Open-weight LLM comparison
DeepSeek R1 vs Mixtral 8x22B
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 | DeepSeek R1 | Mixtral 8x22B |
|---|---|---|
| Total parameters | 671 B | 141 B |
| Active parameters (MoE) | 37 B | 39 B |
| Architecture | MoE (671B total, 37B active per token) | MoE (141B total, 39B active per token) |
| Vendor | DeepSeek | Mistral AI |
| License | MIT | Apache 2.0 |
| Released | 2025-01-20 | 2024-04-17 |
| Weights @ FP16 | 1342 GB | 282 GB |
| Weights @ INT8 | 671 GB | 141 GB |
| Weights @ INT4 | 336 GB | 71 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 | DeepSeek R1 | Mixtral 8x22B |
|---|---|---|
| MMLU (5-shot) | 90.8 | 77.8 |
| MATH (AIME 2024, pass@1) | 79.8 | not published |
| GPQA (Diamond) | 71.5 | not published |
| Codeforces (Elo) | 2029.0 | not published |
| HumanEval | not published | 76.2 |
| MATH (maj@4) | not published | 41.8 |
Sources: DeepSeek R1 model card · Mixtral 8x22B 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
DeepSeek R1 vs Mixtral 8x22B: which is bigger?
DeepSeek R1 has more parameters (DeepSeek R1: 671 B; Mixtral 8x22B: 141 B). More parameters usually means higher ceiling on capability and higher memory requirement, though MoE architectures decouple total parameters from per-token compute.
DeepSeek R1 vs Mixtral 8x22B: which is newer?
DeepSeek R1 released 2025-01-20; Mixtral 8x22B released 2024-04-17.
DeepSeek R1 vs Mixtral 8x22B: which needs less memory to serve?
Mixtral 8x22B needs less HBM. Weights-only footprint at FP16: DeepSeek R1 1342 GB; Mixtral 8x22B 282 GB. Half those numbers at INT8, quarter at INT4. Real serving adds 10-30% for KV cache.
DeepSeek R1 vs Mixtral 8x22B: which license is more permissive?
DeepSeek R1: MIT. Mixtral 8x22B: Apache 2.0. 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 · DeepSeek R1 full page · Mixtral 8x22B full page.