Open-weight LLM comparison
DeepSeek V3 vs Mixtral 8x7B
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 V3 | Mixtral 8x7B |
|---|---|---|
| Total parameters | 671 B | 46.7 B |
| Active parameters (MoE) | 37 B | 12.9 B |
| Architecture | MoE (671B total, 37B active per token) | MoE (46.7B total, 12.9B active per token) |
| Vendor | DeepSeek | Mistral AI |
| License | MIT | Apache 2.0 |
| Released | 2024-12-26 | 2023-12-11 |
| Weights @ FP16 | 1342 GB | 93 GB |
| Weights @ INT8 | 671 GB | 47 GB |
| Weights @ INT4 | 336 GB | 23 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 V3 | Mixtral 8x7B |
|---|---|---|
| MMLU (5-shot) | 88.5 | 70.6 |
| HumanEval | 82.6 | 40.2 |
| MATH (0-shot) | 61.6 | not published |
| GPQA | 59.1 | not published |
| MATH (maj@4) | not published | 28.4 |
Sources: DeepSeek V3 model card · Mixtral 8x7B 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 V3 vs Mixtral 8x7B: which is bigger?
DeepSeek V3 has more parameters (DeepSeek V3: 671 B; Mixtral 8x7B: 46.7 B). More parameters usually means higher ceiling on capability and higher memory requirement, though MoE architectures decouple total parameters from per-token compute.
DeepSeek V3 vs Mixtral 8x7B: which is newer?
DeepSeek V3 released 2024-12-26; Mixtral 8x7B released 2023-12-11.
DeepSeek V3 vs Mixtral 8x7B: which needs less memory to serve?
Mixtral 8x7B needs less HBM. Weights-only footprint at FP16: DeepSeek V3 1342 GB; Mixtral 8x7B 93 GB. Half those numbers at INT8, quarter at INT4. Real serving adds 10-30% for KV cache.
DeepSeek V3 vs Mixtral 8x7B: which license is more permissive?
DeepSeek V3: MIT. Mixtral 8x7B: 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 V3 full page · Mixtral 8x7B full page.