Open-weight LLM comparison
Command R+ vs DeepSeek R1
Side-by-side
Straight from each model's release page and Hugging Face card. Memory shown is for the weights alone: add roughly 15% for real serving. Bold column marks the more parameters and the smaller memory footprint.
| Field | Command R+ | DeepSeek R1 |
|---|---|---|
| Total parameters | 104 B | 671 B |
| Active parameters (MoE) | — | 37 B |
| Architecture | dense | MoE (671B total, 37B active per token) |
| Vendor | Cohere | DeepSeek |
| License | CC-BY-NC-4.0 (non-commercial) | MIT |
| Released | 2024-04-04 | 2025-01-20 |
| Weights @ FP16 | 208 GB | 1342 GB |
| Weights @ INT8 | 104 GB | 671 GB |
| Weights @ INT4 | 52 GB | 336 GB |
Which is smarter (published benchmarks)
Vendor-reported quality scores on the standard leaderboards. Bold column marks the higher score on the same test.
| Benchmark | Command R+ | DeepSeek R1 |
|---|---|---|
| MMLU (5-shot) | 75.7 | 90.8 |
| HumanEval | 70.7 | not published |
| MATH (AIME 2024, pass@1) | not published | 79.8 |
| GPQA (Diamond) | not published | 71.5 |
| Codeforces (Elo) | not published | 2029.0 |
Sources: Command R+ model card · DeepSeek R1 model card. Benchmark methodology and prompt template can shift these numbers by several points, so treat these as relative rankings, not absolute scores.
Common questions
Command R+ vs DeepSeek R1: which is bigger?
DeepSeek R1 has more parameters (Command R+: 104 B; DeepSeek R1: 671 B). More parameters usually means higher ceiling on capability and higher memory requirement, though MoE architectures decouple total parameters from per-token compute.
Command R+ vs DeepSeek R1: which is newer?
DeepSeek R1 released 2025-01-20; Command R+ released 2024-04-04.
Command R+ vs DeepSeek R1: which needs less memory to serve?
Command R+ needs less HBM. Weights-only footprint at FP16: Command R+ 208 GB; DeepSeek R1 1342 GB. Half those numbers at INT8, quarter at INT4. Real serving adds 10-30% for KV cache.
Command R+ vs DeepSeek R1: which license is more permissive?
Command R+: CC-BY-NC-4.0 (non-commercial). DeepSeek R1: MIT. Apache 2.0 and MIT allow unrestricted commercial use; Llama Community License allows commercial use but restricts training larger models on outputs; CC-BY-NC and vendor-specific licenses (Qwen 72B, Gemma) have narrower terms. Check the model card for the exact clauses.
See also: every LLM comparison · Command R+ full page · DeepSeek R1 full page.