Mixtral 8x22B vs Gemma 3 27B
Architecture Comparison
SpecMixtral 8x22BGemma 3 27B
TypeMOEDENSE
Total Parameters141B27B
Active Parameters39B27B
Layers5662
Hidden Dimension6,1443,584
Attention Heads4832
KV Heads816
Context Length65,536131,072
Precision (default)BF16BF16
Total Experts8N/A
Active Experts2N/A
Memory Requirements
PrecisionMixtral 8x22BGemma 3 27B
BF16 Weights282.0 GB54.0 GB
FP8 Weights141.0 GB27.0 GB
INT4 Weights70.5 GB13.5 GB
KV-Cache / Token229376 B507904 B
Activation Estimate2.50 GB1.50 GB
Minimum GPUs Needed (BF16)
H100 SXM5 GPUs1 GPU
L40S7 GPUs2 GPUs
Quality Benchmarks
BenchmarkMixtral 8x22BGemma 3 27B
Overall7376
MMLU77.878.0
HumanEval46.048.0
GSM8K78.485.0
MT-Bench80.082.0
Mixtral 8x22B
MMLU
77.8
HumanEval
46.0
GSM8K
78.4
MT-Bench
80.0
Gemma 3 27B
MMLU
78.0
HumanEval
48.0
GSM8K
85.0
MT-Bench
82.0
Capabilities
FeatureMixtral 8x22BGemma 3 27B
Tool Use✓ Yes✓ Yes
Vision✗ No✓ Yes
Code✓ Yes✓ Yes
Math✓ Yes✓ Yes
Reasoning✗ No✗ No
Multilingual✓ Yes✓ Yes
Structured Output✓ Yes✓ Yes
API Pricing Comparison
Cheapest Output (Mixtral 8x22B)
$1.20/M
Input: $1.20/M
Cheapest Output (Gemma 3 27B)
$0.20/M
Input: $0.10/M
| Provider | Mixtral 8x22B In $/M | Out $/M | Gemma 3 27B In $/M | Out $/M |
|---|---|---|---|---|
| — | — | $0.10 | $0.20 | |
| together | $1.20 | $1.20 | $0.30 | $0.30 |
| mistral | $2.00 | $6.00 | — | — |
Recommendation Summary
- ‣Gemma 3 27B scores higher on overall quality (76 vs 73).
- ‣Gemma 3 27B is cheaper per output token ($0.20/M vs $1.20/M).
- ‣Gemma 3 27B has a smaller memory footprint (54.0 GB vs 282.0 GB BF16), making it easier to deploy on fewer GPUs.
- ‣Gemma 3 27B supports a longer context window (131,072 vs 65,536 tokens).
- ‣Mixtral 8x22B uses MOE architecture while Gemma 3 27B uses DENSE. MoE models activate fewer parameters per token, improving inference efficiency.
- ‣Gemma 3 27B is stronger at code generation (HumanEval: 48.0 vs 46.0).
- ‣Gemma 3 27B is better at math reasoning (GSM8K: 85.0 vs 78.4).