 14B · 30B · 32B · 235B |
| Smallest VRAM (4-bit) | ~2 GB | ~1.5 GB |
| Context window | 160K tokens | 256K tokens |
| Abilities | Tool callingReasoning mode | Tool callingReasoning mode |
| Licence | MIT | Apache 2.0 |
| Run it | ollama run deepseek-r1:1.5b | ollama run qwen3:0.6b |
Which one should you pick?
- Qwen3 runs on smaller hardware: from ~1.5 GB of GPU memory, against ~2 GB.
- Qwen3 reads longer inputs: a 256K-token context window.
Pick DeepSeek-R1 if…
- your tools or fine-tunes already target it (released Jan 2025, so support is mature)
Best for: Open reasoning model; small sizes are Qwen and Llama distills
Pick Qwen3 if…
- you have less GPU memory (it starts at 1.5 GB)
- you need longer inputs (256K tokens)
- you want the newer model (Apr 2025)
Best for: Text models with a thinking mode; huge install base
VRAM and download size
Ollama's default 4-bit builds with an 8K context. Read GB as system RAM on a CPU-only machine or a Mac.
DeepSeek-R1
| Model | Download | VRAM |
|---|---|---|
| 1.5B | 1.1 GB | ~2 GB |
| 7B | 4.7 GB | ~6 GB |
| 8B | 5.2 GB | ~6.5 GB |
| 14B | 9 GB | ~10.5 GB |
| 32B | 20 GB | ~22.5 GB |
| 70B | 43 GB | ~48 GB |
| 671B | 404 GB | ~445 GB |
Qwen3
| Model | Download | VRAM |
|---|---|---|
| 0.6B | 523 MB | ~1.5 GB |
| 1.7B | 1.4 GB | ~2.5 GB |
| 4B | 2.5 GB | ~3.5 GB |
| 8B | 5.2 GB | ~6.5 GB |
| 14B | 9.3 GB | ~11 GB |
| 30B | 19 GB | ~21.5 GB |
| 32B | 20 GB | ~22.5 GB |
| 235B | 142 GB | ~157 GB |
Frequently asked questions
Is DeepSeek-R1 better than Qwen3?
It depends on the job and your hardware. Qwen3 runs on smaller hardware: from ~1.5 GB of GPU memory, against ~2 GB. Qwen3 reads longer inputs: a 256K-token context window. For quality, test both on your own prompts: benchmark rankings change with every release.
Which needs less VRAM, DeepSeek-R1 or Qwen3?
DeepSeek-R1 starts at ~2 GB (1.1 GB download). Qwen3 starts at ~1.5 GB (523 MB download).
Can I use DeepSeek-R1 and Qwen3 commercially?
DeepSeek-R1: MIT. Qwen3: Apache 2.0. Check the exact licence of the model you deploy; custom licences can limit very large companies or certain uses.
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