Open-weight LLM comparison
Gemma 2 27B 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 | Gemma 2 27B | Mixtral 8x7B |
|---|---|---|
| Total parameters | 27 B | 46.7 B |
| Active parameters (MoE) | — | 12.9 B |
| Architecture | dense | MoE (46.7B total, 12.9B active per token) |
| Vendor | Mistral AI | |
| License | Gemma Terms of Use | Apache 2.0 |
| Released | 2024-06-27 | 2023-12-11 |
| Weights @ FP16 | 54 GB | 93 GB |
| Weights @ INT8 | 27 GB | 47 GB |
| Weights @ INT4 | 14 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 | Gemma 2 27B | Mixtral 8x7B |
|---|---|---|
| MMLU (5-shot) | 75.2 | 70.6 |
| HumanEval | 51.8 | 40.2 |
| MATH (0-shot) | 42.3 | not published |
| GPQA | 25.3 | not published |
| MATH (maj@4) | not published | 28.4 |
Sources: Gemma 2 27B 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
Gemma 2 27B vs Mixtral 8x7B: which is bigger?
Mixtral 8x7B has more parameters (Gemma 2 27B: 27 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.
Gemma 2 27B vs Mixtral 8x7B: which is newer?
Gemma 2 27B released 2024-06-27; Mixtral 8x7B released 2023-12-11.
Gemma 2 27B vs Mixtral 8x7B: which needs less memory to serve?
Gemma 2 27B needs less HBM. Weights-only footprint at FP16: Gemma 2 27B 54 GB; Mixtral 8x7B 93 GB. Half those numbers at INT8, quarter at INT4. Real serving adds 10-30% for KV cache.
Gemma 2 27B vs Mixtral 8x7B: which license is more permissive?
Gemma 2 27B: Gemma Terms of Use. 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 · Gemma 2 27B full page · Mixtral 8x7B full page.